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Agent Based Semantics Using Relationships

K. Srihari, Dr. A. Chitra T. Rajan

Abstract


The semantic web initiative of the world-wide web consortium (w3c) has been active for the last few years and has attracted interest and skepticism in equal measure. Semantic search generally deals with the searching of the keyword along with the concept related to that semantic word. Perhaps the most widely developed space at the moment within the semantic web is in information management, i.e. the organization and discovery of information. This is the primary motivation behind the semantic web’s development, but people are taking a variety of approaches to developing tools to extend the current web into a true semantic web. One of the methods of implementing semantic search is by means of agents like indexing agent, query agent and ontology agent. Index agent downloads files from web and extract textual content, query agent process all queries, ontology agent extracts the descriptive concepts and calculates relation between those concepts. The main problem with these agents is the pages retrieved after searching are not relevant. The other method of implementing semantic search is by combining semantic search and ontology learning. The need for domain ontology for information retrieval to improve answer queries, However ontology in information retrieval systems requires regular updates and relationship between new concepts. Semantic search and ontology collectively improves the value of indexing documents and enriching ontology. It is done by multi layer like domain ontology,topic ontology, and natural language ontology. The main constraint here is this system is based on little domain ontology that cannot be expanded. Semantic search is also implemented by improving results with light weight semantic search. Here search queries and light weight semantics are combined together to improve search results. It is done by basic and concept scoring computation. Here indexing is done allocating resources and results are obtained by means of search engines. The main problems in this research are the inverted resources and metadata are not allocated properly for indexing. The problems with the previous implementations are time complexity, accuracy in retrieving pages, domain ontology,and resource allocation for indexing. The semantic pages are searched in the query repository based upon the algorithm for query retrieval. Then an algorithm for ontology based search is implemented. The ontology is obtained based on multi level ontology structure. Then a retrieval algorithm is designed to implement the semantic search.


Keywords


Ontology, OWL, RDF, Searching.

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References


An agent based semantic search engine for scalable enterprise applications by andrea passadore,alberto grosso, proceedings ontose 2009.

Combining semantic search and ontology learning for incremental web ontology engineering by nesrine,hajer,henda ,proceedings wism 2009.

Relevance feedback between text and semantic search by harry www 2009

Improving search results with lightweight and semantic search by marian simko, maria bielikova www2009.

A relation based page rank algorithm for semantic web search engines by fabrizio lamberti,IEEE,Andrew sanna and Claudio demartini,member IEEE transactions and knowledge and data engineering , vol 21 no1 january 2009.

Cali A. (2003)Reasoning in data integration system: why LAV and GAV are siblings. Proceedings. of the 14th International Symposium on Methodologies for Intelligent Systems (ISMIS 2003).

Calvanese, D., De Giacomo, G. & Lenzerini, M. (2001). A framework for ontology integration. Proceedings of the 1st Internationally Semantic Web Working Symposium (SWWS), 303-317.

Caraballo, S.A. (1999)Automatic construction of a hypernym-labeled noun hierarchy from text. Proceedings of the 37th Annual Meeting of the ACL.

Cimiano, P., Hotho, A. & Staab, S. (August 2004). Comparing conceptual, partitional and agglomerative clustering for learning taxonomies from text. Proceeding of ECAI-2004, Valencia.

Daconta, M., Obrst, L. & Smith, K. (2003) The Semantic Web: A Guide to the Future of XML. Web Services and Knowledge Management. Wiley.

Dean, M. & Schreiber, G. (2003) OWL Web Ontology Language:Reference’. World Wide Web Consortium.. http://www.w3.org/TR/2003/CR-owl-ref-20030818/

Decker S., Jannink J., Melnik S., Mitra P., Staab S., Studer R. & Wiederhold G., An Information Food Chain for Advanced Applications on the WWW. ECDL 2000, 490-493.

Doan A., Madhavan J., Dhamankar, R., Domingos P. & Halevy A.(2003)

Learning to match ontologies on the Semantic Web. VLDB Journal,12(4), 303-319.Euzénat, J., Remize, M. & Ochanine, H. (2003).

Projet Hi-Touch. Le Web sémantique au secours du tourisme.Archimag.Faure, D. & Nedellec, C. (1998)

A corpus-based conceptual clustering method for verb frames and ontology. Proceedings of the LREC Workshop on Adapting lexical and corpus resources to sublanguages and applications. ed., P.Verlardi.

Fensel, D., Bussler, C. & Maedche, A. (2002) Semantic Web Enabled Web Services’. International Semantic Web Conference, Italy, 1-2.

Friedman, M., Levy, A. & Millstein, T. (1999) Navigational Plans For Data Integration. Proceedings of of AAAI’99, 67–73.

Gershon, N. & Eick, S.G. (1995) Visualisation's New Tack: Making Sense of Information. IEEE Spectrum, 38-56.

Gomez-Perez, A., Fernandez-Lopez , M. & Corcho O. (2003)Ontological Engineering. Springer.Gomez-Perez, A. & Rojas, M.D.(1999)Ontological Reengineering and Reuse. 11th European Workshop on Knowledge Acquisition, Modeling and Management (EKAW’99,Germany). Lecture Notes in Artificial Intelligence LNAI 1621 Springer-Verlag, 139-156, eds., Fensel D. & Studer R.

Guarino N. (1998) Formal Ontology in Information Systems. First international conference on formal ontology in information systems, Italy, Ed. Guarino, 3-15.

Gruber, T. (August 1993).Toward principles for the design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, special issue on Formal Ontology in Conceptual Analysis and Knowledge Representation. Eds, Guarino, N.& Poli , R.

Grüninger, M. & Fox , M.S. (1995) Methodology for the design and evaluation of ontologies. IJCAI’95 Workshop on Basic Ontological Issues in Knowledge Sharing, Canada. Ed. Skuce, D.

Halevy, A.Y. (2001)Answering queries using views: A survey. The VLDB Journal, 10(4),270–294.

Hameed, A. , Preece, A. & Sleeman, D. (2004) Ontology Reconciliation. Handbook on Ontologies. Eds. Staab, S. & Studer,R.,231-250.

Harsleev V. & Möller R.. (2001) Racer system description. Proc. of the Int. Joint Conf. on automated reasoning (IJCAR 2001). Lecture Notes in Artificial Intelligence 2083, 701-705, Springer.

Heflin J. (2004) OWL Web Ontology Language Use Cases and Requirements. W3C Recommendation.WWW.W3.org.

Horrocks I. (2002)DAML+OIL: a reasonable Web ontology language.Proc. of EDBT 2002, Lecture Notes in Computer Science 2287, 2-13,Springer.


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