Discovering Semantic Connections in Community Data Eleni Yiangou BSc Computer Science 2009/2010

Transcription

1 Discovering Semantic Connections in Community Data Eleni Yiangou BSc Computer Science 2009/2010 The candidate confirms that the work submitted is their own and the appropriate credit has been given where reference has been made to the work of others. I understand that failure to attribute material which is obtained from another source may be considered as plagiarism. (Signature of student)

2 Summary Modern search engines consist of indexes amounting to billions of pages of spidered web content. When a user enters any given keyword phrase into a web browser search box, the search engines return those pages that they CONCEIVE to have a high relevance to the keywords entered by the web user. I say CONCEIVE, as the results returned by the search engines for any given keyword are not necessarily ranked by the value of the content. More often than not, these results are biased by ranking factors other than the content. This is a whole different ranking algorithm, and outside the scope of this report. Despite the fact that all major search engines have made conscious efforts to improve the relevancy of their search results by employing LSI (Latent Semantic Indexing), the relevancy of the content in a particular web-page to the searched keywords is not sufficient by itself to return search results based on content value alone. With this in mind, I set out to develop a web based application that would use semantic algorithms to separate, rank and present search results based on the relevance to the searched keywords and also based on raking factors from outside the system. User feed-back is an example of such an outside ranking factor. Users of the system would have the ability to vote on the value and relevance of the search results the application returned. This vote ability would have an additional benefit, in that it would allow the system to apply semantic algorithms to results returned across all users. Search engines like Google, Yahoo and Bing, have enormous resources at their disposal and are constantly updating their algorithms. Their search results however are still lacking in semantic relevance. What we get when we search for Bears still gives us pages about Polar Bears and The Chicago Bears Football Team for example. This was want I set out to address. My aim was to solve the relevance problem by employing advanced semantic algorithms to create an online application that would return relevant results to users searching a database of academic papers, along with the possible connections (research topic, searched keywords, search results, etc) existing between users of the system. i

3 Acknowledgments First of all I would like to thank God for the constant encouragement to work hard and ultimately complete this project to my satisfaction. Then, I would like to thank my supervisor Dr. Vania Dimitrova for the continued support and advice she offered me. Without her help, I don t know if this project would have ever been completed successfully. I hope God will keep her, and her family healthy! Moreover, I would like to say a big thanks to my best friend Bill for his unlimited patience and willingness to support me. I m also very grateful to all the people who devoted their time and energy to participating in the evaluation process of my project. Special thanks to: KRR Group participants for their presentations, recommendations and support. My proofreader, Andrew Paxton I would like to dedicate this work to my mother! ii

4 Table of Contents Project Summary i Acknowledgments ii Table of Contents iii 1. Project Outline Problem Description Project Aim Methodology and Objectives Minimum Requirements Schedule 5 2. Background Reading and Research Research Aim Community Background Semantic Connections Semantic Similarity Semantic Relatedness Functional Requirements of the LeARN Community Tools and Methods. 14 iii

5 Wordnet Wordnet MySQL Database Measures of Relatedness Sphider Version 1.3 A Search-based Method Ranking Algorithms Design The LeARN Community Data The System Use Case Diagram Application Scenarios Prototype Data Selection Client - Server Architecture General LeARN Community Architecture Algorithms Used Algorithm 1: Related Users Algorithm 2: User-Based Related Keywords Algorithm 3: Recommended Materials Based on Keywords Algorithm 4: Recommended Materials Based on Rankings.. 49 iv

6 5. Implementation of the Application Iteration 1: Feasibility of the LeARN prototype Architecture Iteration 2: Algorithms Implementation Implementation of Extension 1 Include in Services Menu the Upload in the Prototype Evaluation User Prototype Evaluation objectives Procedures, Participants and Materials User Result Analysis Project evaluation Further Work Project Conclusion References Appendices.. 67 Appendix A. Personal Reflection.. 67 Appendix B1. Original Project Schedule 69 Appendix B2. Original Project Schedule 69 Appendix B3. Analysis of Schedule.. 70 Appendix C. Background Reading and Research Plan Appendix D. The description of community data schema.. 73 Appendix E. The core tables of WorNet SQL database 76 v

7 Appendix F. MySQL query (section 2.7.2, p. 19) result. 77 Appendix G. Sphider Sorted Results in LeARN prototype 78 Appendix H. Ranking Algorithm 1 [45].. 79 Appendix I. Prototype Environment Screenshots 80 Appendix J. Algorithm implementation coding parts 90 Appendix K. Client-server architecture, with server side scripting technologies [50]. 96 Appendix L. Evaluation Scenarios and Questionnaires 97 vi

8 1. Project Outline 1.1. Problem Description Web-based communities are becoming increasingly important places for people from diverse backgrounds to seek and share expertise. This is because existing search engines are unable to answer queries that require deep semantic understanding of the query or the document [1]. Additionally, research communities are valuable for researchers undertaking new studies. This is because it is vital to know the communities of researchers with the same research topic or area of interest [2]. As a result, researchers are in need a of an effective web-based Community environment consisting of tools capable of understanding material in depth and discovering the semantic connections existing in the community data. For people (students, researchers and staff) within the University of Leeds who are conducting research with a common aim, but are members of different departments, will find webbased research communities an invaluable tool to pool resources, interact and learn. Learning is a result of interactions within a particular social community data. As such, users can benefit from discovering resources read by other users or other data that both increases their knowledge in a particular research area and helps them to understand a particular topic of interest. The initial idea behind this project came from two existing projects, the AWESOME 1 and the BRAIN 2. The AWESOME project is an online community for dissertation writing, which integrates information about Final Year Projects. This information (title, author, degree program, year and semantic annotation) can serve to recommend relevant projects to Undergraduate students. Moreover, in the AWESOME project, students and tutors have the opportunity to annotate dissertations and share not only examples but also useful guidelines. The BRAIN project is about developing an online environment to help people discover connections between them, which can give them the opportunity to develop collaborative research and innovation networks. Based on how those two projects work, author decided to develop a similar web-based client/server application where the participants are Research students. The aim of this application is to give students the opportunity to find and share material relevant to the research area they are interested in. Furthermore, it will discover the possible connections users may have according to the material they downloaded or ranked and help them identify other materials which may be related to their research

9 1.2. Project Aim The aim of this project is to create a web-based client-server application in which community data will be analyzed in order to identify similarities and connections between community members and the material (papers, reports, books) they are involved with. It will focus on discovering the semantic connections that exist in community data. The community is called LeARN Community (Leeds Academic Research Network). This community aims to identify how users coming from different departments within the University of Leeds can be related / connected according to the material they are most interested in so that they will have the opportunity to develop collaborative research networks. The initial idea was to use Community data from the existing research projects AWESOME and BRAIN. However, author decided to analyze data that aims to be beneficial for students who are doing research. Consequently, the data to be analyzed are materials written by people are working in the University of Leeds. Since most of the staff members within the University are participants of at least one research area, they will have written material for these areas. Therefore, students (users) who are doing research in a particular area will have the opportunity to interact with material written by their teachers and if they have a strong connection with regard to their interests, collaborate with them. Finally, as the LeARN Community aims to be a practical tool for students of the University of Leeds only, the materials, research areas and any other relations which may exist are devised to give users the opportunity to collaborate, leading to a true community research network Methodology and Objectives The project described in this report, involves the design of a prototype. This prototype is a web-based client server application, called LeARN (Leeds Academic Research Network). To develop a prototype, the author first decided which methodology to follow in order to describe the software process. First of all, according to Summerville [13], a software process can be defined as the set of activities that leads to the production of a software product and these activities may involve the development of software from scratch in a standard programming language. Based on the same source, four fundamental activities are common to all software processes. These activities are: System Specification; Software Design and implementation; Software Validation; Software Evolution. The users of this prototype will be students and staff of the University of Leeds, who are currently involved in research. These people are the future end-users of the LeARN application. As identifying end-users for the purposes of the project development was difficult, for the initial software validation the use of opportunistic software development methodologies such as scenarios was required. This 2

10 meant that three scenarios were to be used to receive feedback regards to the functionality of the prototype. Although the traditional waterfall model has been at the centre of software development for a number of years, the LeARN community was designed using the Evolutionary Development model. Keeping in mind that the LeARN application is designed to be web-based and internet architectures and technologies are constantly changing, a more fluid model was required. The Evolutionary Model has a number of benefits which are highly applicable to such a changing web environment: 1. Frequent Updates are simplified 2. Dynamic Architecture 3. Simple to integrate with user feed-back 4. Simple to integrate with a developing prototype The Evolutionary Model is in essence a collection of Waterfall Models. As such, the extension of applications is simplified and working prototypes can be delivered in a more timely fashion. Given the benefits, the Evolutionary Model was the logical choice for the LeARN community application. The LeARN Community application ultimately aims to allow people to browse and search for materials related to their particular research area and identify potential research similarities with other users. The activities that users could perform are: Search for papers related to keywords they enter Search and read abstracts of particular materials Download material Search and view which material has been downloaded by each users Search and view material written by a particular author View and rank particular material Vote (Like or Don t Like) for material (People can Like a material if and only if they believe that the keywords they used are related to the material returned and not if they Like the material itself). Perform advanced search using keywords that are similar (synonyms) to the keywords they already entered into the system (According to WordNet dictionary) The functions of the LeARN community aim to promote and foster new research collaboration amongst people from varied academic sectors. Consequently, the objectives and process structure of this project are: 3

11 Collection of the sample data and identification of the structure Identify what connections could be identified and decide the possible scenarios (what the possible connections between community data and projects will be) Do the appropriate Research. For example, research on identification of possible methods and tools that are either readily available or author created for semantic comparison of the data. Create the web-based Client-Server LeARN Community Implement the community data related to the scenarios identified at the second stage. Conduct evaluations so that the effect of scenarios on the application will show how effective and successful the application is 1.4. Minimum Requirements The minimum requirements have changed since author first submitted the original set because the initial idea was to use the data from the existing AWESOME and BRAIN projects. The format of that data was not compatible with the requirements of this project however and as a result, a review of the minimum requirements became necessary. The new set of minimum requirements is: Create the data scheme for representing the community data Formulate possible scenarios and queries for mining community data Design the prototype of a Client - Server web-based application that provides basic knowledge functionality focusing on reading or downloading materials Discover how users can be connected based on the material they have accessed Possible extensions include: Allow staff - users to upload different types of community data. On the one hand this will extend the interface of the application and on the other, reformulate the application so that it converts the data to the appropriate data format allowing it to be used in the database etc. Integrate appropriate Visualization techniques to show the relevant connections between the community data and between people. Extend the community data mining algorithms so that they will use ontologies. Extend the project by doing deeper evaluation involving real users rather than scenarios. 4

12 1.5. Schedule Appendix B1 includes a Gantt chart that details the original schedule for this project. The revised schedule (in Appendix B2) was deemed inefficient to accurately depict the course of events. This is mainly due to the fact that the initial schedule did not make allowances for completion of implementation of the prototype. The Appendix B3, describes how events of Schedule followed. 5

13 2. Background Reading and Research The Background Reading and Research schedule is illustrated in Appendix C, using Mind Map Research Aim Web-based communities have rapidly become important places for people with different interests and background knowledge but common goals to collaborate, share and develop a new knowledge base. Author s first target is to discover the possible semantic connections between people in order to help them to work constructively in the community. The second target is to find the semantic connections of the materials existing in the community, in order to help users find materials related to the keywords provided. Consequently, in order to help readers understand all the terminologies and notations surrounding semantic connections, community and community data, this part of the report will discuss all the relevant definitions, tools and methods that are used during the design of this project Community Background The term community carries various meanings, types, characteristics and purposes. In general, it is pointed out that community can be defined as a particular type of social system that is distinguished by characteristics [3] such as: 1. Users of a system can easily recognize their relationships and areas of common interest with other users 2. The system is persistent and has continuity 3. The operations are dependent on voluntary cooperation 4. The system is multifunctional 5. The system is not only complex and dynamic, but also large enough to make instrumental relationships predominate 6. There is a geographic element which is associated with its definition as well as basic boundaries However, that is not the only description of the term community. According to Christenson and Robinson, community has many standard definitions that are sufficient in most situations but they vary in terms of the elements included. Consequently, it is difficult to explore all the meanings of communities. Therefore, the idea that best fits in this project s approach adopted and points out that community is the environment that is characterized by membership, influence, integration as well as the fulfilment of needs and emotional connection [4]. Types of Community The term community is not only open to various definitions but also can be of various types such as Geographic Community, Community of Culture and Community Organizations. The type of community adopted in the research described in this report is the Community of one Organization. This is because the participants of the LeARN community will be Researchers, students and staff who are within the 6

14 University of Leeds only. People who are not registered at the University of Leeds will have access to the LeARN Community but they will not have the necessary permissions to download material or use the other functionalities of the system. Moreover, the community is categorized into: International Community: participants of this community are the governments of the entire world or a group of them and all have a specific international relation. Community of Practice (CoP): users are participants of groups who learn to do their work together, even if they are experienced or not and sustain their success on discussion about their work together. It arises as people address recurring set of problems together [5]. Virtual Community (VC): is a community where participants use words on screens to exchange pleasantries or argue, engage in intellectual discourse, conduct commerce, exchange knowledge, share emotional support, make plans, brainstorm, gossip, find friends and lose them, play games, create a little high art and a lot if idle talk. Virtual communities support everything people do in real life, leaving their bodies behind. [6] Community of Interest: a collaborative group of users who exchange information in pursuit of their shared goals, interests, missions, or business processes and who therefore require a shared vocabulary for the information they exchange. [7] Type of LeARN Community: The Community of Practice is based on the key idea of Legitimate Peripheral Participants where users inexperienced in the research area expand their knowledge by working on the periphery of the community and as soon as they prove their competence, are invited to play more important roles by completing much more important tasks. In addition to this key idea, it encourages inexperienced users to rate the publications existing within the community. Since knowledge is inseparable from practice, this benefits them due to the fact that they participate in expert activities and as a result broaden their knowledge on a specific area. In general, in a Community of practice all users are active members. They learn how to participate by learning the traditions, ideas and vocabulary of the group they related to. However, having similar work doesn t necessarily imply this is a community of practice. It may simply be a community of interest. In a community of interest, it is presumed that users have a similar way of working, related ideas to discuss, a way of interaction, shared experiences and common way of thinking about the specific research area [8]. As a result, the most important things that keep members working together within the community are the shared learning and interests. A Community of Interest is more task-based instead of knowledge-based [5] Taking into account the above clarifications, the term that best describes the LeARN Community is the Community of Interest. This is due to the fact that the participants of the LeARN Community want to 7

