semantic information retrieval model by spectral clustering
Annie Jones,Senduru srinivaslu
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:2 Issue:2 Year: 08 March,2014 Pages:99-105
Abstract
The Web which is increasing day by day has huge volume of unstructured data, with several aims, qualities and aspects which makes retrieval a tedious task. Semantic web which extend our current web has a focus to retrieve the data more precisely with vocabularies. These vocabularies are understood by the people and computer. Ontology, the core concept of semantic web which explodes data from knowledge base consists of instance of classes. This paper proposes method on how retrieval of information semantically can be done from heterogeneous data store. Here clustering methodology is used to match both ontology and the user query. The algorithm will navigate into the deep roots of the ontology structure and group the similar nodes with the query. The collected similar data are stored in a buffer area to produce an optimized output. The clustering of the data is done semantically to achieve higher relevancy. The spectral clustering algorithm which is used to achieve clustering semantically will locate the sparsely located data and match them efficiently. The basic idea is to collate the web not only to link the large heterogeneous documents but also to instruct meaning of the information in those documents.
Kewords
Semantic application, Heterogeneous, Ontology, Clustering
Reference
[1] Marek Nekvasil a, Vojtěch Svátek b,∗a Adastra.Towards savvy adoption of semantic technology: From published usecases to category-specific adopter readiness models Business Consulting, Karolinská 654/2, 186 00, Praha 8 - Karlín, Czech Republic b University of Economics, Prague, Nám. W. Churchilla 4, 130 67, Praha 3, Czech Republic. [2] Miriam Fernán dez1, Iván Cantador2, Vanesa López1, David Vallet2, Pablo Castells2, Enrico Motta Semantically enhanced Information Retrieval: an ontology-based approach Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom 2 Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Madrid, Spain [3] Guha, R. V., McCool, R., & Miller, E. (2003). Semantic search. In Proceedings of the 12th International World Wide Web Conference (WWW 2003), pp. 700-709. Budapest, Hungary. [4] Seaborne, A. (2004). RDQL – A Query Language for RDF. W3CMember Submission. [5] Prud'hommeaux, E., & Seaborne, A. (2006). SPARQL Query Language for RDF. W3C Working Draft. Sabou, M., Gracia, J, Angeletou, S., d'Aquin, M., & Motta, E.,(2007). Evaluating the Semantic Web: A Task-based Approach. In Proceedings of the 6th International Semantic Web Conference (ISWC 2007), pp. 423-437. Busan, South Korea. [6] Ulrike von Luxburg.A Tutorial on Spectral Clustering Kaushal Giri, Role of Ontology in Semantic Web DESIDOC Journal of Library & Information Technology, Vol. 31, No. 2, March 2011, pp. 116-120
