CHAPTER 7. Observations, Conclusions and Future Directions Observations 7.2. Limitations of the Model 7.3. Conclusions 7.4.

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1 CHAPTER 7 Observations, Conclusions and Future Directions 7.1. Observations 7.2. Limitations of the Model 7.3. Conclusions 7.4. Future work Domain-specific Ontology for Student s Information in Academic Institution Page 169

2 CHAPTER-7 Observations, Conclusions and Future Directions This Chapter summarizes the work that was carried out by me. This Chapter includes the observations made with regards to the model developed. It includes the observations with regards to the testing that was carried out over the model for consistency and completeness. Since the scope and the method of development vary from researcher to researcher, the limitations of the model have been identified. It also concludes the work carried out. A brief discussion on the future usefulness and extensions of the use of Ontology is also provided. 7.1 Observations During the designing of the model, a few observations were made regarding the model, its working and the testing of it About the Model Following are the observations regarding the model that has been prepared: i ii iii iv v The model uses the mathematical representation of a graph which consists of triples i.e. the subject-predicate-object model. The model is equivalent to a Directed labeled graph in a single direction. The Classes form the nodes of the graph. The connections between classes are formed using Object properties. An Object property defines the relationship between 2 objects. These are similar to the labels over the directed arcs. The Classes have their own attributes which are identified as Datatype properties. Domain-specific Ontology for Student s Information in Academic Institution Page 170

3 vi vii viii ix x There are a few Datatype properties available in the Upper ontologies DC and FOAF, which have been reused in the model to provide for the semantic networking of ontologies. Each Class where the values are predefined, those values have been converted into sub-classes with one instance inside each. A Record for a class is identified as the Instance of that class. An Instance of a Class is linked to one or more instances of other Classes based on the type of Object property created for linking those Classes. It forms a layout similar to a network diagram where every node is reachable from any other node via multiple interlinking nodes. xi The network so formed is a one-way directed graph except for 2 classes where reverse referencing is provided so that a search on the unique data is possible, just as in RDBMS with primary key. xii xiii The model being open-ended can be incorporated as a whole or partially in any application requiring similar information representation data model. Also the model can be extended by adding a link to another existing ontology, thus resulting into a larger networked ontology, which forms the basis of the semantic network. xiv The ontology model consists of 105 classes, out of which 42 classes form the first level whereas 63 form subclasses. xv xvi A total of 82 Object properties are created to link a class to another class. These properties are used to connect 22 unique Domain classes to 42 Unique Range classes. Out of 82 Object properties, 53 are Functional and 2 are Inverse-Functional Object properties. Functional Object properties restrict the relation of an instance from one Class with only one instance from the other Class. The Domain-specific Ontology for Student s Information in Academic Institution Page 171

4 reverse may not be true i.e. 1:1 relationship in E-R diagram in only one direction. xvii Inverse-Functional Object property indicates that an instance of one Class is related to only one instance of another Class and that instance of the other Class is related to the same instance of the first Class i.e 1:1 relationship in E-R diagram in both ways. xviii There are a total of 152 Datatype properties used in the model. Out of these there are 149 Functional and 3 Non-Functional properties. xix xx xxi Functional Datatype property ensures that only a single value is permitted for an attribute. Non-Functional Datatype property allows for more than one value for an attribute within a single instance. The model has reused 5 Datatype properties from the DCTERMS and 8 Datatype properties from the FOAF project. The reused properties are: dcterms: abstract, identifier, date, title, publisher FOAF: name, gender, title, mbox, birthday, phone, age, fundedby xxii For all the Classes where there is One-out of-many options to be selected, the options have been converted into classes connected through a single Object property and having a single Datatype property on the parent Class, inherited by these disjoint subclasses. Also single instance per Class has been created on these Classes. There are 18 such Object properties which connect more than one Class to its parent Class. xxiii The model works on the standard inheritance of hassubclass and hasinstance relationships. If a node has another node within it, then it forms the parent-child relationship. xxiv Each of the Class, Object property and Datatype property is documented using the dc:description property from DC. Domain-specific Ontology for Student s Information in Academic Institution Page 172

