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A Comparison of Information Retrieval Models using Domain Ontology applied to the Digital Libraries of India. Dr. Sanghamitra Dalbehera Librarian, Institute of Technical Education & Research(I.T.E.R ) S.O.A University, Bhubaneswar Odisha sanghamitra348@gmail.com. CONTENTS. Introduction
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A Comparison of Information Retrieval Models using DomainOntology applied to the Digital Libraries of India Dr. SanghamitraDalbeheraLibrarian, Institute of Technical Education & Research(I.T.E.R)S.O.A University, BhubaneswarOdisha sanghamitra348@gmail.com
CONTENTS • Introduction • Need of Information Retrieval System Using Domain Based Ontology • Objectives of the Study • IR Models Used • Scope & Methodology • Results of the Analysis • Conclusion • References
INTRODUCTION • Ontology defines the basic terms and relations comprising the vocabulary of a topic area as well as the rules for combining terms and relations to define extensions to the vocabulary.” • Ontology is a model of a domain, O ={C, R, A˚}, Where C is a concept R C C X C, Where R is relations. For r= (C1, C2 )ϵ R. Or R(C1)= (C2) A˚ is a set of axioms on O Whereas, Concepts: set of entities within a domain. Relations: interactions between concepts in the domain. Axioms: explicit rules to constrain the use of concepts. Instances:concrete examples of concepts in the domain
Need for information retrieval Model Based on Domain Ontology • It cannot give an exact expression: users cannot express the retrieval contents exactly simply using keyword or keyword strings. • There are misunderstandings between expressions: natural language of human beings take different forms with changing of time and zones. • Information islet: concepts in brains are interknitted, and results searched by users cannot expand in concept. It is independent to knowledge concept system of human beings. CHARACTERISTICS OF IR MODELS • Management:-This function is the most important function of an ontology library system which facilitate the re-use of knowledge (ontologies).the important aspects of management of ontology library system are storage, identification and versioning support. • Adaptation:- Ontology library systems should facilitate the task of extending and updating ontologies. They should provide user-friendly environments for searching, editing and reasoning ontologies. Important aspects in an ontology library system include support in finding and modifying existing ontologies.the main aspects of adaptation are searching,editing and reasoning. • Standardization:-providing access to upper-layer ontologies such as: Upper Cyc Ontology, SENSUS and IEEE upper-layer ontology and standard representation languages such as: RDFs, XMLs or DAML+OIL are the keys to developing knowledge sharing and re-use to its full potential.
OBJECTIVES • To know about the approach to the distributed construction of a knowledge base, by a community working in a changing domain, holding potentially conflicting views about the structure of their domain; • To know the adaptation of a knowledge-based web server, Java ontology management tools and underlying knowledge modelling language to implement the digital library server. • To reuse of the domain and expert knowledge • To make the domain assumptions explicit • To increase interoperability among various domains • To increase the scalability
DAML Ontology library system • DARPA Agent Markup Language (DAML) • The DAML ontology library system is client/server-based. The structure of the stored ontology in this library system includes: ontology uri: ontology id; description; keyword; poc (point of contract): name, organization,email; submitter: name, organization, email; dmoz(open directory category); funder; properties (properties names); and namespaces
IEEE Standard Upper Ontology (IEEE) • IEEE Standard Upper Ontology (IEEE) library system is very simple and is accessible in its preliminary form on their website. • They provide user-friendly environments for searching, editing and reasoning ontologies. • Classifying ontologiesemphasizes the library system function of reorganizing ontologies; and • The modularity structure of an ontology library system can facilitate the process of re-use, mapping and integration; it guarantees proficient ontology re-use.
WebOnto • The WebOnto’s ontology library system is client/server and graphically based. It stores an ontology as a module with a unique name for identification. • It supports asynchronous and synchronous ontology editing. Ontology searching is limited to ontology navigating or browsing. The ontology is represented by OCML, which can support rule-based reasoning. • Ontologies are divided into small units & stored in a specific Module containing name, type, and the names of class parents. This system can draw graphical representations of ontologies based on the modularity storage.
ScholOnto • The ScholOnto provides a relatively simple set of argumentation links to make it as easy as possible to add an argumentation link to a concept or claim; more elaborate schemes can be introduced as there is the demand. • The domain targetted by ScholOnto is the relatively consistent way in which researchers in a community present and contest new claims in a literature . • A claim is formally defined as a relation between an agent, who makes a legal-scholarly-assertion, with some justification. • A legal-scholarly-assertion is a statement instantiating a scholarly-relationship (e.g. addresses, predicts, refutes) between two elements (e.g. methodology X addresses problem Y). The justification may be free text supporting a claim, but more substantively, either a document (which may have its own associated conceptual structure) or a specially created structure to serve as backing.
