Abstract
Internet search engines are indispensable tools that assist users to find information on the World Wide Web (WWW). These search engines use different keyword-based indexing techniques to index Web Pages. Although this approach assist users in finding information on the Web, many of the returned results are irrelevant to the user’s information needs. This is because of the “semantic-gap” between the meanings of the keywords that are used to index Web Pages and the meanings of the terms used by the user to formulate his query. In this paper, we introduce an approach to employ knowledge represented by multiple large-scale general-purpose ontologies to derive the semantic aspects of the user’s query. In addition, we utilize statistical-based semantic relatedness measures to compensate for missing background knowledge in the exploited ontologies. Experimental instantiation of the proposed system validates our proposal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tanaka, K., et al.: Improving Search and Information Creditability Analysis from Interaction between Web1.0 and Web2.0 Content. Journal of Software 5(2), 154–159 (2010)
Royo, J., Mena, E., Bernard, J., Illarramendi, A.: Searching the web: From keywords to semantic queries. In: Proceedings of the Third International Conference on Information Technology and Applications (ICITA 2005), pp. 244–249. IEEE Computer Society Press, CA (2005)
Lei, Y., Uren, V., Motta, E.: Semsearch: A search engine for the semantic web. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 238–245. Springer, Heidelberg (2006)
Tran, T., Cimiano, P., Rudolph, S., Studer, R.: Ontology-Based Interpretation of Keywords for Semantic Search. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 523–536. Springer, Heidelberg (2007)
Wimalasuriya, D., Dou, D.: Using Multiple Ontologies in Information Extraction. In: CIKM 2009, Hong Kong, China, pp. 235–244 (2009)
Cilibrasi, R., Vitanyi, P.: The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)
Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM, 409–409 (1995)
Matuszek, C., Cabral, J., Witbrock, M., DeOliveira, J.: An Introduction to the Syntax and Content of Cyc. In: AAAI Spring Symposium (2006)
Maree, M., Belkhatir, M.: A Coupled Statistical/Semantic Framework for Merging Heterogeneous Domain-Specific Ontologies. In: The 22nd International Conference on Tools with Artificial Intelligence (ICTAI 2010), Arras, France, pp. 159–166 (2010)
Fabian, M.S., Gjergji, K., Gerhard, W.: YAGO: A Core of Semantic Knowledge Unifying WordNet and Wikipedia. In: 16th International World Wide Web Conference, WWW, pp. 697–706 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Maree, M., Alhashmi, S.M., Belkhatir, M. (2011). QuerySem: Deriving Query Semantics Based on Multiple Ontologies. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23535-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-23535-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23534-4
Online ISBN: 978-3-642-23535-1
eBook Packages: Computer ScienceComputer Science (R0)