5 xxv xxvi When the model is visualised as a simplified tree, it results into a 6-level tree structure with the Student Class as the root node. The Student Class is considered as the root Class and the starting point of the ontology. However, any Class can be used as the starting point for querying since it forms a networked structure. xxvii The model works almost similar to the structures created in any network data model. The understanding has a lot of similarity to the DBMSs Observations during the testing of the Model Following are the observations during the testing of the model: i ii iii iv v vi vii A total of 449 Instances for a total of 5 Students has been entered to test the RDF Dataset The test query involving all the Classes results into a Cartesian product of all the instances. The query on the model may start from any node as long as the query proceeds downwards into the subclasses as it is a directed graph in a single direction only. The number of classes involved is huge and hence it reduces the visibility of the data drawn from a SPARQL query string. Hence partial queries were also tested and then combined with the main query to form a complete profiling query for a student. The queries are written similar to the understanding that is used with SQL in any DBMS. The query form used for testing was SELECT query form only. The query requires regular and complete graph patterns for it to be executed correctly. However, such complete structures may not be available at all times. The query would fail when a class containing no matching instance was used. To overcome this default effect of Domain-specific Ontology for Student s Information in Academic Institution Page 173

6 the SPARQL, blank nodes have been used for testing wherever the data is assumed to be missing. Nonetheless, there are various options with the SPARQL like OPTIONAL or FILTER options that can be used instead of entering blank nodes. The blank nodes have been represented here as (two hyphens). viii ix x xi xii xiii The intention of the query was to test the completeness of the graph pattern and locating for all the triples from the graph, which could be seen easily by extracting a single Datatype property from each class. Hence only the Basic Graph pattern matching queries were executed against the ontology. The SPARQL queries return results in RDF graph format, which can be used directly in any application or can be used in as an input for another query. Since the query binds data to variables, it is easy with a one-way directed graph, since the intermediate unnecessary data may be omitted from the output. While testing, you may generate the instance ID in the display as well as any of the Datatype property values of the class. SPARQL Property Path expressions have been used in the evaluation of the partial queries. Since all the Object property at the first level start from Student object, it is considered as the starting point for my testing. This is not always necessary as long as a direct or indirect path is available between any two classes. Hence, testing by querying for unknown relationships is possible. The Jambalaya add-on with Protégé provided a pictorial view of the interconnections between nodes and a way of using structuredwalkthrough to identify the missing connections and links between Classes. xiv Any Class not connected directly can be accessed via their common parent Class at any depth.the testing of the ontology Domain-specific Ontology for Student s Information in Academic Institution Page 174

7 shows the completeness in terms of connectivity between different Classes and in terms of accessibility of the all Classes, Object and Datatype properties. 7.2 Limitations of the Model i ii iii iv v vi The Ontology is restricted to contain the information of the Student from the future point of view. It means that the working information on a day-to-day basis like Attendance, Library transactions, Financial transactions, etc have not been included. Hence the scope is restricted to the amount and kind of information necessary for creating a complete biodata. Ontology is used for storing relationships between data to reflect the semantics and associations. Thus the Ontology does not subscribe to the requirement of storing day-to-day information as well as working information of the student. The Ontology does not have control over the kind of data that can be entered in a class attribute. This restriction can be implemented in the Application that is constructed over the ontology. Though the Upper ontologies, FOAF and DC, have a few generic properties, all of them have not been used in my ontology for the sole reason that the requirements were different. The ontology itself does not have any facility for mapping and integration. Neither does it have support for inference. Hence integration with other Framework or API would be required to explore the full capabilities and usability of the ontology. There are no rules included to support for inference with the purpose of making the ontology open for integration within an institution having their own rules implemented within the application working over the ontology, thus not restricting the use of the ontology. Domain-specific Ontology for Student s Information in Academic Institution Page 175