15 find materials related to their interest in a particular research topic. They can the find the material that other people are interested in. The benefits of this community lie in its ability to identify how users can be related. The interested area/category, the materials being downloaded, uploaded or ranked and generally the ways that each user interacts within the community are the main characteristics that will be identified and related for each individual. As a result, each user can find within this community other users that share common interests in order to collaborate and find useful material for their topics. Consequently, according to the definition of the communities and categories described above as well as the way users in our approach interact, the Community of Interest can best describe LeARN community, despite the fact that it can also characterized in some cases as a Community of Practice. The main overlap with a Community of Practice lies in how it offers similar benefits and impacts people working together exchanging material and keywords. Characteristics of the LeARN Community The main characteristics of the Community in this project are: Actors of Community: The Research Community consists of the following actors: 1. Researchers, 2. Students coming from different departments, 3. Staff who currently work within the University of Leeds. Activities of Actors within the Community: The benefits and activities of the members in this small functioning community are the following: 1. Users from different disciplines and knowledge background will find material by entering some keywords into the system, 2. Users can rank material returned if they believe that it is what they expected when entering the keywords into the system, 3. Users shall share materials. For example, users will download and upload material. 4. Users shall rank materials they like from 1 to 5 in order to leave their feedback and help other users. 5. Users shall identify which other users downloaded a particular piece of material. 6. Users shall find all the publications of a particular author through references to the author name existing in any material. 7. Users shall conduct advanced search by choosing synonyms that the system returns in the event that the user is unable to describe their exact requirements and therefore, the initial keywords didn t return the expected materials. 8

16 Size of Community: Despite the fact that Communities of Interest may vary from fairly large, to relatively small, for the purposes of this paper, the LeARN Community will assumed to be of a small size Semantic Connections Definition 1 (Semantic Connectivity): Two entities e1 and en are semantically connected if there exists a sequence e1, P1, e2, P2, e3, en-1, Pn-1, en in an RDF 3 graph where ei, 1 i n, are entities and Pj, 1 j < n, are properties. [35] Semantic connections in the LeARN Community data can refer to the possible meaningful relations that may exist between not only the content (data which are publications) but also between members but the term can be associated with many descriptions. In the particular Community, semantic connections derived from activities achieved by users. Author assumes that there can be connections which members are unaware of. Firstly, taking into account that users are from diverse departments probably with completely different knowledge background author can discover a connection between them by examining their interests, the material they have worked with and the research area in which they are most active. For example, this can be shown if we design the following scenario: Two people share an interest in the same material, they have searched for similar keywords or they are interested in the same research area even if they are from different disciplines. Therefore, these two people should have a connection as they have similar interests, research area and possibly common topic, which means that those two people have a semantic connection. Secondly, the community data that will be used by researchers consists of materials such as papers, articles and books. Author cannot only define semantic relationships between users and their interests but also between the materials they rank and download. For example, this can be shown if we design the following scenario: Material-1, Material-2 and Material-3 are available in the Community database. User-1 is downloads Material-1, User-2 is downloads Material-1 and Material-2 and a User-3 is downloads Material-2 and Material-3. Consequently, since users choose to download some common materials, it can be assumed not only that they share common areas of interest, but also that the three materials share common 3 Resource Description Framework 9

17 content and could effectively be clustered into the same research area. Therefore, those three materials have a semantic connection. The LeARN Community includes not only people but also learning resources such as publications of the staff of the University of Leeds. With continued interaction between people, resources and community, sharable resources accumulate become better organized and a wider knowledge base is developed. Consequently, semantic relations are discovered. The increased resources, relations and knowledge in turn help in discovering more relations [10]. In this section of the report the input data formalized so that a conventional structure of a community considered by material sharing, aiming to help users improve their knowledge of a particular topic. Therefore, semantic connections such as those described above, will be exploited, enabling the LeARN Community function to be described as follows: The input data will be: Keywords associated with each material Materials - Publications of KRR (Knowledge Representation and Reasoning Group) Author s information The user who shared or accessed specific materials A list of KRR group teams The types of semantic connections between LeARN users will be: The relationship between users according to the material Downloaded The relationship between users according to the material they are related (Like) The connections between keywords prompted to the system by users (Liked / Don t Liked) The connections between materials according to the rank value assigned to each material by users The similarity of the materials downloaded by each user, taking into account its keywords. In case a piece of material does not have associated keywords, the keywords provided by the user system consider in order to return the particular material which is ranked by the user as well related (liked). The aim of this work is to use algorithms, methods and tools that can discover the semantic connections of the users. The common interests that may connect people may be unknown by them, so author aims to discover them in order to give them the opportunity to work together, taking into account the possible semantic connections described above. 10

18 2.4. Semantic Similarity Definition 2 (Semantic Similarity): Two entities e1 and f1 are semantically similar if there exist two semantic paths e1, P1, e2, P2, e3, en-1, Pn-1, en and f1, Q1, f2, Q2, f3,, fn-1, Qn-1, fn semantically connecting e1 with en and f1 with fn, respectively, and that for every pair of properties Pi and Qi, 1 i < n, either of the following conditions holds: Pi = Qi or Pi Qi or Qi Pi. We say that the two paths originating at e1 and f1, respectively, are semantically similar. [35] The aim of this section of the report is to define the term semantic similarity that best fits to this project. Before attempting to describe what semantic similarity is, let consider the following scenarios: The similarity between two words correlates with the common attributes/characteristics one word shares with the other. The more shared and common attributes/characteristics the two words have the closer similarity they have. The similarity of two words correlates with their differences. That means, the more differences one word has with any other word(s), the less possibility there is to be similar. The maximum similarity between two words occurs when they are exactly the same. [22] According to the above scenarios, the semantic similarity of two words could be the measure that defines the similarities and the differences of the features/characteristics between those two words. Significant characteristics could be some fundamental definitions or connotations of a particular word. For example, the term {dog} could have as a connotation {has four feet}. Based on this idea, the term {dog} has much more semantic similarity with the term {cat} than the term {computer}. Usually, the measure of the similarity is displayed as a score (number) that represents how similar or different two words are. Of course, this score does not always correspond to people s way of thinking as soon as a human may find a correlation between two words from his point of view and finally point out that the words are semantically similar in some way. Consequently, programmers and researchers are designing algorithms for discovering semantic similarities of the words using an additional percentage on the score in order to be closer to all the human similarity reasoning point of views and judgments. In the next section, we discuss the semantic similarity as well as semantic relatedness of the words. According to Nuno Alexandre Lopes Seco [14], there are four approaches that can be used in order to compute the semantic similarity [15]., those approaches are: 1. Ontology-based approaches: Uses ontology such as Wordnet in order to find the relations of its nodes and discover the score of similarity. 2. Corpus-based approaches: Uses a body of text (corpus or corpora) in order to identify cooccurrence relations from the statistical relations that exist. This aims to discover if a semantic affinity exists between two words. This can be achieved if two words frequently occurre together. 3. Information Theoretic approaches: Uses a hybrid approach that considers not only corpora but 11

19 also ontologies. It is based on Information Content that stems from the information theory. 4. Dictionary- based approaches: Uses a machine-readable dictionary aiming to identify relations, which may exist between the various concepts. Comparing the various approaches of similarity that are also computational approaches to semantic relatedness, Dictionary-based approaches are the most relevant at the first stage to be used for this type of project. At a further stage, corpus-based approaches may be used as an extension of this project or as future work. This section of the report discusses the algorithm that calculates the semantic similarity between two words. As shown in section 2.5.1, a word can have more than one sense that can lead to ambiguity. For example, the term {community} has 6 senses. Therefore, the algorithm [16] that is used in order to discover the semantic similarity works as follows: Initial Query: Distance Learning Distance has 6 senses and Learning has 2 senses in WordNet 3.0 To get optimized query: 1. Calculate similarity score of each pair of concepts as 6*2 = 12 similarity score is returned 2. Choose the sense with highest value of similarity score 3. Replace the original query with synonyms of a sense that has the highest similarity score. The complete algorithms that are used for discovering semantic similarities in LeARN community data can be found in the Implementation chapter. 2.5 Semantic Relatedness The previous section discussed the semantic similarity between two words. This section, discuss how two words could be semantically related based on their meaning (similarity of meaning). This is due to the fact that it is essential to distinguish between similarity and relatedness, as the two terms are often used incorrectly. According to Philip Rensik, semantic similarity represents a special case of semantic relatedness. For example, the terms {car} and {gasoline} have a closer relation than the terms {car} and {bicycle}, but it is true to say that the latter pair are more semantically similar. This is because, {car} and {bicycle} share features such as {have wheels}, {transport people or objects}, and many more [10]. Pertaining to the above example, it is obvious that two terms may be coupled (i.e cars require gasoline to function) but they may not have any common features / characteristics and consequently in accordance with the definition described in the previous section, are not semantically similar [11]. By associating semantic similarity and semantic relatedness, author can conclude that two words could be semantically similar and semantically related when and only when they share common features, common characteristics, and many more shared senses. This can be best explained by recalling the above 12

20 example with the {car} and {gasoline} as well as by another example; The term {motherboard}, is partof the term {computer}. Taking into account these examples, is becomes crucial to focus on the different relations of meronyms (Part-of), antonyms,etc). On the other hand, it is important to check the relations of hyponyms and hypernyms of the two words. For example, the terms {car} and {bicycle} have common features which arises from the term{vehicle} and consequently both {car} and {bicycle} are part of the term {vehicle} ( is-a-kind-of). The above descriptions and examples, aim to give an overview of how two terms can be related. In this project approach, one example of semantic relatedness could be the following; User-1 provide as keywords the term {taxonomy} and he is member of the Visualization research area. User-2 provides as keyword the terms {Biological classification} and he is a student so doesn t belongs to any research area. User-3 enters as keyword the terms {Linnaean taxonomy}. Despite the fact that all three user s keywords seem to be semantically related and especially User-1 to User-3, User-2 and User-3 are more semantically similar and related as the keywords they have provide have exactly the same meaning, sense and features. In addition to the above, a different way of measuring the relatedness of two words is to measure their semantic distance [12]. For example, using a dictionary like WordNet 4, it is possible to measure, the distances that two significations have, and therefore discover how related they are. This can achieved by considering that, the closer two significations (words) are, the more related they are. Some measures used to calculate this distance are Leacock-Chodorow, Rensik, Wu-Palmer, and many more [20]. The Wordnet dictionary is semantically-similar-based rather than linguistic-similar-based thus by measuring the distance of the two words then can discover how related or unrelated they are. As an example, the measure of semantic distance [12] is described in the following scenario: We assume that we have the following pairs of keywords with the similarity scores shown. The keywords entered into the system by the user are: sim(k1,k2) = 0,1 ; sim(k1,k3) = 0,2; sim(k1,k4)= 0,3. Based on the above measures, it is obvious that there exists a constant growth of 0,1 between each similarity assessment. At this stage, if we assume to measure it in different way, we have: dist (k1,k2)=10; dist(k1,k3)=0,2; sim(k1,k4)=3,33. Now, we get a difference of 5 between the first pair and a difference of 1,7 between the second Therefore, it is obvious that when calculating correlation values we get erroneous results. The implementation of LeARN prototype, uses sim(k1,k2) rather than dist(k1,k2). More details about how this works are discussed in chapter

21 2.6. Functional Requirements of the LeARN Community According to Summerville [13], functional requirements should describe what the system should do in detail. This aims to discuss the inputs, outputs and exceptions of the software that is created. This section of the report provides the functional requirements of LeARN Community prototype. The input data of the community was discussed in section 2.3 and considered in this section. In addition to this, the phase of this community is functioning. As a result, this part of the research starts by providing all the elements that relate to the functioning of this knowledge sharing community. This community consists of a list of users U, a set of Materials M, and a set of material ratings R. Consequently, the LeARN Community environment E, will be defined as E:{U,M,R}. Since the environment E is changing all the time, it means that the user performs various actions on it. Those actions are: Sign_Up : a user shall be able to register to the community Sign_In : a user shall be able to log in to the community Sign_Out: a user shall be able to exit the community Download_Material : a material could be downloaded from the community by a user Upload_Material: a new material could be uploaded by a user Rate_Material: a user shall be able to assess how related to the keywords prompted to the system a particular material is Rank_Like: a user shall rate a material to assess how interesting he found a particular material The description of the actions performed in the environment of the community, are going to be stored in the database (section 3.1, figure 4). Appendix D analyzes all the information about the entities of the database Tools and Methods In this section of the report, author discuss the part of our research that focuses on the identification of various tools and methods to be used to discover semantic connections in the Community data based on the semantic similarity of the data. The aim of this research is to find the most compatible tools and methods and apply them to our prototype so that it will achieve its goal. In order to achieve this goal, the functionality of the LeARN Community Environment considered as discussed in previous sections and how these actions can practically formulated. The tools we have chosen to use are the following: Wordnet: [22] is a semantic lexicon for the English language. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. It was developed at Princeton University. 14

22 Wordnet MySQL Database: is a ready-to-use WordNert database, designed by Bernard Bou, and is available from Princenton University. It is available in MySQL, PostgressSQL, Sqlite, HsglDB and Derby. [36] Sphider Version 1.3: is an open source web spider and search engine. It includes an automated crawler, which can follow links found on a site, and an indexer that builds an index of all the search terms found in the pages. It is written in PHP and uses MySQL as its back end database. [37] Wordnet Similarity: implements measures of similarity and relatedness that are all in some way based on the structure and content of WordNet. [28]. It is a package developed by Ted Pederson et al. This package has a Wordnet::Similarity API which receives two words and applies computer measures in order to calculate the semantic similarity value of the particular words. It is based on the WordNet English lexicographic dictionary. [38] Wordnet: WordNet is a semantic electronic dictionary which is based on the meaning / definition of the words as well as on the semantic relationships a particular word may have with any other word. All the words are semantically ordered instead of alphabetically ordered. Thanks to the semantic connections, the meaning of the words of a particular language can be correlated, designing a Network of Definitions and that is exactly the network illustrated in WordNet. Its design derives not only from the psychological but also the from the linguistic theories based on how lexical information is structured and stored in the memories of English people or other people who speak and have a well versed background in the English language. WordNet was first developed in Princeton under the direction of Professor George A. Miller (Principal Investigator) for the English language. Due to its huge success, programmers began the design of WordNets for other languages such as EuroWordNet5 (Dutch, Italian, Spanish, English, French, German, Czech and Estonian), Balkanet6 (the Balkan languages Bulgarian, Czech, Greek, Rumanian, Serbian, Turkish), etc. WordNet is designed to use four syntactic categories. Thus, it saves especially words that are in one of the four parts the speech (POS): Nouns, Verbs, Adjectives and Adverbs. WordNet 3.0 database consists of nouns grouped in set of synonyms, verbs grouped in sets of synonyms, adjectives grouped in set of synonyms and finally adverbs grouped in sets of synonyms. In total it consists of records and sets of synonyms. According to WordNet 3.0 database statistics [15], WordNet 3.0 database consists of noun, verb, adjective, and adverb strings. All exist only once. Despite the fact that many strings exist only once within a syntactic category, strings may belong to more than one syntactic category. The main monad in such a network is a definition that is represented from a particular word, a particular explicative definition (gloss) and all the possible synonyms that can represent the particular word

23 (synonym sets - synset). The phrase all the possible synonyms can be explained as the set of words all located in the same community / environment and each can be replaced by any other word without changing the initial meaning. For example: The set {ambulance, hospital} is a set of synonym words, which can be defined as follows: Ambulance is a vehicle, equipped for carrying sick and wounded persons to and from hospitals. The set {car, auto, automobile, machine, motorcar} is a set of synonym words of the English WordNet which can be defined as follows: Wheeled motor vehicle, usually propelled by internal combustion engine Moreover, since all the words that are synonyms are grouped in order to create sets of synonyms, each synonym set (synset) represent a concept. For example, the word community has three meanings as shown below: Figure 5: The noun community has 6 senses (WordNet screenshot) Each word corresponds to a particular set of synonyms that describes a meaning of the particular word. In case the word has various definitions/meanings, then it is displayed in many places of the hierarchy (WordNet organizes the definitions corresponds to each particular word in increasing order, according to the frequency the word is used). The connection of the words is achieved by discovering the relations exist between them. According to WordNet, relations can be distributed in two categories: Semantic Relations: are relations defined between the set of synonyms (synset) that can be related. For example, hyponym, hypernym, etc Verbal Relations: are elations defined between the set of words such as: Antonym in order to describe two words that have opposite meaning, Derived from in order to describe two words that the one is the derivative of the other. To sum up, the connections supported by WordNet are shown in the figure below: 16