OntoDoc • OntoDoc uses a reference ontology to represent a conceptual model of the digital library domain, distinguishing between text, image and graph regions of a document, providing attribute relations for them, like size, orientation, color. • Analysis phase • Indexing phase • Typing free text or through composition of semantic expressions.
Components of JeromeDL • Resource management: Each resource is described by the semantic descriptions according to the JeromeDL core ontology. Additionally a fulltext index of the resource’s content and MARC21, and BibTEX bibliographic descriptions are provided. Each user is able to add resources via a web interface. To satisfy the quality of delivered content, each resource uploaded through the web interface has to be approved for publication. • Retrieval features:JeromeDL provides searching and browsing features based on Semantic Web data. • User profile management: In order to provide additional semantical description of resources, scalable user management based on FOAF is utilized. • Communication link: Communication with an outside world is enabled by searching in a network of digital libraries. The content of the JeromeDL database can be searched not only through the web pages of the digital library but also from the other digital libraries and other web applications.
CASE-BASED REASONING CYCLE retrieve New Cases New Cases Retrieve Cases reused Previous cases database Solved cases Learned cases revise Repaired cases retain Proposed Solution Confirmed Solution
Case-Based Reasoning(CBR) • The main idea under CBR consists in storing experiences as cases and problem-solving processes as instances of cases. When a new problem is encountered, the system uses the relevant past stored cases to interpret or to solve it . • to use the previously discovered ontology-aided semantic metadata representation in OWL, and to ask the user about resource characteristics and to respond to queries with ranked cases. • At each step, a ranked set of recommended queries related to previous similar cases is provided. After the case selection, new documents are imported and classified by using the relevance Feedback .
CONCLUSION • Increasing problem solving efficiency, the problem solving process does not start from the blank. The appropriate solution is taken to derive the current problem. • A similar new problem will recall the store solution of the similar problem. • In Rule-Based reasoning, rules will be uncompleted when principle of domain is not well understood and the formulations of complex rules of some domains are difficult. But CBR allows problem solving informally, in such domain as cases capture associations between situations, solutions and outcomes. • The solution suggested by cases may be more accurate than suggested by chains of rules . User acceptance, because the solutions are on the basis of what really happened it is likely to be more confidently accepted
References • Alani, H. (2006). Position paper: Ontology construction from online ontologies. Proceedings of the 15th International Conference on World Wide Web (WWW '06): 491-495. • Benslimane, S. M., Benslimane, D., and Malki, M. (2006). Acquiring OWL ontologies from data-intensive Web sites. Proceedings of the 6th International Conference on Web Engineering (ICWE '06): 361-368. • Berners-Lee, T. (2007). Testimony before the United States House of Representatives, Committee on Energy and Commerce, Subcommittee on Telecommunications and the Internet. Retrieved April 30, 2007, from • Cimiano, P.(2006). Ontology Learning from Text: Algorithms, Evaluation and Applications; Springer Science and Business Media,p-20. • Corcho, O. And Gomez-perez, A. (2000).A Road Map on Ontology Specification languages. In Workshop on Applications of Ontologies and Problem solving methods, 14th European Conference on Artificial Intelligence (ECAI’00), Berlin: Germany, August 20-25. • Ding, Y. and Foo, S. (2001). Ontology research and development. Part 1 – A review of ontology generation. Journal of Information Science 28 (2): 123-136. • Ding, Y. and Foo, S. (2002). Ontology research and development. Part 2 – A review of ontology mapping and evolving. Journal of Information Science 28 (5): 375-388. • Daconta, M. C., Obrst, L. J. And Smith, K.T. (2003)The Semantic Web: A Guide to the Future of XML, Web services and Knowledge Management. Indianapolis • Guarino, N. & Welty, C. (2001).Supporting Ontological Analysis of Taxonomic Relationships. Data & Knowledge Engineering. 2001, 39 (1), pp. 51-74. • Gahleitner, E., Behrendt, W., Palkoska, J. and Weippl, E. (2005). On cooperatively creating dynamic ontologies. Proceedings of the Sixteenth ACM Conference on Hypertext and Hypermedia (HT '05): 208-210. • Jones, D., Bench-capon, T. and Visser, P.(1998) Methodology for Ontology Development..NY:Chapman-Hall, 1998. pp, 20-35. • Jacob, E. K. (2003). Ontologies and the Semantic Web. Bulletin of the American Society for Information Science and Technology, 2003, 29 (4). • Khoo, C. S. G. and Aa, J. C. .(2006). Semantic Relations in Information Science. Annual Review of Information Science and Technology. , 40: 157-229. • Robu, I., Robu, V., and Thirion, B. (2006). An introduction to the Semantic Web for health sciences librarians. Journal of Medical Library Association 94 (2): 198-205. • Taniar, D., and Rahayu, J. W. (2006). Web Semantics and Ontology. Hershey, PA: Idea Group Pub.
