Approximate Information Filtering on the Semantic Web
- 436 Downloads
Facing the increasing amount of information available on the World Wide Web, intelligent techniques for content-based information filtering gain more and more importance. Conventional approaches using keyword- or text-based retrieval methods have been developed that perform reasonably well. However, these approaches have problems with ambiguous and imprecise information. The semantic web that aims at supplementing information sources with a formal specification of its meaning using ontologies can potentially help to overcome this problem. At the moment, however, the semantic web still suffers from its own problems in terms of heterogeneous ontologies and the need to relate them to each other. In this paper, we argue that we can overcome this problem by using shared vocabularies, a standardized language for encoding ontology that supports basic terminological reasoning (in this case DAML+OIL) and techniques from approximate reasoning. We introduce the approach on an informal level using didactic example and give a formal characterization of the method that include correctness proofs for the problem of information filtering.
KeywordsInformation Source Information Item Query Concept Local Ontology Ontology Alignment
Unable to display preview. Download preview PDF.
- 1.N.J. Belkin and B.W. Croft. Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM, 35(12):29–38, December 1992.Google Scholar
- 2.Diego Calvanese, Giuseppe De Giacomo, and Maurizio Lenzerini. A framework for ontology integration. In Proceedings of the international semantic web working symposium, Stanford, USA, 2001.Google Scholar
- 3.Diego Calvanesea, Giuseppe De Giacomo, and Maurizio Lenzerini. Description logics for information integration. In Computational Logic: From Logic Programming into the Future, Lecture Notes in Computer Science. Springer Verlag, 2001.Google Scholar
- 5.K.C.-C. Chang, H. Garcia-Molina, and A. Paepcke. Boolean query mapping across heterogeneous information sources. IEEE Transaction on Knowledge and Data Engineering, 8(4), 1996.Google Scholar
- 6.William B. Frakes and R. Baeza-Yates. Information Retrieval: Data Structures and Algorithms. Prentice-HALL, North Virginia, 1992.Google Scholar
- 7.R. Gaizauskas and K. Humphreys. Using a semantic network for information extraction. Journal of Natural Language Engineering, 1997.Google Scholar
- 8.Volker Haarslev and Ralf Moller. Description of the RACER system and its applications. In Proceedings of the Description Logics Worlshop DL-2001, Stanford, CA, 2001.Google Scholar
- 9.Michel Klein. Combining and relating ontologies: an analysis of problems and solutions. In Ontologies and information sharing, number 47, Seattle, USA, August 2001.Google Scholar
- 10.Yannis Papakonstantinou, Ashish Gupta, and Laura Haas. Capabilities-based query rewriting in mediator systems. In Proceedings of 4th International Conference on Parallel and Distributed Information Systems, Miami Beach, Flor., 1996.Google Scholar
- 11.G. Salton and M.J. McGill. Introduction to modern information retrieval. McGraw-Hill, 1983.Google Scholar
- 12.B. Selman and H. Kautz. Knowledge compilation and theory approximation. Journal of the ACM, 43(2):193–224, March 1996.Google Scholar
- 13.H. Stuckenschmidt and F. van Harmelen. Ontology-based metadata generation from semi-structured information. In Proceedings of the first intenational conference on knowledge capture (K-CAP’01). Sheridan Printing, 2001.Google Scholar
- 14.Frank van Harmelen, Peter F. Patel-Schneider, and Ian Horrocks. A model-theoretic semantics for daml+oil (march 2001). http://www.daml.org/2001/03/model-theoretic-semantics.html, march 2001.
- 15.Frank van Harmelen, Peter F. Patel-Schneider, and Ian Horrocks. Reference description of the daml+oil (march 2001) ontology markup language. http://www.daml.org/2001/03/reference.html, march 2001.
- 16.Pepjijn R. S. Visser, Dean M. Jones, T. J. M. Bench-Capon, and M. J. R. Shave. An analysis of ontological mismatches: Heterogeneity versus interoperability. In AAAI1997 Spring Symposium on Ontological Engineering, Stanford, USA, 1997.Google Scholar
- 17.David Yarowsky. Word-sense disambiguation using statistical models of Roget’s categories trained on large corpora. In Proceedings of COLING-92, pages 454–460, Nantes, France, 1992.Google Scholar