24 Figure 6: The WorldNet Relational Pointers The WordNet is described in detail in this section due to the fact that it is the foundation of our next workload. WordNet is going to be used in order to discover the semantic connections that may exist in the LeARN Community data using the MySQL Package that is discussed in section Wordnet MySQL Database As already discussed in previous sections, WordNet is a semantic electronic dictionary. It is a completely free, powerful tool that has the capability to interlink synonym sets known as synsets, by means of conceptual and lexical relations. It is available from Princeton University through a lot of projects and packages (APIs) [22]. For the LeARN Community prototype, WordNet MySQL has been chosen as the database software to be used. This will be used to implement a variety of semantic similarity and relatedness measures based on information that is available in the English lexical database of WordNet. The core of WordNet 3.0 database scheme that is used is illustrated in Appendix E. In the WordNet SQL builder [39], which is designed by Bernard Bou 7, data are already queried via the MySQL database. However, according to the functional requirements of the LeARN prototype design, author has only used some of those queries that are used for the advance search in the prototype. The queries have been implemented according to the requirements of this prototype, are the following: 7 bbou@ac-toulouse.fr 17

25 Notions and terminology that are used in the queries description according to [17] are proposed here in order to describe what each terminology means: Lemma: is the string that represents the word. Synset: A synset can be roughly viewed as an entity of semantic information. It is a set of words that can be substituted for each other in some sentences (but not necessarily all possible sentences) without changing its truth valuation. A word can also be present in more than one synset, because a word can have several meanings. Sense: A sense in this context is the association between the syntactic entity (a word) and the semantic information it carries (a synset). This part of the report discusses how author implement each of the queries: 1. The first SQL query ($query_03), consists of 4 SELECT statements. a) SELECT (morph) 18

26 FROM morphmaps INNER JOIN words USING (wordid) INNER JOIN morphs USING (morphid) WHERE lemma = $word This query uses the SELECT DISTINCT in order to select only the different (distinct) morphs that exist in the morphmaps table, which is in the database. Using the INNER JOIN, one query retrieves, the wordid and morphid from the tables words and morphs respectively. Using WHERE, only the lemma that is the same with the input word ($word) is selected. The WHERE clause is the condition of the SELECT query. The above SQL Select QUERY returns all the possible morphs that a lemma may have. b) SELECT DISTINCT (lemma) FROM morphmaps INNER JOIN words USING (wordid) INNER JOIN morphs USING (morphid) WHERE morph = '$word' This query uses the SELECT DISTINCT in order to select only the different (distinct) lemmas that exist in the morphmaps table, which is in the database. The procedure that is followed in order to retrieve wordid and morphid of INNER JOIN is the same as in 1. Using WHERE, the morph that is the same with the input word ($word) is selected. The above SQL Select QUERY returns the lemma that is the same with the input word. c) SELECT synsetid, lemma, SUBSTRING (definition FROM 1 FOR 60) FROM wordsxsensesxsynsets WHERE synsetid IN ( SELECT synsetid FROM wordsxsensesxsynsets WHERE lemma = '$word') AND lemma <> '$word' This query uses the SELECT query in order to select the synsetid, the lemma and the definition of a paricular word. The SUBSTRING function is used in order to return only a part of a character string. Here, it returns only the definition that matches with the particular synsetid and lemma. Using WHERE statement, the synsetid is selected that is in SELECT query d. d) SELECT synsetid FROM wordsxsensesxsynsets 19

27 WHERE lemma = '$word' This query uses the SELECT query in order to select the synsetid from the database table wordsxsensesxsynsets that completes the WHERE condition which requires the lemma to be the same with the input word('$word'). Therefore, this SQL Select QUERY returns the synsetid of the lemma that is the same with the input word('$word'). Thus, $query_03 returns the morph of the input word ($word). Using the one SELECT query into the other, aims to retrieve the morph of a word only once avoiding repetition. 2. The second SQL query ($query_06), consists of 3 SELECT queries, all working as described above. The only difference here is that it retrieves definition and synsetid from the Views of the Database rather than a table of the database. The components View of the database consists of Virtual tables which are components of wordnet 3.0 database. The power of those virtual tables lies in their ability to execute a particular query and retrieve the desired information in a single query rather than run the whole code and queries. Here, the View table that is used is the wordsxsensesxsynsets from where it retrieves synsetid, lemma and the definition. An example which shows the results by running the 2nd and 3rd SELECT query, with input word model, can be found in Appendix F. Therefore, the complete query ($query_06) returns the lemma of the input word ($word) that is not exist in the results in the table above, only once. 3. The third and final query ($querysynth) that is used returns the lemma from the wordsxsensesxsynsets View table. For example, if this query runs for the input word model the results returned are: modelling, simulation, theoretical account, framework, example, good example, exemplar, manikin, mannequin Measures of Relatedness In this section of the report, the author decided to discuss 8 algorithms for measuring semantic similarity or relatedness. Those measurements were originally based on WordNet English lexical database of concepts and relations. 1. Resnik: This measurement [23], in based on the information content (IC) that the Least Common Subsumer (LCS) (most informative subsumer) has. The resulting value is always greater-than or equal-to zero. It notices that the upper bound of the resulting value is too large and varies according to the size of the content that is used so that the IC values are determined. According to [34], the upper bound of the result of this measure is ln(n) where N is the number of words 20

28 existing within the corpus. The formulas that should be applied in order to get the appropriate similarity value are: freq(w)= then, p(c)=, followed by p(w)= where words(w) is the total number of words that exists in the term w, and N is the total number of words that exists in the corpus. Thus, the information content (IC) of a word can be found by: IC(w)=- log(p(w)) Due to the fact that a word may have multiple senses, the total number of similarity value can be found by computing the maximum information content value as follows: simres(w1,w2)= IC(w) S(w1,w2) is the total number of senses exists within w1 and w2. However, according to Nuno Seco the probability of existence of words depends on the number of hyponyms that exists within the Wordnet. This is because, the Wordnet is semantically structured rather than lexicographically structured. Therefore, the final measurement that can be used in order to use the Resnik measure in to compute the similarity of w1 and w2 can be found as follows: ICwn(w)=1- Where hypo(w) is the total number of hyponyms within the word, w. 2. Lin: Lin is a measure based on the information of content that two words have [30]. It discovers the semantic similarity of two words by computing the amount of information that is contained in the commonality between the two words, divided by the amount of information in the descriptions of the objects. [22] The formula of the Lin measure is: sim lin (w1,w2) = sim 3. Jiang-Conrath: Jiang and Conrath suggested a measure that is based on the information of the content by calculating the semantic similarity/distance between words and concepts and then the semantic similarity of the commonality of the two words. [24] The formula they used for calculating the distance is: dist jcn (w1,w2)=ic(w1)+ic(w2)-2. IC(LCS(w1,w2)) and they continued by calculating the semantic similarity that exists between the two words by the formula: 21

29 sim jcn = 4. Leacock-Chodorow (LCH) : is a measure that is based on the hierarchies is-kind-of that exists in the WordNet English lexicographic database aiming to calculate the shortest path between two synonym sets (synsets). The noun hierarchies that exist in the WordNet are assumed to have a shared entity. That means, the two entities have at least one shared path. Aiming to determine how semantically similar the two synsets are, this measurement uses the following formula: LCH(w1,w2)=-log( ) In the above formula, the shortest_path(w1,w2) denotes the shortest path length between w1 and w2 and LCH executes the Leacock & Chodorow measure The D is the maximum depth of WordNet noun hierarchies. When this measure is applied, the weights of the hierarchy links are assumed to be equal. According to [31,32], this assumption is incorrect. This is because, the synsets that are at the top of the hierarchy, are more semantically similar than those are close to the leaf of the hierarchy. However, this measure is very simple and that is its main advantage. 5. Hirst-St.Onge: is a measure, which is based on the WordNet Eglish Dictionary. The dictionary is used in order to discover how semantically related two words are. The semantic relatedness that is measured here focuses on the similarity of two words rather than their synsets. Hirst and St.Onge propose that measure, in order to calculate the relatedness values that correspond to all the possible concepts (i.e words) rather than only hybernyms and hyponyms. All the links of WordNet, are clustered in three categories. Those are: the Upward: for representing the Part-Of relations, the Downwards: for representing is-kind-of relations and finally the Horizontal: which represents the opposite word meaning. In addition to those categories, three levels of links relatedness exist.[26] Those are: extra-strong, strong and medium-strong. For strong relations, the weight is assigned to be 2-C, but the weight of any mediumstrong path can be computed by: Weight = C-Path_Length-k*Changes_in_Direction In the above formula, C and k are constants. Therefore, the longest path between the two words is, and the most exchanges on path s directions performed, the smallest weight of the particular semantic relatedness is [33]. 6. Wu-Palmer: suggests a measure of semantic similarity, which is based on the distance and depth of ontology. [27] The formula that is applied is: sim wup (w1,w2)= 22

30 From the above formula, which is discussed in [27], this measure takes into account the distance between each of the two synsets with the support of the Least Common Subsume, LCS as well as the distance of LCS from the root of the hierarchy. In the above formula, w1 and w2 are the two synsets that are considered how similar they are. Moreover, lcs is the least common subsumer (LCS) and root is the hierarchy root. 7. Banerjee-Pedersen: That is an extended overlap measure [29] where relatedness between two input keyword synsets (k1,k2) computed and the glosses of those synsets that are related to k1 and k2 through explicit relations that are provided in WordNet compared. It starts by setting a non-empty list (RELS) which consists of the set of relations that have either one or more relations. That is set as follows: RELS { r r is a relation defined in WordNet} The above formula, assumes that each relation exists (RELS) has a function which has the same name where receives as input a synset and returns the gloss of one or more synsets that are related to the input synset by the designated relation. Then, it continues by appending in the RELS list set of pairs of relations retrieved from the relations of the above equation. That is pairs where: pair (r1,r2) is chosen, (r1,r2 RELS) and thus, the pair (r1,r2) should also be chosen in order to have a reflexive relatedness measure. The reflexive relatedness measure is defined as relatetedness(k1,k2) = relatedness(k2,k1). Therefore, Banerjee and Pedersen in [29] define the RELPAIRS set as follows: RELPAIRS = {(R1,R2) R1,R2 rels; IF (R1,R2) RELPAIRS, then (R1,R2) RELPAIRS} They finally use a score() function which accepts as an input two glosses, then discovers all the phrases that overlap between them and finally returns a score. Hence, the relatedness score for k1, k2 found by [29]: relatetedness(k1,k2) = score( R1(k1),r2(k2)) (r1,r2) RELPAIRS 8. Patwardhan-Pedersen: discussed in [28] the measure that computes the depth of the nodes rather than the edges. It has been recently introduced from Patwardhan and Pedersen and is based on the Gloss Vector measure. The vector measures incorporate information from WordNet glosses. Thus a co occurrence matrix for each word that used in the WordNet glosses from a given corpus created, and then each gloss/concept with a vector that is the average of these co occurrence vectors represented [28]. 23

31 All of the above measurements of semantic similarity and relatedness, are supported by supported by WordNet::Similarity API (section 4.3) [40] and the table below includes advantages and disadvantages of each measure: Figure 1.7.5: Classification of measures according to [40] All of the above algorithms could be used as similarity measures. Using, WordNet::Similarity API, the measurement that best fits to this prpoject is this proposed by Resnik and is based on information content (IC). The IC of each concept is calculated according to the frequency of occurrence of that in a large corpus of text. In the prototype implementation of this project, text is only one short paragraph for the abstract and the title of each material Sphider Version 1.3 A Search-based Method LeARN Community is developed as a search-based model that treats the discovering of semantic connections in community data problem, as a search for semantically related material. The baseline coding part of the whole project is written in PHP. Therefore, a PHP search engine was decided to be the search tool. That is, the Sphider [37] Version 1.3.5, which is an open source web spider and search engine, written in PHP which uses MySQL as its back end database. Sphider provides the ability to add search functionality in the LeARN Community, including a crawler that is able to follow links discovered on a particular site. In addition to this, according to the developer of the tool, Ando Saabas8 [41] Sphider also includes an indexer that develops an index of all the search terms identified within the pages. Furthermore, it supports various advanced features such as word auto completion, spelling suggestions 8 ando.saabas(a t)gmail.com 24

32 and many more. Sphider [37] was chosen as the LeARN community search tool due to the flexibility of its search functions, which include: The ability to support multiple words in the search procedure Supports phrase search using characters The ability to support excluding words (such as web based) The ability to perform word steaming (for example for scheme also finds outline, schema and many more) Is a really simple template system Given the user s desired keywords, the Sphider Version PHP search engine, constructs search queries in cooperation with Wordnet 3.0 MySQL [36] package, aiming to discover all the possible connections which may exist in the LeARN community data. One of the major advantages of the Sphider crawler is the fact that it returns all the results sorted. It measures how many times (frequency) the keywords prompted by the user occur in each material and also shows the percentage of this frequency. It first returns the material which has the maximum frequency of the keywords, second returns the material in which the keywords exists less times etc, as shown in Appendix G. The title of each returning publication is associated with a number that represents the frequency percentage of the keywords entered in the particular material Ranking Algorithms..Ranking of documents is a critical component for today s search engines. [42]. This part of the report deals with the importance of the document ranking procedure in the LeARN Community and details the ranking algorithm is used. The reason for using a Ranking algorithm is to give users the opportunity to provide a satisfaction rating for materials of their interest. Therefore, the quality of the matching results will be improved and more information about connections between the users will be available. Consequently, semantic connections between users will be more accurate. In the LeARN Community, the use of a Ranking Algorithm is important and is applied for various reasons such as: 1. User s opinions could affect other user s decisions with regards to which other material to download. This is achieved by recommending materials to a user, based on what other users with similar interests (shared interests) users have ranked. 2. Helps to discover which users are interested in the same materials aiming to create a connection between them. (i.e: People rank the same materials with high score, could be related) 25

33 Figure 2.7.5a: Collaborative filtering [45] According to Offer Drori [43], there are several ranking methods which sort results of a search engine according to the number of appearances of the search terms, or the use of common keywords and many more. In the LeARN Community approach, the Shpider crawler, is used as the search engine and Wordnet 3.0 MySQL package, is used as the database and therefore, the ranking algorithm will not affect the results of a search because this workload will be completed after deep semantic understanding by the above tools. The central idea of the ranking algorithm is to improve the uncovering of small subgroups of users that best reflect the user choices. To begin with, materials are clustered and located in the database according to the subarea the KRR Group (category) belongs to. Therefore, the algorithm should discover and recommend materials to the user according to what other users have ranked. For example, if User-1 ranks Material-3 and Material-4 as a 5, then User-2 who also ranks Material-3 as a 5, may also find Material-4 to be interesting. This could be achieved using explicit or implicit methods of collaborative filtering. However, in the LeARN prototype, the category that a material belongs to is considered and materials are filtered taking this into account. Only materials that belong to the same category would be recommended. The main idea behind collaborative filtering in the LeARN application is to discover each material that is downloaded and ranked with score grater than or equal to 4 (by users) aiming to match it with other, similar materials. Each material that matches the input material will be stored in a recommended material list that will be returned to the user. According to [45,47] this procedure could be distributed in three phases: 1. Represent the Data 2. Define the neighborhood 3. Make the appropriate recommendations 26