8 vii viii ix x xi xii As the number of Instances of Student class and its associated instances in the other Class increases, the ontology would expand in an uncontrolled way, which can be controlled only through the use of an application which would act as an intermediary between the ontology representation and the user. Issues pertaining to the Semantic web and the Semantic Stack like Security, Proof and Trust needs to be addressed while implementing the application over the ontology. Since multiple ontologies covering partial aspects of my domain are already available, it becomes necessary to identify a mechanism for ontology mapping and alignment. For this, a few projects are already undertaken worldwide, which must be fully implemented for covering overlapping domain ontologies. Unless an application is developed to consume the data of the ontology, the users cannot use it directly as it will require a minimum knowledge of query language. As the amount of information to be included in the schema is huge, it becomes practically impossible to manage the code manually. Hence it would require the knowledge of an ontology tool for editing. Since the ontology has been tested using an ontology editing tool, its implications, when implemented over the web needs to be assessed. 7.3 Conclusions The Ontology is an attempt to reduce the plea of the academia where loads of information is required to be stored and made accessible to anyone, human or agent, from anywhere. The Semantic Web is a promising technology of the future whereby the entire data can be made available to the users over the Internet without the need for any Domain-specific Ontology for Student s Information in Academic Institution Page 176

9 proprietary software applications. This ontology will allow the users to break-free from the traditional storage patterns. It will also help in the automatic discovery of information by a webbot. It was decided to construct the ontology in a modularized way to encourage reuse, where other ontology and applications could pick and choose the modules applicable to their requirement. Since the ontology is serialized in RDF/XML format, all the facilities available for querying can be used over the developed ontology. Many ORLs are available for the Semantic web as seen in the earlier Chapters. The current scenario is creating ontologies in RDF/OWL languages. Thus, the ontology made is future oriented and will be usable with the newer technologies that are existing or will be developed in the near future. Since the ontology has been developed using OWL-DL, the agents and the semantic web browsers will be able to take advantage of the DL and FOL for deriving new knowledge. Since I have customized the ontology engineering process from combining a few popular methodologies, incluing the General methodology, Methontology and OTK Methodology, the model development followed a standard way. Protégé provides a framework which is easy to use for creating the ontology. Because of its inherent Ontology engineering methodology, which matched my ontology enrineering process, the creation of Ontology took lesser time in comparison to using another methodology with a separate Ontology editor. It provides for creation of OWL ontology serialized in RDF/XML. The Protégé tool s SPARQL query facility allows for testing the Ontology without using an external testing tool. On the whole Protégé provided a great support in the development of Ontology. The main applicability area of the so developed ontology will be for profiling the information of the students in the academic institutions, Domain-specific Ontology for Student s Information in Academic Institution Page 177

10 over the Semantic web, which is currently not available. The information can be placed on the web without the need for any proprietary database store, thus allowing for interoperability and integration. The information stored in the repository can be used by the students themselves to keep a track of their personal updates and information. Applications over this ontology may be developed in such a way that the student themselves can continuously update their own information over the web. This will help the organization to keep a track of the students information in a standardized, centralized and platform independent way such that it is updatable, searchable and easily mergeable with other ontologies in the same or other domain. It can also be helpful for the future employers to trace the details of their prospective employee from his past institutes of study. It will provide for reusability of domain knowledge. The ontology can even be used by various universities for profiling of the students. This will ensure standard structure of information representation by all the institutions [41]. The Semantic Web browsers and software agents will have the capability to identify relationships and understand semantics of the data due to its basic property of being linked. Relationships may be defined from level to level based on the triples declared between the nodes at any levels. Since meaning is associated with the data within the structure, the search engines and software agents will be able to differentiate between the requirements and other search results. 7.4 Future work Ontology is the basic building blocks for the Semantic web. Once the basic level of ontology creation is over, the real work of using them for information exchange and searching begins. Creating a network of ontology is a humongous task. The ontology must converge information from all spheres of the world. It must be tested for efficiency and Domain-specific Ontology for Student s Information in Academic Institution Page 178

