Providing Information Sources Domain for Information Seeking Agent From Organizing Knowledge

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1 2014 1st International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE) Providing Information Sources Domain for Information Seeking Agent From Organizing Knowledge Istiadi 1,2 1 Department Of Electrical Engineering Widyagama University of Malang Malang, Indonesia istiadi@widyagama.ac.id Lukito Edi Nugroho 2, Paulus Insap Santosa 2 2 Department of Electrical Engineering and Information Technology Universitas Gadjah Mada Yogyakarta, Indonesia lukito@ugm.ac.id, insap@jteti.gadjahmada.edu Abstract This paper proposes a tracking mechanism to obtain the information sources that are stored on the personal knowledge organization which will be used to direct the search of information on the Internet by a software agent. Semantic representation of the organizer is viewed as a map of the information sources that are classified hierarchically based on the scopes of knowledge domains from the standpoint of the agent. The tracking mechanism by query will look for the information sources in the knowledge domains based on the same scope of a defined knowledge domain. This tracking will produce a list of information sources that will be followed to get the domain location as internet access preferences Keywords Knowledge organization; information seeking; domain information I. INTRODUCTION One dimension of Self-directed learning is finding resources [1]. The Internet is one of the many information sources that provide open content of knowledge resources. However, the abundance and diversity of information on the Internet is one of the problems to obtain the relevant information. Ensuring relevant information requires much attention, so that the availability of information seeking tools will help reduce the effort. The study of the web information retrieval have generally been done on the repository system which organize of centralized resources, such as personalization of preferences and navigation behavior in [2], personalization of browsing behavior and collaborative filtering in [3], and the use of world knowledge base in [4]. However, resource organizers that are personal and local as in [5] have not been exploited further. While in the framework of cognitive tools [6], in addition of the knowledge organization, also need information seeking feature which supports the provision of information as learning materials. When information sourced from the Internet as the knowledge resources has been organized through a semantic representation, the representation potentially be utilized as basis for computer operation [7]. In the organizing, the information has been classified in the domains of knowledge [8] and if the information sources (URLs) are also stored in classified system [9], then the sources can be utilized as preferences to exploit for the search area of information within the same scope of knowledge domain. This approach is based on the concept of user behavior in accessing the Internet [10]. Internet users generally use their access experience to get the information needed in certain locations. When the user has experience to obtain information on a domain from a particular source, then this domain probably referred back to the complement of other relevant information. Sources location that remembered by the user will help in limiting the scope of information search space. Tools with autonomous capabilities (such as software agents) require a knowledge base as a basis for reasoning [11]. In the context of the information seeking, the knowledge base should be able to provide preference to support the information searching. So, the agent can act more efficient to seek the related information that are needed. Based on the aformentioned concept, this paper proposes a tracking mechanism in personal knowledge organization to obtain information sources that in the next step should be extracted by functional part of the agent to get the domain location as access references. This domain then can be used to focus the information search area by the software agent. Furthermore, we start with explanation of knowledge representation structure which is used on the knowledge organizer. The next step we discuss the information seeking approach based on the location domain. Finally, the tracking mechanism that proposed to provide the location domain will be explained with an example of simulation using Protégé software. II. SEMANTIC STRUCTURE FOR ORGANIZING PERSONAL KNOWLEDGE Knowledge organization requires a knowledge representation as organizing scheme. The scheme with semantic structure has the advantage, as it allows to be traced by machine (computer program) that on particular needs can be used as base its operations [7]. Ontology is one of Semantic /14/$ IEEE 285

2 model which provides definition of the concepts and their relationships formally [13]. Schematic model of organizing in this study using ontology approach to express the concept of knowledge structure and the concept of knowledge objects. Knowledge structure level describes the relationships among the information in the knowledge domain (KD), while the knowledge object (KO) is a representation of information resources as digital document. This scheme (Fig.1) was adapted from the ontology model in [8] and the previous work [9] by simplify the scheme only two levels and add some relation to accommodate some kind of relationship which inspired from [13, 14]. In [13], Organizing scheme declared into some categories relationships including hierarchical, associative, and equivalency. In [14], there was providing some elements to describe a digital document and utilizing some specific relationships to organizing the documents. knowledge object as description of digital documents. A part of these properties is the document source that may contain the address of the Internet resources in the form of a URL. Furthermore, this property will be tracked and exploited to support information seeking. The design of the above scheme is still a concept model which needs to be implemented using ontology language that can be operated by a computer program. OWL is a language to express the ontology on the web which is a W3C standard [15]. OWL is used not only for web-based applications, but also enables for desktop applications [16]. Ontology development has been supported a number of software include Protégé. Protégé provide a GUI-based development ontology to define a class, object property, datatype property and to enter individuals [16]. After design phase then can be generated serialization format of OWL. Query operation feature using SPARQL allow testing and simulation can be performed to obtain certain data. Fig. 2 presents the development of knowledge organizers ontology that has been designed. The kinds of pairs relationships structure: - ispartof/haspart - isreferencedby/references - isbasisfor/isbasedon - isrequiredfor/requires - UsedFor/Use Fig. 2. Ontology development of knowledge representation Fig. 1. Ontology design of knowledge representation According to Fig. 1, there are two definitions of classes, the Knowledge Domain (KD) and the Knowledge Object (KO). KD class represents the structure in the presence of relations in hierarchical, associative, and equivalency categories. Relation ispartof / haspart is a medium to express the hierarchical structure which is used to build the classification, while the other relations included in the associative, and equivalency. KD class connected to KO class through hascontent / iscontetof relation. KO class has properties related to the III. INFORMATION SEEKING APROACH BASED ON LOCATION DOMAIN In general, a web site contains information resources in a particular domain [17]. Web resources locations organized on the server by using the URL format [18]. A host of web marked as domain names (URL base), while the web elements allocated in certain paths. The existence of a domain name allows developed advanced search in the search engines which can limit the search to a specific host. Users can specify a search on a site with a domain name and include the keyword. This agrees with the behavior of users in accessing information on the Internet. The user behavior in accessing information on the web is generally influenced by their experiences [10]. The user experience is formed when a site to be a concern because it 286