34 Ranking Algorithm 1: Consider users opinions about materials in order to help other users make decisions about which other material to download. Assuming that all user only rate materials they find interesting, the algorithm in Appendix H, selects the number of distinct rankings of a material as a measure. The metric that is created by values represents the independent opinions and is used in estimating the worth of a material [44]. Therefore, the analysis that focuses on materials with the most ratings is completed and the particular Ranking algorithm finishes. Ranking Algorithm 2: We consider rankings to the user based on the materials that have been downloaded together Aiming to match the most semantically similar set of a given publication, the algorithm builds a related publications list. This can be achieved by finding materials that users tend to rank and consequently download together. We use the algorithm shown in figure 2.7.4b in order to calculate the similarity between a particular material and all semantically related materials. For each material in material list, M1 For each user U who rank M1 For each material M2 rank by user U Record that a user rank M1 and M2 For each material M2 Compute the similarity between M1 and M2 Figure 2.7.4b: Ranking Algorithm 2 [46] Given a similar publications list, the algorithm finds material similar to each of the user s downloads and ratings, aggregates these publications, and then recommends the most popular or correlated publications. This computation is very quick [46], depending only on the number of publications the user downloaded. 27

35 3. Design Focusing on the research topics discussed in previous section, the intuition behind this part of the report is to provide us with the main parts of the design process of the LeARN Community prototype, which would enhance user s experiences. Firstly, this section identifies the data sources are in the community such as publications, authors and research groups are in the Knowledge Representation and Reasoning (KRR) area. Secondly, it devises some scenarios explaining the basis for building the prototype. The scenarios will also be used in the different phases of application aiming to refer to the various phases of the LeARN Community application The LeARN Community Data The aim of the community data section is to give a brief overview about what data is available in the LeARN Community, how it can be categorized and how data relates in the database. To begin with, the initial idea was to use data from AWESOME or BRAIN existing projects. When examining the suitability of the data in these existing projects to the LeARN Community, it was decided that the data would not really be helpful for students of the University of Leeds. This is because the information did not match material members of the community may search for when doing research. Therefore, instead of using materials that can be found in the traditional search engines like Google, Google Scholar, Yahoo and many more, data will be comprised of material written by Staff of the University of Leeds only. The main concept behind this idea was to include distinguishable data rather than material which is already available in traditional search engines and make the application more relevant to students. As a result, the database behind the LeARN Community application will include applicable ions; papers and articles whose authors are staff within the University of Leeds regardless of the fact that they may or not be experienced members of the community. The community environment consists of Users, Materials and Rankings of Materials. Users are registered in a department of the University and interact in the LeARN community using some keywords and wait for the material to be returned. According to semantic connections discussed in section 2.3, material may also be related to the users downloads or ranks. Therefore, material is related to the user requests and the research group it belongs to. The available research groups of KRR area are Qualitative Spatial Reasoning, Foundational Ontology, User Modelling and User-Adaptive systems and finally, Program Analysis. Authors could categorize each material or type (publication, book, paper, etc) depending on what users prefer. Moreover, each user may want to rank a material, thus another relationship between material and user s rankings may exist. Consequently, when the database is designed, all the above relations are taken into account. To get a view of the fundamental structure of the LEARN community database, it is necessary to identify the required information for each of the entities it uses. For example, Users, User Requests, Material, Author, Assigned_Material, Research Group, Material Categories, Material_by_author, and finally user Roles are 28

36 some entities/tables included within the database created with respect to the LEARN community. This is the general idea of components used for the database creation [48,49]. For a more detailed explanation please refer to Appendix section 3. The following figure represents the database: Figure 4: Tables and relations in the learn database of the LeARN community 3.2. The System Use Case Diagram This part of the report describes what users can do with the system (Figure 3.4) as described in previous sections and in scenarios, using use cases as follows: 29

37 Figure 3.4: System Use Cases 3.3. Application Scenarios Once the data and the data scheme representing the relations of the community data are identified, this section of the report devises the requirements of the application needs. It was decided that one of the most appropriate methods to describe the possible actions of users within the community is the use of scenarios. According to John Wiley & Sons and LeARN environment approach, a scenario describes the various human activities which are performed within the community in a story that explores and discuss 30

38 the contexts, the needs and the requirements of the application. Based on this idea, from John Wiley & Sons point of view, a scenario does not necessarily describe how a software can be used but, considering the vocabulary or phrases of users, a scenario can be completely understood by the stakeholders (scenarios can best demonstrate the users view of interactions within the LeARN Community Environment). Scenario 1: John is a new member who is student in the University of Leeds and is interested in finding papers related to Semantic Web. John has no background of what was happening previously in the community and therefore he is unsure as to whether there are any relevant materials on the research area he is interested in. When he visits the LeARN Community environment, the system prompts for username and password. John is not registered and as a result he selects the Membership option from the options menu (i.e. Home, About, Services, Membership and Contact). The system displays a form indicating with red stars all the required fields. John completes his details there and submits the form. The system verifies the details submitted and displays a message confirming a successful submission of registration. Then, he verifies his registration using the confirmation received in his box. He returns to the homepage and enters his username and password into the system. The system verifies John s password and displays the menu of choices (i.e. Home, About, Services, Membership, and Contact). John chooses the Services option and the system displays the search menu screen. He chooses to search for publications and enters his keywords in the keywords-field Modeling Analysis, then chooses the Go button. The system displays a list of publications relevant to the keywords but John finds that none of these are what he was looking for. As such, he decides to select the advanced search button which appears at the bottom of the search box. The system displays immediately a search menu screen where John enters the desired keywords and selects Go. At that point, the system displays words onscreen that are related (i.e. synonyms, morphs, etc) to the keywords entered. Then, John selects the 3-4 words that he believes best match his search requirements and selects the Advance Search button. The system then displays a list of publications and he chooses the required publication so that it displays the Title, Author and Abstract of the particular publication. Finally, John selects the publication to be downloaded and as soon the download is complete, we may choose to exit the LeARN Community System. Scenario 2: George is a researcher in the University of Leeds and he is an existing member of the LeARN Community. He is interested in finding papers related on Ontologies. When he visits the LeARN Community environment, the system prompts for username and password. George enters his username and password into the system. The system verifies his password and displays a menu of choices (i.e. Home, About, Services, Membership, and Contact). George chooses the Services option and system displays the search menu screen. He chooses to search for publications and enters in the keywords-field Ontologies, then chooses the Go button. The system displays the search results and he chooses the 31

39 required publication. The system displays the Title, Author and Abstract of the particular publication. George read the details and decides that the paper is related to the research he is doing. Thus, he chooses to download it. Waiting for paper to be downloaded he chooses Related_Users link from the other available options (Related Users, Author Details, KRR Groups). The system then displays a list of the Community Users who have already downloaded the particular publication. George chooses the first user at random (Nicole) and notices that the system prompts all the profile details provided by Nicole and all the materials she read are listed there. Therefore, George looks for more materials that are related to his research in Nicole s reading list aiming to discover related papers he was unaware of and may be beneficial to him. He decides that nothing is related to the particular area of his interest at the moment and he returns to the previous page and chooses the Author_Details link from the other available options. The system displays the details available for the author as well as a list of all the papers he wrote. George finds an interesting paper from the list of writings of that author and selects the download option. At that time, the system prompts that both downloads are complete and then George exits the LeARN Community system. Scenario 3: Dr. Peter is a Lecturer at the University of Leeds and an existing member of LeARN Community. He is interested in uploading a paper. When he visits the LeARN Community environment, the system prompts for username and password. Dr. Peter enters his username and password into the system incorrectly. The system attempts to verify his password, identifies that the details given are invalid and displays an error message. Dr. Peter correctly re-enters his username and password and attempts to sign-in again. The system verifies his log-on details and displays a menu of choices (i.e. Home, About, Services, Membership, and Contact). He selects the Services option and then from the listed choices he selects to upload a material. The system displays a form indicating with red stars all the required fields. Dr. Peter provides the details of the material (Title, Author, Abstract, Keywords, Date of Publication, and Category) he will upload and then selects the Browse button in order to browse the material he wants to upload from his computer. At the next stage he chooses submit and the system displays a message confirming the success of the upload. Dr. Peter then exits the LeARN Community environment Prototype The prototype is a web-based client server application that uses the Wordnet 3.09 dictionary and its MySQL ready-to-use database 10 in order to discover the semantic similarities and relatedness that exists in the community data. Moreover, it discovers the semantic relatedness of the individuals who are registered in the community taking into account how semantically similar are the material the users are interested in and how related their actions are. This prototype aims to give users the opportunity to search for a particular publication and download it, rank it, find other publications by a particular author,

40 discover other users who are interested in the same material or research area and finally find all the available semantically related materials. User will achieve all these actions by providing some keywords to the system. The system analyzes the keywords used by each user aiming to record all the information about the actions for each member and material. The LeARN Community application does not require experienced users. This is due to the fact that apart from a Basic Search function, it also supports Advance Search where it receives the keywords prompted by the user and does a deep semantic analysis not only of the keywords but also of the materials that are available in the database so that it returns the most related materials. The semantic search capability of the prototype is a corporation of Sphiderplus11 Crawler and Wordnet 3.0 MySQL package. In addition to this, the Wordnet::Similarity Java API is also used as an external prototype in order to compute the semantic similarity of two words. All the connections between users and between materials in this prototype are designed using MySQL queries. This is because MySQL is a well known tool for the author and that is also the main reason it was decided to use MySQL Wordnet::Similarity package. Further information about these tools can be found in section 2.7. The following figure shows the main functionality steps of the Advance Search function, are inspired by [16] so that it is clear how the resulting publications are returned to the user: Figure 3.3: Advance Search Architecture of LeARN prototype

41 3.5. Data Selection Data Selection addresses the task of finding the desired material in the LeARN Community Environment. Without useful data, the system will not be interesting to the members of the community. Since the data is to be retrieved from the Knowledge Representation and Reasoning Group as discussed in section 3.1, it was decided to look at the data format, the citation of a particular material and what information about authors is available. First of all, all the data will be retrieved from the following sources: Knowledge Representation and Reasoning Group (KRR) Research Subareas 1 Qualitative Spatial Reasoning 2 Foundational Ontology 3 User modeling and user-adaptive systems Source Program Analysis Figure 6: The table shows where the Community data are retrieved In the sources shown in table 6, there is a large amount of publications, papers, articles, presentation slides, notes and many more related to each research subarea. Despite the fact that Geotechnical & Geoenvironmental Software Directory12 (2010) statistics shows that most the people tend to use word files (.doc) rather than.pdf files, at the first stage, it was decided to only use data that is in.pdf format. This is due to the fact that the vast majority of the materials available are in.pdf format. A problem arose during prototype creation related to the size of each.pdf file. This is important in the uploading stage of a file as scenario three describes, as soon as the.pdf files available in the sources shown above are too big. Therefore, not only the data format but also the size of the materials are important and are taken into. Furthermore, because the data used in the LeARN Community Environment at this stage will be used in the evaluation stage through the scenarios; each subarea will have 5 papers. This is because we have a limitation of the sizes of the data files and the data formats. In total, 20 papers will be available in the community

42 In addition to this, as soon as a second scenario presents the relationship of a particular publication and its author as well as all the other related to that author materials, it is crucial to focus on the information which can be found for each author and in each material. Usually, material citations in our sources consists of Title, Category, Year of Publication, Abstract, sometimes Keywords and finally first name and last name of each author. Therefore, with respect to the authors, the known information are their Name and the materials they have written only, unless that author is an existing member of the community, at which time more information could be found in his/her profile. In addition to the above information, it is already known in which research area the material belongs to as soon as we take into account from which research subgroup source it was retrieved. Moreover, Keywords are important components of each material. This is because the tools and algorithms that discover the semantic similarities and relatedness (section 2.7) of each word, initially use only the title and keywords of each material. Therefore, in the initial stage of the application design, data that has associated keywords is preferable to other materials that do not have keywords. In later stages, in the event material does not have associated keywords a consideration would be to take into account its abstract using corpus semantic correlations using the appropriate tools Client - Server Architecture Ian Summerville [13, page 249], points out that The client- server architectural model is a system model where the system is organized as a set of services and associated servers and clients that access and use the services. Based on the same expert, the basic components of the particular model are: 1. Servers, which are used in order to offer services to other sub-systems, 2. Clients, which are used in order to call on the services offered by the various servers, 3. A network, which gives all the clients the permissions to access these services. The advent of the Internet and the growth of commerce on the World Wide Web have evolved into the revolution of client-server architecture. According to F. Soesianto and Ismail K. Ibrahim, a web-based system is a variation on the well known three tier architecture which is designed in order to support the full advantages of cooperative processing as well as distributed computing. This is achieved using either the Internet or the intercompany WAN (Wide Area Network) as a network (F. Soesianto and Ismail K. Ibrahim, 2000). The three-tier architecture [21] supports the following three independent systems: 1. Client components running on local workstations (tier one) 2. Processes running on remote servers (tier two) 3. A discrete collection of databases, resource managers, and mainframe applications (tier three) The above systems are the three tiers that represent the Presentation layer, the Business logic layer and the Data/Resource layer respectively. Presentation logic layer in Web-based systems such as the LeARN Community consist of the Web browser, which is the application interface. It is usually Internet Explorer, 35

43 Netscape Navigator or Mozilla. The Business logic layer is designed into the Web browser aiming to interpret the HTML pages in corporation with scripting languages such as JavaScript and VBScript. Data/Resource is the layer that is used to get or set the data to, or from the database. This layer interacts only with the database. In the LeARN Community approach, the appropriate database queries are written so that some information is retrieved and other information is set back to the database. Based on the idea is supported by the Web Developers Notes, [50] there are three models of client server architecture: 1. Model 1 of the client server architecture works with static HTML pages 2. Model 2 of the client server architecture works with CGI Scripts 3. Model 3 of the client server architecture works with server side scripting technologies. The LeARN Community application uses model 3 with the server side scripting technologies (Appendix K) because that is the most compatible architecture. It involves dynamic response that is generated using Personal Home Pages (PHP) and Java Server Pages (jsp) technologies. This is due to the fact that PHP is an open source technology that provides easy access to Internet service. Moreover, the coding part of this model only embeds the server-side language inside the HTML page that it is interpreted and generates the final HTML page that is displayed to the client by the browser. Finally, it is crucial to refer to the fact that the embedded server-script code is not visible to the client because the server only submits the HTML code. [50] To describe how the Client Server Architecture is used in the design of the LeARN Community application, the following scenario is considered: A student writes 2 keywords (Query Input module) and requests a publication with the closest relation to those keywords. The request sent from the client through the PHP page passes to the PHP interpreter by the server along with various program variables. The semantic network (WordNet 3.0) does the similarity computations and generates the appropriate concept. After this, the query is semantically expanded and sent to the search engine (Sphider). The search engine discovers the related publications. Then, the interpreter processes the PHP code and generates a dynamic HTML output. This is sent to the server, which redirects it to the client. As the browser is unaware of the functioning of the server, it only receives the HTML code, which it appropriately formats and displays the results on the screen General LeARN Community Architecture Since the essential components for the data of the LeARN Community have been identified in the previous sections of the report, a proposed general architecture can be devised for prototype design, thus the general architecture is discussed in this section. The scenarios discussed in section 3.3 are taken into account and figure 3.7.a, which presents the interactions between Users, WordNet MySQL package and Material Database Server as follows: 36