11 effectiveness with worldwide use and integration from multiple heterogenous sources. The enterprise database infrastructure today consists largely of high-end transaction processing systems on one hand and RDBMS-based information systems on the other. Over the last few years, however, there has been high demand for accessing these enterprise databases over the internet, and also to use them, not only in information searching and exchange, but also to support e-business operations. These include transactions between systems within an Enterprise through Enterprisewide integration, between Businesses in a Supply Chain and between Businesses and Customers. The initial reaction of most companies was to integrate these diverse operations by building or buying softwares such as application servers. Recently, XML offered the option of performing the necessary integration by exchanging standardized data. The SQL standard defines the most basic way of finding and extracting data from an RDBMS, so it is easily possible to write middleware applications that would extract data from one database and put in another. But these applications are relatively complex and must pay careful attention to the database schemas on each side in order to work properly. XML, on the other hand, is simply a standardized meta-format that can represent any kind of data [181], and for which precise schema definitions are optional. Multi-agent systems have received much attention in recent years because of their many advantages in complex and distributed environments. In the era of Semantic Web, the ontologies have established itself as a powerful tool to enable knowledge sharing and it is an important means in Semantic Web to achieve the semantic interoperability among heterogeneous systems distributed over the Internet. Both ontologies and agent technologies are central to the semantic web, and their combined use will enable the sharing of Domain-specific Ontology for Student s Information in Academic Institution Page 179

12 heterogeneous, autonomous knowledge sources in a capable, adaptable and extensible manner. I plan to convert it into a standard through W3C. It will be put on the Internet for others to access and use, so that the applications can adhere to the ontology structure. Since this ontology is open-ended, anyone can add more features and thereby extend the use of the ontology. After the ontology base is in place, the applications for the relevant ontology may be created on top of them, which may use this ontology for storing relations of information from anywhere over the Internet. This ontology provides a structure that represents a Knowledge-base. Though the Semantic web allows agents to interoperate in open multi-agent system and overcomes one major issue of using some agreed language and logic to provide sufficient expressive power required for agent interaction, another challenge lies in the creation of ontologies that can help the agents in the everyday interaction in a multi-agent system. In addition to the development of ontologies for commitments, agents and semantic web need ways to express the reason about the reputation, trustworthiness and security of the agent that they encounter as well as the data within the ontology. Upon that, the application may need support for searching of the ontology wherein the Semantic web browsers come into picture. Acting upon the ontology, agents like FIPA or KQML may be used which can allow for interoperability among software agents and provide agent-agent communication, given a common thread of connection between them and a common ontology in their KB and thus supporting for automatic interaction and exchange of information. The fundamental concept of the Semantic web will be the markup of Web services to make them computer-interpretable, use-apparent and agentready [132]. Conventional web services use XML standard for syntactic interoperation of web services. For example, WSDL which can specify the Domain-specific Ontology for Student s Information in Academic Institution Page 180

13 operations allowed and the structure of the data exchanged without considering the semantics of it. Semantic web services use universally accepted standards for semantic data interchange. Hence it is possible to combine data from various diverse sources. But a more semanticallyoriented language may replace the web services scenario by adding semantics to the web services. Also more sophisticated replies can be formed from the semantics of the networked data. Some examples of Semantic Web Services include: Simple Semantic Web Architecture and Protocol (SSWAP) uses OWL, Semantic Automated Discovery and Integration (SADI) uses RDF, etc. The ontology can be used along with keeping track of pattern analysis of links on-the-fly by an agent while the user is searching, which shows which data/links are viewed together to establish a sequence. Also, which probable keywords would be used next by the user can be used to suggest to the user beforehand [40], thus maintaining a better result display in terms of timings and quality. Since a few partial ontologies are available in my domain, some of which are shown in Chapter-3, they may be available with different Class names and Property names. To provide a unified understanding of all the terms, we can take advantage of ontology mapping tools like Ontology MApping FRAmework for distributed ontologies in the Semantic Web (MAFRA) [148], PROMPT [144], Quick Ontology Mapping (QOM) [57], etc., thereby reducing the risk of misinterpretation and correlation of same information represented with different names over the Semantic Web. Domain-specific Ontology for Student s Information in Academic Institution Page 181