3 Fig. 3. Case example knowledge resources organization contributes significant information or user often explore the site so that the scope of any information found stored in memory. So, users can directly access to certain sites that have been known the scope of its contents to get the other related information. Based on the approach, when the information resources along with the data source has been mapped in the knowledge representation, then it can be viewed as a memory of information source on the other side. The sources of information that can be viewed classified based on the scope of knowledge domains. If the user expects other information of a domain of knowledge, the information was chance be found in the domain name of environmental resources that are already available. IV. TRACKING DOMAIN INFORMATION SOURCES FROM ORGANIZING SCHEMA In the hierarchical structure of the knowledge organization, the scope of the knowledge domain is composed from a broad scope until to the narrow scope. It also illustrates the potential level of the breadth content of resources are covered. When a new knowledge domain is added as part of an existing domain, then the tracking process can be started from the domain knowledge at the level closest to the broader level. The mechanism of the process of tracing is presented as follows: a. Begin from the position of defined a new knowledge domain, go to the knowledge domain on the upper level that covers the surrounding areas. b. From the knowledge domain that found, get all the knowledge domain below and get the sources of information in the knowledge object. c. If the location of information sources (URL) are found then extract them to obtain a domain name (URL base) as the preference of the search area needed information. But if the location of the information source is not found, go to domain knowledge on a wider scope on the upper level and repeat b step. As illustration of how the tracing mechanism is operated, the following description will explain through an example and simulation using protégé. For example, someone interested in studying knowledge of database. In the learning process some information resources has been organized as shown in Fig. 3. Fig. 3 just show the hierarchical structure (haspart relations) from KD as a case example, while any other kind relationships and KO elements is not displayed. In structure it appears some KD in the scope of database knowledge that has been explored. Among some KD, there exist SQL KD that cover SQL_Delete KD, SQL_Insert KD, SQL_Select KD, and SQL_Update KD that are contained documents obtained from sources on the Internet as shown in the picture. For example, the person has the insight that there is a procedure in a SQL query to create view, but not know it in detail. Perhaps he hopes to supplement the information from the sources that contribute. A SQL_View KD can be defined as new KD with include some words such as "Database SQL View" as keyword and expect the software agent can complete the content with relevant information. Based on the tracking mechanisms that have been identified, then the process is presented as in the following stages. First stage, starting from defining the SQL_View KD to find KD at the upper level. This can be done with a query by selecting KD with haspart relation to SQL_View KD. Fig. 4 shown the query to obtain upper KD and the result. 287

4 Corresponding the proof of case example, the tracking mechanism can be formulated as function as basis for computer operations. The function is declared by borrowing SWRL notation[19]. Fig. 6 capturing knowledge structure as illustration that used for the function formulation. D x D y1 D y2 D y3 D yn D o O y1 O y2 O y3 O yn Fig. 4. Query the upper level of KD Fig. 6. Illustration of knowledge structure for function formulation The results of the query will take on the position of SQL KD. The next stage, from the current position find all KD below and get the information sources of KO in it. The query command to obtain the information sources shown in Fig. 5. If Do is a defined new knowledge domain, then the operation to get the knowledge domain that cover it can be expressed in (1). ispartof(?do,?dx) coverage(?dx) (1) Once the knowledge domain is found in Dx, then the operation to obtain the information sources covered by Dx can be expressed in (2). haspart(?dx,?dy) hascontent(?dy,?oy) DocSource(?Oy,?Sy) contains(?dx,?sy) (2) Function in (2) is similar to the process of selection of information sources (Sy) that is filtered from the DocSource data property in the knowledge object (Oy) as content of knowledge domains (Dy) are covered by Dx, so the output function can generate a list of information sources in the scope of the knowledge domain. Furthermore, the functions that have been formulated then can be used as a reference for computer operations in tracking the information sources in the knowledge organization. Fig. 5. Query of information sources Query in Fig. 5 above to select KD under SQL KD using haspart relation. The results are used to find KO therein using hascontent relation. To obtain information sources of each KO carried data selection on DocSource property. Based on the results of the query, it can be seen several sources of information on scope of the SQL KD. If the sources of information in the format of the URL that are extracted then its domain name will be obtained including and as target search area. With the identified domain name and the declared keywords then can be used by software agents to focus the information searching. V. FURTHER RESEARCH Previous explanation has provided an overview how to find the information sources from the knowledge resources organization that will be extracted to obtain the domains location of accessing resources. The next stage required design of an agent model for information seeking that will execute it. The agent should be able to utilize the reference tracking mechanisms at the level of organizing knowledge and follow it up for navigating the searching information to certain host. Furthermore, the search results can be filtered using a method of information retrieval. On the other hand, Organizing knowledge may contain information documents which is not from the Internet, but contribute to increase users knowledge. It is also potential for further use in supporting information seeking. When the information documents that were mapped in the organizing scheme and allow to be extracted the keyword terms then it can be used to strengthen the search of other resources on the Internet based on concept of topic descriptor and discriminator 288