44 Figure 3.7.a: General Architecture of the LeARN Community search The complete description of this diagram is described at the end of section 3.6 as a scenario. The user initially prompts to the system some keywords using the web browser. The requested keywords are passed to the scripting languages, which are clients (PHP and Javascript). Then, the algorithms that are written as queries in WordNet MySQL package and MySQL server are used so that all the appropriate synonym sets (synsets) of keywords which are in Wordnet English dictionary database are found. Then, all the materials that are related to the keywords are passed from the database to the scripting languages. The browser displays all the appropriate results to the user as HTML pages. In other words, clients perform some actions through the Internet (HTTP and TCP/IP protocols) using a web browser such as Internet Explorer, Mozilla, Safari or any other. The request that user/client enters to the system is passed in the Apache Web Server where the various scripting languages such as PHP and Javascript execute the appropriate scripts. All the information that is available for user are stored in the LeARN database using MySQL as the database management system. Thus, by each user request, the appropriate algorithm is executed and the appropriate information is retrieved form the database to pass it to the user. This is the 3-tier architecture that is used in figure 3.7.b. Figure 3.7.b presents the semantic connections in community data in the three layers of the client server process as follows: 37

45 Figure 3.7.b: The Three Layers Architecture of the community data The main contribution of the illustration of the system in Figure 3.7.b is to show how the various jobs for discovering the connections of not only users but also materials are distributed. To begin with, the Business / Logic layer implements the functionality of the LeARN system. This is achieved by the implementation of the algorithms, tools and methods described in previous sections. Secondly, the Data Layer provides access to the various external systems. For example, in our prototype, the data layer provides access to the database described in Appendix E and the core of WordNet 3.0 database. Therefore, this layer provides information such as user profiles, materials, synsets from the dictionary of WordNet and many more. Finally, the presentation layer provides the system s user interface. Therefore, through the Presentation layer a user enters keywords into the system. The Business layer executes the appropriate algorithms, tools and methods and retrieves from the database the appropriate synsets. After that, Sphider crawler searches the database by the queries described in section for all the publications (P) that are semantically related to the keywords. In the same layer, the appropriate actions are performed and all the related materials are discovered in the Data Layer. If we assume that Mr Orange and Mr Yellow are interested in materials P1 and P10 but Mr Green is interested only in P1 as shown in the figure above, drawing a baseline about what concepts are considered so that we can discover which users and which materials can semantically be connected. These concepts are: 1. How related the keywords prompted by users are? 2. How related the abstracts of the P1 and P10 are? 3. Are the authors of materials the same? 38

46 4. Does user like the materials? 5. Does the user rank the material with a score greater than or equal to 4? 6. Does the user like the material or not? All the above concepts are considered, aiming to filter users based on the similarity score they have. In figure 8, despite the fact that all three users are interested in material P1, only users Mr. Orange and Mr. Yellow connected because those two are more semantically similar. 39

47 4. Algorithms Used 4.1 Algorithm 1 Algorithm 1a: Related Users (based on Downloads) Goal: The main concept behind this algorithm is to discover which users (two or more) have shared interests, i.e they Download same material(s). Usage: A user of the LeARN Community can read the abstract, keywords and title of a material even if they are not registered. However, only members of the community can have access to the functionality of the system. Therefore, each registered user has a user_id and has the opportunity to Download a publication. Two or more users are related if and only if they have Downloaded the same material. Each user who accesses the full details of a material can identify which other users have downloaded the particular publication. Thus, the user can navigate through the names of the Related Users displayed in order to find what other materials they download, rate or rank. This could be helpful not only for newcomers but also for experienced users who want to quickly discover more materials related to their search based on other users preferences. How is applied: Each time a registered user (user_id) requests a material for Download, the material_id and user_id is stored in the user_requests table of the LeARN database. Therefore, it is known who requests which material. When a user downloads a publication (publ), this algorithm is run in order to find other users that also downloaded it. Then, algorithm 1a creates a user_material_list for each user_id and stores all the papers that are downloaded by the user_id. It continues by checking which materials of the user_material_list are the same with the publication that the logged in user downloaded, aiming to create the connection with all the users downloading the particular material (publ). The user_material_list clears each time the user changes. The final list, which is the Rel_Users_List, is returned containing all the users that have downloaded a particular publication. Pseudo code: 40

48 Input: user_id, user_request, material_id, publ //publ is the material_id of the material that algorithm checks to find other people that downloaded it Output: List with all the Related Users (Rel_Users_List_D) based on downloads //User_requests_list = [(material_id, user_id),.] user_material_list = [] For each user_id in user_requests { // record that user_id Downloads material_id add material_id in user_material_list // (user_id,material_id) For each material_id in user_material_list { //check if material_id is the same to publ to create connection If (material_id == publ) and (user_id not in Rel_Users_List) then } user_material_list = [] } return Rel_Users_List add user_id in Rel_Users_List 41

49 The Data Scheme: In order to determine which fields of the LeARN database would be used for this algorithm, which fields would be most useful by analyzing the data contained within them are assessed. The following table rows from the learn database were chosen to be made available: Table 1a: Table fields made available for query Algorithm 1b: Related Users (based on Likes) (Likes determines the Relevance of keywords entered by user with the returned publication) Goal: The main contribution behind this algorithm is to discover which users (two or more) have shared interests, i.e they Like the same material(s). The criteria that should be fulfilled so that a user should Like or Don t Like a material are discussed in Algorithm 2. Briefly, a user does not vote as Like a material according to her/his personal opinion but, according to how the keywords he/she entered to the search are related to the publication returned. Usage: The complete description of Like or Don t Like votes are described in Algorithm 2. However, this algorithm, without taking into account how voting works, explains what the role of voting is, so that two or more users are related. A connection between two or more users can be established not only if they have download the same publication but also if they vote positively (Like) for the same publication. That is because for people who Like a material, keywords they used are related to the publication returned to them by the system. Consequently, it is undoubtedly true to say that those users who Like the same materials are using shared keywords. Therefore, they search for common materials. As a result, a connection between those keywords is created. How is applied: 42

50 The input of this algorithm is a publication and its aim is to find all the users that Like the particular publication. Moreover, any registered user of the LeARN Community can vote a material as Like or Don t Like. Algorithm 1b, creates a user_material_list2 that stores all the materials voted by each user positively which means they Like them (likeval = 1). Then, it checks which of the materials in the user_material_list2 has the same material_id with the material (publ) then the system tries to find which users positively voted for it. The user_material_list2 deletes its elements each time the user_id changes in order to store in the material_id s that are voted for by the next user. The final result of this algorithm is the Rel_Users_List that consists of all the users who Like the publication which was the input of the algorithm. Pseudo code: Input: user_id, material_id, likeval, publ //publ is the material_id of the material that algorithm runs to find other people that Likes it // likeval =1 if material_id voted as Like or 0 if voted as Don t Like Output: List with all the Related Users (Rel_Users_List_L) user_material_list2 = [] for each user_id in log_keywords { if likeval = 1 then // Record that user_id Likes the material_id add material_id in user_material_list2 for each material_id in user_material_list2 { if (material_id == publ ) and (user_id not in Rel_Users_List_L) add user_id in Rel_Users_List_L } user_material_list2= [] } return Rel_Users_List_L The Data Scheme: In order to determine which fields of the LeARN database would be used for this algorithm, an assessment is made of which fields would be most useful by analysing the data contained within them. The following table rows from the LeARN database were chosen to be made available: 43

51 Table 1b: Table fields made available for query 4.2 Algorithm 2: User-Based Related Keywords Goal: The aim of this algorithm is to find how many users gave a positive vote - Like (likeval =1) or negative vote - Don t Like( likeval = 0) for a particular material based on keywords prompted for search by each user. This is employed so that the users discover what keywords are related with each material. Therefore the appropriate keywords are displayed in the LeARN environment, followed by a score (%) representing the keyword s positive feedback. This aims to give users the opportunity to see all the related keywords for a material and use them for further search. Usage: It is important to note that users can vote as many times as they want. The criteria for Like or Don t Like that should be fulfilled are: 1. Positive Vote (i.e: Like) should be assigned for a material if and only if user believe that the keywords he/she prompts into the search box are closely related to the material returned. For example, Natasha prompts in the search box: Vague Adjectives. The system returns the publication with title: A Theory of Vague Adjectives Grounded in Relevant Observables. Therefore, the keywords she prompted into the system are closely related to the resulting publication and hence, she gives a positive vote (Like) to the material. 2. Negative vote ((i.e: Don t Like) should be assigned for materials returned by the system and are not related with the keywords prompted by the user. For example, Martin prompts to the system the keywords Semantic Web and the resulting publications are 1) Integrating Open User Modelling and Learning Content Management for the Semantic Web, 2) Towards automated knowledge-based mapping between individual conceptualizations to empower personalization of Geospatial Semantic Web. For the second paper, he decides that his keyword do not really 44

52 match the resulting material because the material is about Geospatial Semantic Web. Therefore, the material gets a Don t Like vote. In both examples, the algorithm counts the positive and negative votes and displays these to the LeARN environment as well as how many people Like or Don t Like some materials based on the criteria discussed above. A major problem of this algorithm is that noise exists in the results. This is because, if User-1 searches for a set of keywords {k1,k2,k3}rather that one keyword and votes, then the vote is assigned for all 3 keywords. Therefore, the set of keywords is accepted or rejected depending on the likeval. Therefore in case k3 is (not) closely related with the material it is affected by the other two and takes the appropriate likeval. How it is applied: Each time a user finds a material (material_id is retrieved from user_requests table) and wants to vote for it, the keywords (keywords) he prompts to the system are stored in the log_keywords table of the learn database. Then, it calculates how many Like votes exist ( rplus), how many Don t Like votes exist (rminus) and finally how many people have voted (counterall). The algorithm returns the like_persentage, the total number of people who voted and the keywords that correspond to the results. Therefore, the appropriate result of the algorithm is displayed in the screen of the LeARN application with the corresponding keyword. Pseudo code: Input: Keywords prompted to the system by user and likeval Output: keywords with the score liked or disliked and how many people vote for each material_id in material // material table contains all the info of the publications. { for each material_id in log_keywords // if material_id exists in log_keywords table means paper voted (like or don t like) for each keywords in log_keywords // keyword likeval is 1 if liked or 0 if not { counterall = counterall +1 if likeval ==1 then rplus = rplus+1 else rminus = rminus + 1 // how % like the particular paper keyword like_percentage = (rplus / (counterall) *100) } return like_percentage, counterall, keywords } 45

53 Figure ii: User-Based Related Keywords Algorithm 46

54 The Data Scheme: Table 2: Table fields made available for query 4.3 Algorithm 3: Recommended Materials Based on Keywords Goal: The main target of this algorithm is to match a user s interested publications to similar materials so that a recommendation material list (recc_mat_listkw) is created. The algorithm is executed taking into account the keywords of each material. It aims to discover publications with semantically similar keywords and recommends them in the recommendations list to a user. This list will be displayed next to each publication in the Recommended materials Based on Keywords section of the LeARN prototype. Usage: It s crucial to note that this algorithm focuses on: 3. The keywords already existing in the LeARN database for the particular publication (in the table material). 4. The keywords that corresponds to the publication voted as Liked, in table log_keywords and hence have likeval =1. User prompts to the system some keywords and the system returns the appropriate publication results. Then, user selects one publication. Through that publication the user has the opportunity to find which other materials are related to it. Rather than Algorithm 4, which is based on rankings, this algorithm uses the keywords as described above (1,2) and calculates the value that corresponds to the semantic similarity of those keywords with the keywords of the other publications. As shown in the pseudo code the Sim(Kn,keywords) is used. Kn is the keywords existing for the selected publication and keywords are the keywords stored in the database for each other material. However, that is a similarity measure which runs by the Wordnet::Similarity API and gives the semantic similarity of the pair of keywords input. Each pair that has high similarity value ( >=0.5) are set to be related and thus the algorithm sets the likeval = 1. That means, the keywords prompted are related to the particular publication. Therefore, the publications that have likeval = 1 and are not already in the recommendation list are added to it. 47

55 For example: The user selects M1. M1 has a keywords set { k1,k2, kn}. Then, system takes all the materials existing in the database {M2,M3, Mn}, one by one and finds their keywords. It then executes the Sim(Kn,keywords) and gets a score representing the semantic similarity value of the two. The materials whose keywords have semantically similar value >= 0.5, take are appended to the rec_mat_listkw. Selected material: m1 {k1,k2, } Sim(k1, keyword1) = 0.6 Sim(k1, keyword2) =0.5 Sim(k2, keyword1) = 0.8 Sim(k2, keyword2) = 1.0 Material 2: m2 {keyword1,keyword2} Total: sim val = ADD Selected material: m1 {k1,k2, } Sim(k1, keywordx) = 0.2 Sim(k1, keywordy) = 0.2 Sim(k2, keywordx) = 0.1 Sim(k2, keywordy) = 0 Material 3: m3 {keywordx,keywordy} Total: sim val = IGNORE Therefore, assuming that {m1,m2 m3} are the available materials in the database. By the above results, only material 2 (m2) can be recommended based on keywords for material1 (m1). How it is applied: The algorithm firstly takes as an input a set of keywords (KL) that are available in the database for the selected publication (say P). Secondly, it retrieves from the LeARN database the set of keywords for each other materials (keywords) and tries to discover all the materials that have semantically similar keywords to those that the P has. All the keywords of the materials are in material or log_keywords tables. After this, the algorithm executes the Sim( k1,k2) function, using the WordNet::Similarity API (is discussed in this section) which returns the similarity or relatedness score of the keywords existing for P and the keywords corresponding to all the other materials. If the value that is returned is >= 0.5 and the keywords exists in either title or abstract of the compared material then, the algorithm sets the likeval to be 1 and the material is added to the rec_mat_listkw. In any other case, the likeval is set to be 0 and that means the keywords of P (selected by user material) and the compared by the system material keywords are not semantically similar and hence, there is no connection between the two materials. A major problem of this algorithm is that noise exists when WordNet::Similarity API should be executed. This is because it is only available in Java and therefore cannot run at this stage of the project within the PHP code. Therefore, the API runs as an external application for all the possible combinations of words that exist in the WordNet Dictionary. Thus, a new table is created within the LeARN database where all the possible word sets are stored with their associated semantic similarity score. Then, using queries 48

56 each time the algorithm requires the Sim(k1,k2) score the appropriate semantic similarity score is retrieved and therefore the algorithm continues the execution. All the descriptions of how WordNet::Similarity API works can be found in this section and all the WordNet::Similarity measures have been discussed in section The Data Scheme Pseudo code: Table 3: Table fields made available for query Input: Keywords, likeval, material_id, material_abstract, material_tittle Output: rec_mat_listkw // Keywords List (KL) from the publication selected KL = [ K1, K2,.,Kn ] for each Kn in KL { for each m in material //material is a table in learn db { likeval = 0 // keywords : are the keywords of the other materials for each((kn and keywords) in material_keywords) or (Kn in log_keywords) { if (Sim(Kn,keywords) >= 0.5) or (Kn in material_abstract) or (Kn in material_tittle)) then likeval = 1 } if likeval = 1 then add m to rec_mat_listkw } } } return rec_mat_listkw Figure iii: Recommended Materials based on Keywords Algorithm 49

