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Reseach Article

INTEGRATION OF SEMANTIC WEB AND KNOWLEDGE DISCOVERY FOR ENHANCED INFORMATION RETRIVEL

by S.Kalarani, G.V.Uma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 1
Year of Publication: 2010
Authors: S.Kalarani, G.V.Uma
10.5120/12-114

S.Kalarani, G.V.Uma . INTEGRATION OF SEMANTIC WEB AND KNOWLEDGE DISCOVERY FOR ENHANCED INFORMATION RETRIVEL. International Journal of Computer Applications. 1, 1 ( February 2010), 99-103. DOI=10.5120/12-114

@article{ 10.5120/12-114,
author = { S.Kalarani, G.V.Uma },
title = { INTEGRATION OF SEMANTIC WEB AND KNOWLEDGE DISCOVERY FOR ENHANCED INFORMATION RETRIVEL },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 1 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 99-103 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number1/12-114/ },
doi = { 10.5120/12-114 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:43:42.479259+05:30
%A S.Kalarani
%A G.V.Uma
%T INTEGRATION OF SEMANTIC WEB AND KNOWLEDGE DISCOVERY FOR ENHANCED INFORMATION RETRIVEL
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 1
%P 99-103
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Knowledge management is a process which comprises knowledge discovery, knowledge collection , knowledge organization and knowledge process. Among these four process knowledge discovery is integrated with semantic web for enhanced information retrivel. Knowledge discovery is the process of automatically searching large volume of data for patterns that can be considered knowledge about the data. This is described as deriving knowledge from the input data. Knowledge discovery is defined as "the non-trivial extraction of implicit, unknown, and potentially useful information from the data". Knowledge discovery is one of the key component of knowledge management system. Today's world wide web has large volume of data - billions of document. So it is a time consuming process to discover effective knowledge from the input data. Here define knowledge discovery meta model (KDM) which defines an ontology for the software and their relationships for the purpose of performing knowledge discovery of existing data. Although search engine technology has improved in recent years, there are still many types of searches that return unsatisfactory results. This situation can be greatly improved if web pages use a semantic markup language to describe their content ,this paper describe SHOE a set of simple HTML ontology Extensions. SHOE allows World-Wide Web authors to annotate their pages with ontology-based knowledge about page contents. This paper contains an examples showing how the use of SHOE can support a new generation of knowledge-based search and knowledge discovery tools that operate on the World-Wide Web. Identifying patterns as the process of knowledge discovery an University ontology is taken as case study.

References
  1. Guarino, N. and Giaretta, P. 1995. Ontologies and Knowledge Bases: Towards a Terminological Clarification. In N. Mars (ed.) Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, 25-32. Amsterdam: IOS Press.
  2. Heflin, J.; Hendler, J.; and Luke, S. 1999. SHOE: A Knowledge Representation Language for Internet Applications, Technical Report, CS-TR-4078 (UMIACS TR-99-71), Dept. of Computer Science, University of Maryland
  3. Noy, N. and Hafner, C. 1997. The State of the Art in Ontology Design. AI Magazine18(3):53-74.
  4. Bray, T., J. Paoli and C.M. Sperberg-McQueen. 1998. Extensible Markup Language (XML).W3C(At http://www.w3.org/TR/1998/REC-xml-19980210.html)
  5. Heflin, J., and Hendler, J. 2000. Dynamic Ontologies on the Web. In Proceedings of American Association for Artificial Intelligence Conference (AAAI-2000). Menlo Park, Calif.: AAAI Press.
  6. Heflin, J., Hendler, J., and Luke, S. 1999. Applying Ontology to the Web: A Case Study. In: J. Mira, J. Sanchez-Andres (Eds.), International Work-Conference on Artificial and Natural Neural Networks, IWANN'99.Proceedings, Volume II. 715-724. Berlin: Springer.
  7. Stoffel K., Taylor, M., and Hendler, J. 1997. Efficient Management of Very Large Ontologies. In Proceedings of American Association for Artificial Intelligence
  8. ConfEvett, M.P., W.A. Andersen, and J.A. Hendler. 1993. Providing Computational Effective Knowledge Representation via Massive Parallelism. In Parallel Processing for Artificial Intelligence. L. Kanal, V. Kumar, H.
  9. Kitano, and C. Suttner, Eds. Amsterdam: Elsevier Science Publishers. URL: http://www.cs.umd.edu/projects/plus/Parka/parka-kanal.ps See also: http://www.cs.umd.edu/projects/
Index Terms

Computer Science
Information Sciences

Keywords

Knowledge discovery Knowledge discovery meta model SHOE