5 [20]. Furthermore, this can be an alternative to support information seeking with connected to the personal knowledge organization. VI. CONSLUSION Knowledge representation of the knowledge organization that has been designed not only serves to integrate the resources, but also can be read to find the information sources which stored. The hierarchical structure of the representation can be viewed as a map of information sources based on the scope of the knowledge domain. When a new knowledge domain has been defined, the query tracking mechanism can find the information sources within the same coverage domain, so that the sources potentially to be used as a reference to redirect information search on the internet based on the domain location. REFERENCES [1] Bouchard, P., 2009, Pedagogy without a teacher: What are the limits?, International Journal of Self-Directed Learning, 6(2), [2] Brambilla, M., and Tziviskou. C., 2008, "Modeling Ontology-Driven Personalization of Web Contents", Proceedings of ICWE 2008, IEEE Press, July 2008, Yorktown Heights, USA, pp [3] Mittal, N., Nayak, R., Govil, MC, Jain, KC., 2010, A Hybrid Approach of Personalized Web Information Retrieval, International Conference on Web Intelligence and Intelligent Agent Technology [4] Tao, X., Yuefeng Li, and Ning Zhong, 2011, A Personalized Ontology Model for Web Information Gathering, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 23, NO. 4, APRIL 2011 [5] Vavoula, G. and Sharples, M Lifelong Learning Organisers: Requirements for tools for supporting episodic and semantic learning. Educational Technology & Society, 12 (3), [6] Iiyoshi, T., Hannafin, M. J., Wang, F., 2005, Cognitive tools and student-centred learning: rethinking tools, functions and applications, Educational Media International, Vol. 42, No. 4, pp [7] Obitko, M., and Maˇr ik, V., 2003, Adding OWL Semantics to Ontologies Used in Multi-agent Systems for Manufacturing, Lecture Notes in Computer Science, 2003, Volume 2744, [8] Koutsantonis, D, Panayiotopoulos, J.-C., 2011, Expert system personalized knowledge retrieval, operational research, Volume 11, Number 2, Springer-Verlag [9] Istiadi, L. E. Nugroho, T. B. Adji, 2012 An Ontology Model of Knowledge Representation for Organizing Knowledge Resources, Conference on Information Technology and Electrical Engineering 2012, Yogyakarta [10] Hölscher, C., and Strube, G., 2000, Web search behavior of Internet experts and newbies, Computer networks, 33 (1), pp [11] Russell, S. and Norvig, P., 2003, Artificial Intelligence: A Modern Approach, Prentice-Hall. [12] Obitko, M., Smid, J., & Snasel, V. (2004, September). Ontology Design with Formal Concept Analysis. In CLA (Vol. 110). [13] ANSI/NISO Z39.19, 2005, Guidelines for the Construction, Format, and Management of Monolingual Controlled Vocabularies, American National Standards Institute [14] DCMI-terms, 2012, DCMI Metadata Terms, available from [15] McGuinness, D. L., & Van Harmelen, F. (2004). OWL web ontology language overview. W3C recommendation, 10(10), [16] M. Horridge, 2011, A Practical Guide To Building OWL Ontologies Using Protege 4 and CO-ODE Tools Edition 1.3, The University Of Manchester [17] Istiadi, Azhari, 2012, An Ontology Model for Organizing Information Resources Sharing on Personal Web, in The Fifth International Symposium on Computational Science (ISCS) 2012 [18] Ietf, R. F. C. (1999). 2616, Hypertext Transfer Protocol HTTP/1.1. URL rfc. net/rfc2616. html. [19] Horrocks, I., Patel-Schneider, P. F., Boley, H., Tabet, S., Grosof, B., & Dean, M., 2004, SWRL: A semantic web rule language combining OWL and RuleML.W3C Member submission, 21, 79. [20] Lorenzetti, C., Sagui, F., Maguitman, A., Simari, G., Chesñevar C., 2006, Incremental Methods for Context-Based Web Retrieval. XII Congreso Argentina de Ciencias de la Computación (CACIC), Argentina, Oktober