57 The Data Scheme: Table 3: Table fields made available for query WordNet::Similarity API WordNet::Similarity proposed in section 2.7 and discussed in more detail here. It is a free for use object oriented software package, under Gnu Public License. It is available from the Comprehensive Perl Archive Network [19] as well as SourceForge, which is an Open Source development platform [51]. The functionality of this software supports measurement of the semantic similarity and relatedness between a pair of concepts (or synsets) [28]. This API is developed by Ted Pederson et al and implements not only various semantic similarity but also semantic relatedness measures. All the measures that this API supports are based on the information discovered in the lexical database of WordNet (section 2.7.2). Measures that are supported by WordNet::Similarity API are those which described by: Resnik (1995), Lin(1998), Jiang-Conrath (1997), Leacock-Chodorow (1998), Hirst-St.Onge (1998), Wu- Palmer (1993), Banerjee-Pedersen(2002), Patwardhan-Pedersen (recently] [19]. Algorithm 3, requires WordNet::Similarity API which was available in Perl and Java programming languages. However, the Perl library is being deprecated in favour of the Pure Java Version [38] and as a result is now supported only by Java. The problem that arises at this stage is the fact that the whole LeARN Environment is designed using PHP Programming language and WordNet Java API cannot be embedded in the PHP code at this stage of the project. It is assumed that it will be combined in future work. Therefore, it was decided to execute API as an external part of the application and use its results in Algorithm 3 as explained above. 4.4 Algorithm 4: Recommended Materials Based on Rankings Goal: This algorithm is executed aiming to match each user s downloaded and/or ranked materials to similar materials in order to store those materials in a Recommendation List (recc_list_rank) along with all 50

58 the similar materials. Taking into account the Ranking scores which will be assigned for each material by users, this table is created. The algorithm, aggregates materials that have ranked with similar score, eliminates materials that users have already download or voted, and recommends the remaining publications in a recommendation list to the user. This list will be displayed next to each publication, after users ranking. [446] Usage: It is crucial to note that a user could rank materials with a score 1 to 5 only once, based on their personal opinion and interests. It is assumed that 1 is the less related material and 5 is the most related one. Rankings greater than or equal to 4 are assumed to be very related. For example: Nicole gave a 5 score for the book Java and UML and a 4.5 score for the book Twilight. Then, Kristina ranks with a score of 5 the book Programming in Java. The recommendation list for Kristina will contain only the book Java and UML. This is because her interested area / category includes only this material because the other book is in a different category. By the above example, it is true to conclude that people can rank any material. However, aiming to avoid inclusion of unrelated materials in the recommendation list, the category (table learn_categories in the database) each material belongs to, is taken into account. How is applied: This algorithm requires a number of steps, but is ultimately relatively straightforward. It is an extension of the algorithm proposed in Section Each time a user ranks a material, the material_id and user_id are stored in a table. Therefore the interested publications of each individual are known. Each time a new material is ranked by the particular user, the algorithm tries to discover which are the other available materials that best match with the user s preferences. This is achieved by calculating the similarity score of the existent interested materials of the user and the new material. This is the function Sim(M1,M2) shown in the following pseudo code and first proposed in an example of section 2.5. The way that this function returns the similarity score is discussed in the following paragraph (Cosine Similarity Function). Furthermore, the algorithm checks if the result returned by the Sim(M1,M2) function is greater than or equal than 4, which means the two materials are ranked with a rank value more than 4 then, algorithm is executed by checking if the material is not in the recommendation list, it append it to it. The score 4 is decided to be used in order to separate the well ranked with the non well ranked materials and create lists with the materials best ranked from the user. 51

59 Pseudo code: Input: user_id, material_id, ranking_val, category_id ( from db tables: user, material, rankings, learn_categories) Output: recc_list_rank score =0 ; rec_list = []; for each material_id in rankings, M1 { for each user U who rank M1 { for each material M2 rank by user U record that user rank M1 and M2 } for each material M2 { // check that materials belongs to the same category if category_id(m1) == category_id(m2) then { score(m1,m2) = Sim(M1,M2) // the function Sim(M1,M2) is discussed in this section if score(m1,m2) >= 4 then { if M1 not in recc_list_rank then add M1 to recc_list_rank if M2 not in recc_list_rank then add M2 to recc_list_rank } } } return recc_list_rank } Figure iiii: Recommended materials based on Ranking Algorithm 52

60 The Data Scheme: Table 4: Table fields made available for query Cosine Similarity Function: Algorithm 4, use the cosine similarity function based on the cosine similarity rule. The use of this function is inspired by the [46] and the tutorial by Dr. E. Garcia 13. This function is based on the cosine similarity rule that is a common vector space. Then, it determines the similarity using the Euclidean cosine rule. The cosine similarity formula is the following: Figure 4.4 : Cosine Similarity of vecors A and B 13 According to [46], the algorithms that generate recommendations and requires similarity of two objects (sim(a,b)) measurements varies. At the same source it is pointed out that the most common method is to measure the cosine of the angle between the two vectors. The final result that will be returned by the above formula will be a number that represents how similar two vectors (A,B) are. In Algorithm 4, this function is used and the target is to calculate the similarity of two materials based on the rankings given for them. The data used for each material are their keywords (Sim (K1,K2)). Each vector corresponds to a keyword and the vector s M dimensions correspond to users that have ranked that material. The final result that it will be returned by the above formula will be a number that represents how similar two keywords (k1,k2) are. Therefore, it takes the keywords of two materials that have been ranked by the same user and belong to the same category and then computes how similar those keywords are aiming, to use this score. The score is checked and if it is greater than or equal to 4 then the two materials are similar and thus are added in the user recommendation list based on rankings (recc_list_rank)

61 5. Implementation of the Application The System implementation chapter discuss the steps followed in order to successfully implement the LeARN Community environment prototype. This chapter is divided in the four algorithms functionality as discussed in chapter 4. The aim of the implementation phase is to implement what is discussed in the design chapter, including the functionality of the user activities and system architecture diagrams used in this report. In addition to this, this part of the report presents and discusses the queries and coding used in the algorithms applied. It starts by showing some screenshots of the prototype aiming to illustrate exactly how and where certain components are visible to the end users and how members of the community interact with them Iteration 1: Feasibility of the LeARN prototype Architecture The initial set of functionalities as stated in the Minimum requirements (section 1.4) of this project, provides basic knowledge functionality focusing on reading or downloading materials from the LeARN Community. In addition to the minimum requirements, section 2.2 discusses the user activities and furthermore section 2.6 discusses more precisely the user actions as discovered during the study and analysis of the LeARN Community functional requirements. All the above, are set to be the objectives to be achieved within the development of the prototype. However, the objectives of iteration 1 are: Develop a web-based client server application: LeARN Community Environment 1. Sign_Up, Sign_In, Sign_Out 2. Search for a material using some keywords 3. Return to the user all the related to the keywords prompted, materials 4. Read the abstract, keywords and all the available information of a material 5. Download a material System Specification and Environment: The interface of the LeARN application was designed using PHP, CSS, Javascript and MySQL. The LeARN database (section 3.1), has been designed as soon as the application interface was completed and functional requirements analyzed, using the Navicat Premium Standard, tool. The whole application is set up to work on the local machine of the author, and the web container that was selected to be used is Apache, mainly because the author had worked with Apache Tomcat before, in the third year module Distributed Systems. The objectives 3,4,5,6 were achieved using tools and methods described in previous sections. For example, the Search, at this stage of the project is performed using the Sphider crawler and objectives 4,5,6 achieved using MySQL queries. The figures in Appendix I (1) illustrates how a user of the LeARN environment performs the actions proposed in this. 54

62 How to Sign_Up: Users of the LeARN application have access to the functionality of the Community only if they are registered. Therefore, people can sign up following the instructions of Membership that is available in the menu or by selecting the Sign up which also directs the user to the Membership page. People who correctly complete the form shown in Appendix I (2), should then receive a confirmation at the address they provided. Therefore, by confirming their registration by the link sent to their inbox, they can then sign in to the Community and perform the actions they are interested in. How to interact with the environment: People that are logged into the community can Sign out when they want to. When they are logged in, they have the opportunity to see the complete material list of the publications of the community, which are retrieved (section 3.5) from the KRR Group database. Moreover, users can enter keywords and search for a material. This actions illustrated in Appendix I(3). How to Search and retrieve the desired results: Author assumes that Eleni Yiangou is logged into the LeARN system and interested in finding publications related to models. Therefore, she enters this keyword and searches following the steps shown in Appendix I (4). The system returns the available materials. The materials are displayed in the screen according to how many times the keyword exists within each material. Then, a percentage of keyword frequency is presented for each material. At this stage, this helps users to identify which is the most related material to the keywords prompt. How to read information for a material and how to download it: The user s actions continue by selecting the second material so that she/he reads all the information that is available for the particular publications as shown in Appendix I (5). User finally decides to download the material Iteration 2: Algorithms Implementation The second set of functionalities relates to Algorithms 1, 2, 3 and 4, discussed in section 4 of this report. The author has therefore set the following objectives of iteration 2, as follows: 1. Expand the interface so that Advance Search works correctly for the user, 2. Expand the interface so that user can rate a material from 1 to 5, 3. Expand the interface so that user can vote as Like or Don t Like a material 4. Find connections between users based on materials downloaded and Liked (Algorithm 1) 5. Discover all the keywords prompted by users and are related to a publication (Algorithm 2) 6. Discover all the materials that have semantically related keywords and recommend them to a user 55

63 (Algorithm 3) 7. Recommend materials that have been rated with high scores and belongs to the same category to the users Advance Search and Interface Expansion: The way that Advance Search works is illustrated in section 3.3, figure 3.3. However, in the prototype this can be used by any user who cannot find the materials he/she would like by the keywords he/she enters and therefore, the system returns some semantically related words in order to choose similar words and search again. Those actions are illustrated in Appendix I (6). The design of the Advance Search, required WordNet 3.0 database to retrieve from it the synonym sets requested, the Sphider crawler to assembly locally a collection of the publications and finally the queries to implement the search for the data needed. The queries that are implemented for the advance search are illustrated and discussed in section The complete code can be found in the file advance.php, which is in the CD included with this project. Objectives 2 and 3 are performed together. The interface was extended and the results are displayed to the user as shown in Appendix I (7). How those additional functionalities perform, have been discussed in chapter 4 and the coding parts of their implementation will be proposed in Appendix J. Algorithm 1: Algorithm 1 is distributed in two sub algorithms in order to be implemented more practically. Both are discussed in section 4 of this project report. Here, the author discusses how algorithm 1 is implemented so that the system successfully discovers related users. A user could be related with any other user if Downloads (algorithm 1 a) or Like s the same material(s). To achieve this, the interface is extended as shown in Appendix I (7). The actions that are performed by each user, are stored in the learn database (section 3.1). To begin with, each time a user requests a material, the material_id and user_id are recorded in the database. Therefore, the system knows which materials each user requests for Download. Moreover, each time a user Likes a material, the likeval (log_keywords table) corresponds to the particular material in the database, becomes 1 and thus the system can easily discover people who vote positively for the particular materials. All users that are interested in download or vote positively for a material are assumed to be related. Thus the system creates a Related Users part in the interface where it shows the users corresponding with the criteria discussed in section 4, algorithms 1 a and 1 b. The queries implemented to discover the related users(based on downloads and based on Likes) and display them on the environment interface can be found in Appendix J. Algorithm 2: 56

64 The implementation of this algorithm is designed using queries and php code as shown in Appendix J (Alorithm 2). The interface has been extended and the User-Based related keywords list is shown in the menu of the LeARN application. Each time a user vote a material, Like or Don t like, the appropriate likeval (0 or 1) is stored in the database as shown in Appendix J (Alorithm 2). The likes are calculated and displayed in the user as illustrated in the same appendix, using the calculatelikes function. Algorithm 3: This algorithm is designed in such a way that WordNet::Similarity functionality is used. However, at this stage of the project it was not possible to embed the API that supports WordNet::Similarity due to the fact that it is written in Java. The difficulty was the fact that PHP and this API were difficult to author to be implemented in a single software. Therefore, WordNet::Similarity Java API is used as an extra tool, outside of the LeARN Community and its results are used in Algorithm 3, in each iteration that Sim(k1,k2) called. Dimoklis Despotakis, a PhD student of the KRR group designed this API that is used to retrieve the appropriate results. This API, calculates the Semantic Similarity/Relatedness of two words. Therefore, author created a new table in the learn database, where all the possible keyword sets that can be created are stored. This table is the demoapi and its entities are {demoapi_id, kw1, kw2, sim_score}. Thus, within the PHP code, author uses MySQL queries and retrieves the similarity score of two keywords each time the requested keyword set matches with one set of words that is available within the demoapi table of the database. The appropriate score is then passed to the algorithm so that the procedure is completed. The figure at the left of this section of the report illustrates how the WordNet::Similarity Java API works. One of the major problems of this API is the noise which is caused in the data results of the community is that API is not powerful enough to calculate the similarity score of more than one words. For example, if more than one keyword is prompted in Word1 ( semantic connections ) field and 2 words ( semantic relatedness ) are entered in the Word2 field, then the API will only find the semantic similarity score of the set of the 2 words entered initially (semantic, semantic) to the system. The final results of this algorithm are the information that is shown in the LeARN environment menu, Recommended Materials based on Keywords, as shown in Appendix I (7) and the coding parts are illustrated in Appendix J(Algorithm 3). Algorithm 4: 57

65 This algorithm matches each user s download or rated material to similar materials and then combines those similar materials to the user recommendation materials based on keywords. The extension of the interface of this algorithm and the way that ranking score is stored in the database are shown in figure Appendix J (7). All the functionality of this part of the community implementation can be found in Appendix I (Algorithm 4). 5.3 Implementation of Extension 1 Include in Services Menu the Upload in the Prototype The first extension objective of the LeARN Community prototype was to extent the interface and gives users the opportunity to upload a material. This is finally implemented in the Services option, which is available in the top Menu. The Upload was implemented according to PHP MySQL Tutorial 14. Firstly, author creates a new table, upload, in the learn database as shown above: The file that is uploaded could be of any type and is stored in the type, but the content of the file is stored in the content entity. This entity, uses the BLOB (Binary Large OBjects) data types which in LeARN web application is a collection of binary data that stored in the content entity of the upload table of the learn database. Using PHP to upload files in MySQL, is a procedure that is distributed in two steps: uploading the file in the MySQL Server and then read the file and add it into the MySQL. The major advantage of BLOB is that it stores up to 64 kilobytes of data but the use of different BLOB data type such as MEDIUMBLOB, could give the chance to store up to 16 megabytes. However, for the LeARN prototype, upload size is set up in such a way that it can stores up to 8 megabytes of any file format. How it works: User of LeARN application selects the Services tab from the menu. Then, browse the file from his/her computer and completes the form displayed in the screen. The field of abstract has been implemented by the use of ckeditor 15, which is a text editor that can be used inside web pages. In addition to the implementation parts, the tables that display the authors that has written materials that are available in the community are designed by the use of DHTMLX UI Toolkit 16 Standard edition, which is free. Using this tool, users can drag and drop the authors wrote the material they want to upload, in the empty table that follows rather than writing them again. New authors could also added. Moreover, those tables include a search, thus users can write the initial letters of the author s name and then select him/her. The noise caused in this part of the application is that authors that added cannot be deleted. Therefore, users should be very careful

66 6. Evaluation For the purposes of project evaluation, the Author tested the prototype with a set of virtual users according to the scenarios discussed in section 3.2. This helped to identify any problem areas and also served to propose potential improvements to the model. The evaluation phase then moved to a set of real users who followed the various scenarios and presented their findings in a series of questionnaires. Scenarios were employed in the evaluation process to enable users new to the system to navigate the functionality of the system without any learning curve. 6.1 User Prototype Evaluation objectives: The main goals of the evaluation process were: Goal 1: To examine how appropriate the scenarios were, with regards to the actual application and measure the level of user satisfaction. Goal 2: Goal 3: To identify any problems users may experience with the application To examine if users retrieved the information they were expecting through the use of the search and advanced search functions. The aim was to find if the appropriate publication, recommendation lists, user connections and semantic keywords returned by the search functions fulfilled user expectations. Goal 4: To examine the feasibility of the interface architecture. This aimed to examine whether or not users were able to find the recommendation and information sources easily and effectively. Goal 5: Goal 6: To discover any functionality limitations of the system. To examine the robustness of the system. This aimed to discover possible errors or unexpected results within the system. Goal 7: To identify any issues that a real life application should cope with. This aimed to compare this web-based application to a real application according to user feedback. 6.2 Procedures, Participants and Materials: The evaluation teams were made up of a group of 3 people from the KRR Group and 3 students from the School of Computing, none of which had any prior experience with the application. Members of the evaluation team met with the Author at various locations within the School of Computing were they were asked to follow a particular scenario and fill out a questionnaire with their feedback. During these meetings, the Author provided instruction on how best to follow the particular scenario and conduct the tests. This served to highlight how users new to the system would cope with the LeARN application. The number of participants was limited, as the application was developed on a local server and consequently only one user at a time could test the application on the Author s computer. The evaluation procedure took approximately half an hour for each individual. Due to time limitations, the application couldn t be evaluated by more real users and that is the main reason the Author decided to also employ virtual users (created by the Author and following the scenarios of section 3.2) to test the prototype and 59

67 discover possible drawbacks of the application. The question sets were designed according to the evaluation goals listed above. These consisted of two questionnaires, each aiming to identify different items. The first aimed to examine the effort of users when testing the prototype. This questionnaire can be found in Appendix L (1 and 2). The second questionnaire, aimed to evaluate user opinions of the results returned not only by the prototype but also from the external WordNet::Similarity Java API. This required the participants to grade the semantic similarity of two keyword sets on a scale of 1 to 10. This questionnaire can be found in Appendix L(3). 6.3 User Result Analysis: The aim here was to evaluate the levels of user satisfaction with the end results produced by the application, which is essentially an evaluation of the algorithms discussed in section 4. The evaluation goals are reflected in the questionnaire and the results are analysed below: First Questionnaire: 1. Goal 1: How appropriate do you find the application scenario (with regards to the LeARN architecture)? Most of the participants pointed out that the scenario was very appropriate and helpful and covered many possibilities regarding to the usability of the system. One of the participants suggested that it would be nice to provide a more open search but that was not possible at this stage of the project, due to the limitation of the data existing in the database. Another suggestion was to give users different scenarios so that the various actions between users would be better highlighted. One participant pointed out that scenario was somewhat superficial. 2. Goal 2: Have you experienced any problems with the application? Most of the users identified the same problem. That was confusion surrounding the Likes function. In the interface, there is a Like button that should be selected by users who strongly believe the keywords entered into the system are related to the resulting publications. Despite the fact that there is a clear note about this in the interface, participants confused this function with the idea that Like corresponds to the key idea I like this paper. One of the participants suggested that it would be better if Like was replaced by Related to the Keywords. In addition to this, one participant noticed that as soon as the Like function was used, his/her name was automatically added to the Related Keywords list without explanation. This could be fixed by providing an additional notification to users as to the functionality of the Like option and how this serves to establish possible relations between users. One participant experienced difficulty in the Upload facility in the services option. All the participants pointed out that the upload function worked as expected, but experienced some confusion when prompted to add an author because this could only be done by dragging and dropping author names into an empty 60

68 table. This is a minor problem because if a user does not want to drag and drop an existing author name he/she can search for it in the box that is available or scroll down to find it and add it directly. 3. Goal 3: How appropriate do you find the simulation (data used, components, etc)? Most of the participants found the interface well structured, clear, easy to navigate and user friendly. They also pointed out that the materials used are appropriate for the needs of the application but they suggested adding more materials to the database so that further tests will show more related materials and more accurate user connections. One participant said that he/she didn t have enough time to check all the possible connections he/she had with other users. In future, the Author aims to expand the application and participants may have the opportunity to use it under real conditions, with more materials for as much time as they need. 4. Goal 4: What is your opinion about the feasibility of the architecture? On the one hand, some participants pointed out that the system architecture was very feasible, well organized and noted that such a system would be a very useful tool. In addition the majority of participants noted that relations were clearly displayed by the interface. On the other hand, one participant pointed out that it was not clear what Related Users were and suggested adding a help option after each relation status so that newcomers can select and find out what each relation means and how it works. The Author believes that this suggestion could be very beneficial to the system, enabling users to clearly understand why those relations exist and how this will help in their further selections. 5. Goal 5: Please note any missing functionality with regards to the system: All participants were happy with the system functionality at this first stage. However they suggested adding the ranking value given by each user to the appropriate publication. This is a really good suggestion as real applications have recommendations but all will take into account what is the ranking value for each book or paper based on people who have already read it. One participant noticed that the discussion link existing in the menu, didn t work. This was initially outside the scope of this project but it could be implemented as an extension, time permitting. However, it will be completed in future versions, aiming to provide a forum for semantically related users of the community to share ideas and questions. Lastly, one participant suggested including fields for the publication information for each material. 6. Goal 6: Please write some short notes about the robustness of the web application: All the participants were satisfied and pointed out that system worked well, without any errors. It was also pointed out that the system worked as expected without any problems and both search and advanced search returned appropriate results. One participant suggested that it would be beneficial if the same scenario were repeated with a larger dataset at a future date, to test the scalability of the system. Another participant noticed that keywords returned and selected by Advance Search, if voted as Like were displayed in the Recommended keywords list one by one. That means the system splits possible 61

69 phrases or keyword sets that are word pairs. This is a drawback in the system and is caused because the queries the Author selected to use in order to retrieve similar words from the WordNet dictionary, splits those words in order to return the synonym set of each word rather than the synonym set of the phrase entered. However, keyword pairs like web-based which includes the dash returns correct results. 7. Goal 7: Please identify any issues that a real life application should cope with: One of the participants pointed out that it would be better if Advance Search were implemented in such a way that the resulting words were more accurate with regards to the semantic similarity. This problem exists because WordNet is domain-independent lexical resource therefore, if its set up change to domainspecific measures will be more effective and as a result the resulting words of Advance Search will be more semantically related. In addition participants suggested adding keyboard functionality to the search, rather than solely through the Go button. One participant suggested increasing the security of the web based application to reflect the security levels of a real application. The Author believes this would be a wise step to take, if more materials added and more user profiles created. Additional Comments from virtual users point of view: Three virtual users followed the scenarios proposed in section 3.2 and consequently when real users tested the prototype the possible connections with others were clear. All the feedback and comments from those scenarios are very similar to those proposed by real users, except from the following: It would be a good idea if relations that exist in the LeARN Community could be presented schematically by the use of a Visualization tool. This could be implemented by the use of Touchgraph but due to time limitations, this will be implemented in a future version. If a user uploads material, it is presented directly in the Complete Material List. However, it is not returned if the user immediately does a search. This is because the crawler is set up to perform reindexing once a week. This frequency could be changed. System could send a notification to the other users each time a new material is uploaded. This could be implemented if the discussion board was complete. At this stage of the project, what can be done is to use the link of Discussion as an Announcement board. If two different users use the same keywords but only one or none vote Like then there is no measure of possible relations. This could be improved if the Author added a log table in the database to store all the actions of each user. User profiles are not presented. Only materials read by a user are shown. The prototype could be reformulated so that a user s occupation, interested area, keywords most frequently used and other information would be available to other users. The second version of questionnaire: The results of this questionnaire are presented in one table in Appendix L(4). The keywords (KL) that are 62

70 used are retrieved from material_id = 5 and category_id = 3, aiming to compaire human opinions about semantic similarity score, with the API. All the results retrieved from participants that completed this questionnaire concerned the WordNet::Similarity API that uses its results as an external API of the LeARN Community. General analysis of the results retrieved showed that for most participants, precisely setting a semantic similarity value for a pair of keywords proved to be difficult. However, one of the most serious problems encountered was the fact that users were more able to set an accurate semantic similarity value Sim(k1,k2) if input keywords of more than a single word were used (i.e.: Distance Learning, Semantic Web). The API works perfectly only if it compares two keywords. In other cases it returns the similarity of the first two only and therefore the result is incorrect. Despite this problem, this questionnaire was used in order to compare the similarity value of some keywords from the user perspective versus the API perspective. These results show that users tend to give high similarity value to words that are synonyms or similar and low similarity value to words that are in actual fact semantically similar. This proves the fact that people might be unable to use the correct keywords when they search for a material. Thus tools examining semantic similarity become crucial in a search engine in order to perform a deep semantic understanding of keywords prompted by users and returns the most appropriate results 6.4 Project evaluation: The overall evaluation phase left the evaluation participants satisfied. The scenario was a good introduction to the functionality of the LeARN environment for newcomers. Using the scenario, participants were able to take a quick tour of the application. However, one of the participants expresses his/her disappointment that the scenario was specific and therefore he/she wasn t able to completely understand the relations and connections existing. The same participant pointed out that some of the questions asked needed clarification. The Author s lack of English writing skills and the enthusiasm to express in a short question all the power of the prototype developed may have caused this confusion. However, if the Author had the opportunity to repeat the evaluation, the input of an expert combined with more evaluation time would provide a solution to this point. 6.5 Further Work: The most critical tools that are available in the LeARN community are the Search and Advance Search engines. Both are the tickets to navigate through the various links to find materials a user need and discover the connections that may have with the others. Therefore, future works aims to focus on expansion of the searching parts so that deeper semantic understanding of the keywords entered will be achieved. The materials are in the database could increase significantly and all the user connections could be displayed with some form of Visualization. As soon as the minimum requirements of the prototype are completed and only one extension is completed, then all the other extensions could also be components of author s future work. Moreover, in a future work, the prototype could be improved according to the evaluation feedback received. Finally, future work could include the completion of discussion forum 63

71 where all the related users would be grouped and share ideas, questions and many more. Users profiles could also be constructed and used from other users. 64

72 7. Project Conclusion Completing a project of this magnitude has been a totally new concept for me. What at first seemed like a mountain, soon developed into an ordered routine of planning, coding, checking and refining. Developing specific methodologies and algorithms led to an application that is functional, easy to use and above all, applicable to the specific problem the project set out to address. Web based applications have become more and more complex and feature packed, as web user s expectations and demands have changed. My aim was to develop such a web based application to cater to the specific needs of a specific community. My end result is a web based application that successfully addresses these specific needs of this specific community, while maintaining all the every- day functionality web users today have come to expect. This came as a result of solid methodologies that have been proven to be functional. Development of a successful application can never really be said to end. As user demands change, so must applications evolve. I am confident my methodologies and algorithms and indeed the whole plan of the project will allow for future development to continue, fine- tuning the end results to reflect the changing technologies and expectations. 65

73 8. References [1] Huh, M.M.H.a.P.-W.L.a.E.-N., A Grid-Enabled Framework of Expertise Search Engine Using Web-Based Online Communities. Proceedings of International Workshop on Internet and Distributed Computing Systems, [2] Ueyama, R.I.a.H.T.a.K., Community Mining Tools using Bibliography Data. 2008, National Institute of Informatics: Tokyo, Japan. [3] Cook, J.B. Community Development [cited February 2010; Available from: [4] Chavis, D.W.M.a.D.M., Sense of community: A definition and theory. 10 Feb [5] Wenger, E., Communities of Practice: Learning as a Social System [6] Howard Rheingold, The Virtual Community, 1998; from: mes/rheingold.howard%20!the%20virtual%20community.pdf [7] Guidance for Implementing Net-Centric Data Sharing, DoD G, 12 April 2006, p. 11 [8] Wenger, J.L.a.E., Situated Learning: Legitimate Peripheral Participation. Learning in Doing: Social, Cognitive and Computational Perspectives [9] Zhuge, H., Communities and Emerging Semantics in Semantic Link Network: Discovery and Learning. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, June [10] Resnik, P., Using Inforamtion to Evaluate Semantic Similarity in a Taxonomy. 1995: USA. [11] Budanitsky, G.H.a.A., Semantic distance inwordnet: An experimental, application-oriented evaluation of five measures. 1999, University of Toronto: Toronto, Ontario, Canada M5S 3G4. [12] Seco, N.A.L., Computational Models of Similarity in Lexical Ontologies, in Computer Science. February 2005, University College Dublin: Dublin p [13] Ian Sommerville, Software Engineering 8, 8 th edition, 2007, England [14] Seco, N.A.L., Computational Models of Similarity in Lexical Ontologies, in Computer Science. February 2005, University College Dublin: Dublin [15] Zavaracky, A., Glossary-Based Semantic Similarity in the WordNet Ontology. Master s thesis, 2003, University College Dublin : Dublin, Department of Computer Science. [16] Pandey, K.S.a.L.T.a.S., Efficient Information Retrieval Using Measures of Semantic Similarity. 2005, Nepal Engineering College. [17] Richard Bergmair and Software Competence Center Hagenberg GmbH (SCCH) "The semantilog project", , Accessed in: 26/4/10, from [18] Princeton University, WordNet 3.0 database statistics, 2010, Last Update: April 13,2010, Available at: [19] CPAN (17 Jun 2008), Ted Pedersen > WordNet-Similarity, Retrieved 2010, from 66

74 [20] WARIN, H.O.a.M.V.a.M., Enriching an Ontology with WordNet based on Similarity Measures. 2005, Stockholm University: Stockholm. [21] IBM, Three-tier architectures,last updated: Apr 7, 2010, from e.doc/info/aes/ae/covr_3-tier.html [22] Princeton University, WordNet a lexical database for the English language, 2007, Last Update: 4 May 2010, from: [22] D. Lin, An information-theoretic definition of similarity, In Proc. 15th Inter- national Conf. on Machine Learning, 1998, p Morgan Kaufmann, San Francisco, CA. [23] Philip Resnik, Using Information Content to evaluate semantic similarity in a taxonomy, In IJCAI-95, p , 1995, Montreal, Canada. [24] J. J. Jiang, D. W. Conrath, Semantic similarity based on corpus statistics and lexical taxonomy, In Proceedings of International Conference Research on Computational Linguistics, (ROCLING X), 1997, Taiwan. [25] Claudia Leacock and Martin Chodorow, Combining Local Context and WordNet Similarity for Word Sense Identification, chapter 11, p MIT Press, 1998, Cambridge, MA. [26] Alexander Budanitsky and Graeme Hirst. Evaluating WordNet-based measures of semantic Distance, Submitted for publication. [27] Z. Wu, M. Palmer, Verb semantics and lexical selection, In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, p , Las Cruces, New Mexico, [28] Ted Pedersen, Siddharth Patwardhan, and Jason Michelizzi, WordNet::Similarity - measuring the relatedness of concepts, 2004 [29] S. Banerjee, T. Pedersen, Extended Gloss Overlaps as a Measure of Semantic Relatedness, In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, p , 2003,Acapulco, Mexico. [30] D. Lin, Automatic Retrieval and Clustering of Similar Words, Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2 [31] S. Patwardhan, S. Banerjee, T. Pedersen, Using Measures of Semantic relatedness for Word Sense Disambiguation, In Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, pp , Mexico City, February [32] S. Patwardhan, Incorporating Dictionary and Corpus Information into a Context Vector Measure of Semantic Relatedness, Msc Thesis 2003, University of Minnesota. [33] G. Hirst, D. St-Onge, Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms, In C. Fellbaum, editor, WordNet: An Electronic Lexical Database, chapter 13, pp , MIT Press, [34] Pederson, T. Retrived 24/03,2010, from 67

75 [35] Boanerges Aleman-Meza, C. H., I. Budak Arpinar, and Amit Sheth (September 7,8 2003). Context-Aware Semantic Association Ranking. Semantic Web and Databases Workshop Proceedings. Belin, Large Scale Distributed Information Systems (LSDIS) Lab. [36] Bou, B.,2003, WordNet (wordnet sql). 2010, from [37] Saabas, A., , Sphider PHP search engine. Retrieved 2010, 2010, from [38] Sheffiled, T. U. o., The RESuLT Project, 17/04/2007, from [39] Bou, B.,WordNet - wordnet sql builder, from [40] Ted Pedersen, S. V. S. P., Siddharth Patwardhan, Christopher G. Chute (2007) Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics 40, 2007, [41] Saabas, A. ( ), Ando Saabas, 2010, from [42] Chris Halaschek, B. A.-M., I. Budak Arpinar, Amit P. Sheth, Discovering and Ranking Semantic Associations over a Large RDF Metabase, Athens, GA 306, USA Large Scale Distributed Information Systems (LSDIS) [43] Offer Drori, Algorithm for Documents Ranking- Idea and Simulation Results, The Hebrew University of Jerusalem, School of Computer Science and Engineering. Israel, 2002 [44] Loren Terveen, W. H., Brian Amento, David McDonald and Josh Creter, PHOAKS: a system for sharing recommendations, 1997 [45] Dimitrova, V. (2010). Ranking Algorithm. Knowledge Management and Adaptive Systems - COMP3740, LN:28, University of Leeds, School of Computing, 2010 [46] Amazon Recommendations, Item - to Item Collaborative Filtering, Greg Linden, Brent Smith and Jeremy York, IEEE Computer Society, 2003 [47] Knowledge Management and Adaptive Systems, Lecture 29: Recommender Systems (Part 2):Item-Item Collaborative Filtering, Amazon s algorithm, Ronald Denaux and Vania Dimitrova, School of Computing, University of Leeds, 2010 [48] Ltd, D.A. Database Answers, [cited February 2010]; Data Models Examples. Available from: [49] Ltd, D.A. Database Answers. [cited 2010 February ]; Available from: [50] Web Developers Notes, Article on client server architecture and how web pages, both static and dynamic, are displayed on the client (browser) from the server, 2010, from [51] Ted Pedersen, S. V. S. P., Siddharth Patwardhan, Christopher G. Chute. WordNet::Similarity, from 68

76 9. Appendices Appendix A. Personal Reflection Working as a computer scientist in a work environment and working for course-works and assignments at a University are two completely different concepts. In my opinion however, working on a Final Year project is an solid introduction to what a Computer Scientist can expect in a real job. Of course no one individual would be expected to complete all the stages of a project in a commercial environment, but it is very good preparation for final year students. When I first started project meetings with my supervisor, she asked me questions such as What are your interests and ambitions, What are your strengths and weaknesses, What do want to do after your graduate and so on. I wasn t able to accurately answer those questions because at that stage of my life, my academic career had consisted of following instructions from my supervisors, lecturers and others. With the completion of this project however, I can confidentially provide accurate answer to those questions. Having never worked on such a project before, it was a challenging task. It took me 2 weeks to realise that the project was my own work and it was completely up to me to decide what I wanted to do. I decided then and there that this project should be completed at all costs. The decision was, I believe, a good motivator. I decide to set two goals. On the one hand, involve myself in something which would be challenging enough to stretch me, and on the other, be of a standard acceptable for my University Degree and also a good demo for my portfolio (for the real job mentioned above). Thank God, I had continued and ongoing support and advice from my supervisor and ultimately, I completed a project that I believe achieved both of my goals. Working on the project gave me the opportunity to start coding my own applications in what were completely new (for me at least) languages. For the last two months, the book PHP and MySQL Web Development has been my best friend. My programming skills have improved to the point where and I can now develop web-based client server applications using PHP, HTML, Javascript and MySQL. Having to come to grips with a range of new tools (Navicat Professional, Adobe Dreamweaver, etc) did take up a lot of my time and made my project more complicated, but did give me a very good grounding in these tools. In addition, I have developed an understanding of the importance of semantic similarities between words and I discovered new 69

77 tools like WordNet::Similarity API, Disco, Weak, to name just a few, that have given me new ideas and concepts to further develop my project and make it even more powerful and useful. Despite gaining a considerable amount of knowledge during the implementation of the project, I also had to overcome various obstacles. These were mainly the selection of the correct tools and programming languages. I would strongly recommend that all final year students make the right choices of tools and languages as in my case, I faced a serious setback through choosing the wrong programming language. This mistake seriously impacted the functionality of my prototype and ate into precious time I could not afford. In my opinion, I could have developed a better prototype, with many more semantic connections rather than connections between users, if I had made the selection of the correct tools and programming languages from the start. My supervisor did point this out, but as I wasn t experienced in PHP, I first selected the languages and then the tools. Computer Science may be a universal discipline and I study at an English University, but my native language is Greek. Compiling a detailed report in English was not something I d done before, but continued work over the life of the project and the continued feed-back from my supervisor have make a marked improvement in my confidence in writing such reports. I would like to conclude with a note that I strongly believe will be of value to all students undertaking future projects: Time management, schedule, appropriate methodology, and a good supervisor are the compulsory ingredients for the successful completion of a final year project. These elements will help you design something worthwhile, functional, completed on time and, why not, worthy of a good grade. 70

78 Appendix B1. Original Project Schedule Appendix B2. Original Project Schedule 71

79 Appendix B3. Analysis of Schedule Final Report: Continues writing carried out simultaneously with Research and prototype design, implementation A Draft submitted as mid-project report is revised and used in the final report A Draft submitted to the supervisor is reconsidered and the appropriate changes are completed. Evaluation: Testing of the LeARN prototype Comparing user opinions and WordNet::Similarity API results for semantic similarity of Keywords Comparing user opinions with the publications returned by the LeARN prototype Feedback from potential users (KRR Group members, School of Computing students) Prototype Design: Implementation of Algorithms used Implementation of queries that discovers connections between users Implementation of queries that discovers connections between materials Mid-Project Report: A brief write up of the Background Research done until 05/02/10. Research: Connections / Relatedness Community Algorithms, tools and methods can be used Semantic Similarity Research Plan: Organize in a Schedule what areas should be researched 72

80 What the available tools and methods are What algorithms should be considered What are the tools can be used in the particular project What is the type of LeARN community What programming languages to used Prototype Research: Client Server architecture Database design User interface design Connections using queries and WordNet::Similarity API Users possible connections Materials possible connections Project Plan: Clarify the requirement analysis by considering the following things: What the overall aim of this project is What the objectives of this project are What are the minimum requirements Use Mind map to design a project schedule *Notes: Meetings with the supervisor started on 04/12/09 and completed on the 10/05/2010. (Things discussed are in a log book can be submitted if required) Seminars with Knowledge Representation and Reasoning (KRR) Group started on the 29/01/10 and completed on the 18/03/10 Two presentations completed in KRR Seminars (Slide used can be submitted if required) 73

81 Appendix C. Background Reading and Research Plan 74

82 Appendix D. The description of community data schema There are many ways to represent the tables of the LeARN application database in a scheme. The following are the most compatible. Each table formed as follows: 1. Users: The entity user is a vital component of the database and it should include details such as Title, First Name, Last Name, Occupation, Work address and Username/ Nickname and password the user wants to use in the community but it can also provide information such as Phone (office), Office Location / Address as well as Occupation. (Navathe et al, 1982). Of course, there are some other details that could be included such as Phone (home), private address, Postal address, interests, personal statement,website (office), Website (home) but for the needs of the community this extra information is not needed due to the privacy of users as well as due to the needs of the specific requirements of the community has. (BSCW, ) 2. Material: The entity material is designed based on the Bibtex database. Oren Patashnik and Leslie Lamport (1985) points out that an article requires the following details: author, title, journal, year and optional details could be: volume, number, pages, month, abstract and key. Similarly, other fields are compulsory for books, other for journals, etc (Patanik.1988).In our application, the required fields for the general types of material we will be are using are the isbn, material_title, material_abstract, material_date_of_publication and category_id. Of course, some other information could be included but we decide to use only those which best fits the data we will use. According to some experts, bibliographic information varies according to the type of material you have. Based on the EndNote X3, a book requires different information than a journal or a newspaper. Although, in other papers and programs some other information may appears as optional and some other as required. (Fenn, 2007) Taking into account all the experts opinions, in this specific application we use only the information shown above because these details are enough for each material so that the community will function properly. Except from experts decisions, some online libraries such as Portal (Machinery, 2010) and SpringerLink (SpringerLink, 2010) use specific details for each particular material that are useful for our application material table. Furthermore, we are using only the table material instead of using one table for papers, one for other publications, etc and that is the main reason we are using the category_id. All the materials will be available in table material and each material will be assigned to one category. If necessary one material may be relate to more than one category. (For more details see part (5)) Moreover, instead of using author details and other similar information, we are using the isbn (International Standard Book Number) which exists only once and it refers to only one unique 75

83 material. There is a relation between isbn of material table and isbn of author table. In the case where a paper has more than one author then there is no need to save the material as many times as the number of authors. We are using isbn to give us the authors material. This will give a good normalization form within the database. 3. Author: The author entity is designed based on the Bibliographic information files forbibtex. As a result, according to experts, this entity should include information such as author_title,author_first_name, author_last_name, author_details, author_id and finally isbn for the material. All the other details such as occupation, middle_name, address (work), , address (home), city, Phone(home), Postal address, interests, personal statement, Website (office), Website (home) are not required in our application since authors will be staff within the University of Leeds and for safety reasons we do not want to publish personal information of staff online. If a person / author decides to provide his/her information, this can be achieved during their registration in the community where he creates his profile as user and some of this information could be submitted. Taking into account ACM Portal and SpringerLink, details we are using for author are enough since the only available information is the name and surname of author and sometimes its organization where in our approach, organization is the University of Leeds and therefore is useless to include it. 4. Course: The table course, has been designed aiming to avoid repetition of the same information within the database. The information in this entity are the course_code which is unique and the course_name which is necessary to know. By using course_code of this table and the course_code of assigned material we find which material is related to a specific course. [48,49] We don t include this information in table material because if we include it, it will be stored too many times rending this redundant. 5. Categories: The entity categories are requires information such us category_id and category_name only. Those 2 should be completed so that we can find which material is related to a specific category (books, articles, etc). This aims to avoid repetition of information in the database and the best normal form. 6. User_Request: The entity user_requests requires data such as request_id, and date_request. According to Luke Welling and Laura Thomson (2009), this information is sufficient for the purpose this table is designed for. There the only information will be saved is which of the users requested a specific (isbn) material and when. 7. Assigned_Material: The entity assigned_material requires data such as 76

84 assigned_material_id, isbn and course_code. This table in designed in this database in order to avoid repetition of storing the course_code as many times as a material exists. It is helpful because it stores which material is related to which course as soon as course_code is its foreign key. 8. Material_by author: The entity material_by_author requires mba_id (material by author_id), author_id and isbn. According to the information found in Database Answers [48,49], by including this table in our data model the normalization form of the database is improved. The materials are categorized here by author respect to author_id that is related to the same attribute in the table author and to the isbn which is connected with the isbn of the table material. Any other details such as date of publication, category, course, etc are not included here as the relations between the tables helps to return this information without been in this table and stored a lot of times. 9. Occupation: The table occupation includes the roles of the users and it requires an occupation_id and occupation_type. It has been simple designed because it only stores the possible occupations of users existing within the community. It aims to avoid repetition of storing the same information more than once within the database. 77

85 Appendix E. The core tables of WorNet SQL database 78

86 Appendix F. MySQL query (section 2.7.2, p. 19) result articulate put into words or an expression bible the sacred writings of the Christian religions book the sacred writings of the Christian religions christian bible the sacred writings of the Christian religions countersign a secret word or phrase known only to a restricted group discussion an exchange of views on some topic formulate put into words or an expression give voice put into words or an expression give-and-take an exchange of views on some topic good book the sacred writings of the Christian religions 79

87 Appendix G. Sphider Sorted Results in LeARN prototype 80

88 Appendix H. Ranking Algorithm 1 [45] Input: Data: Materials rated by each user Output: Recommended material 1. Find nearest neighbors 1.1. Discover similarity with each user by calculating the similarity using cosine 1.2 Define neighborhood i. Centre- based neighbourhood (size n) by sorting and choosing the first n ii. Aggregated neighbourhood (size n) by taking the user closest to the centroid at each step and repeat (n- 1) times 2. Weighted sum by 2.1 Scanning the neighborhood and calculating the frequency for each item 2.2 Can be Combined with the rating value 3. Association rules recommendation by 3.1 Expanding the number of materials based on association rules upon what has been recommended by the neighbours 81

89 Appendix I. Prototype Environment Screenshots 1. A general preview of the prototype interface: The home page of Leeds Academic Research Network 82

90 2. How to Sign_Up: The Membership page of Leeds Academic Research Network 83

91 3. How to interact with the environment The first actions a user can perform. 84

92 4. How to Search and retrieve the desired results Enter keywords, search and receive related materials 85

93 5. How to read information for a material and how to download it Selected material presented and downloaded 86

94 6. Advance Search Steps that should performed by Advance Search 87

95 Results of Advance Search 88

96 Rate and Rank functionalities are added to the environment 89

97 7. The possible connections of LeARN environment: (The complete interface) 90

98 8. The Upload form in Services option 91

99 Appendix J. Algorithm implementation coding parts Algorithm 1: Related users based on Downloads Related users based on people Likes a material 92

100 Algorithm 2: User-Based Related Keywords implementation 93

101 Add keywords LIKED or DON T LIKE by user to the learn database The likes are calculated and displayed in the user as shown in figure 5.2.g by the following function: 94

102 Algorithm 3: How results of WordNet::Similarity API could be implemented in the LeARN Environment if was available in PHP. 95

103 96

104 Algorithm 4: Recommended materials based on ranking Add keywords ranked in the learn database: 97

105 Appendix K. Client-server architecture, with server side scripting technologies [50]. 98