A Semantic Vector Retrieval Model for Desktop Documents
Author(s)
Sheng Li
ABSTRACT
The paper provides a semantic vector retrieval model for desktop documents based on the ontology. Comparing with traditional vector space model, the semantic model using semantic and ontology technology to solve several problems that traditional model could not overcome such as the shortcomings of weight computing based on statistical method, the expression of semantic relations between different keywords, the description of document semantic vectors and the similarity calculating, etc. Finally, the experimental results show that the retrieval ability of our new model has significant improvement both on recall and precision.
The paper provides a semantic vector retrieval model for desktop documents based on the ontology. Comparing with traditional vector space model, the semantic model using semantic and ontology technology to solve several problems that traditional model could not overcome such as the shortcomings of weight computing based on statistical method, the expression of semantic relations between different keywords, the description of document semantic vectors and the similarity calculating, etc. Finally, the experimental results show that the retrieval ability of our new model has significant improvement both on recall and precision.
Cite this paper
nullS. Li, "A Semantic Vector Retrieval Model for Desktop Documents," Journal of Software Engineering and Applications, Vol. 2 No. 1, 2009, pp. 55-59. doi: 10.4236/jsea.2009.21009.
nullS. Li, "A Semantic Vector Retrieval Model for Desktop Documents," Journal of Software Engineering and Applications, Vol. 2 No. 1, 2009, pp. 55-59. doi: 10.4236/jsea.2009.21009.
References
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[1] B. Lee, Hendler, and Lassila, “The semantic web,” Scientific American, Vol. 34, pp. 34-43, 2001. [2] S. Decker and M. Frank, “The social semantic desktop,” WWW 2004 Workshop Application Design, Development and Implementation Issues in the Semantic Web, 2004. [3] I. R. Silva, J. N. Souza, and K. S. Santos, “Dependence among terms in vector space model,” Database Enginee- ring and Applications Symposium, pp. 97-102, 2004. [4] G. A. Millet, “Wordnet: An electronic lexical database,” Communications of the ACM, 38(11): pp. 39-41, 1995. [5] G. Asian and D. McLeod, “Semantic heterogeneity resolution in federated database by metadata implantation and stepwise evolution,” The VLDB Journal, the Interna-tional Journal on Very Large Databases, Vol. 18, pp. 22-31, 1999. [6] ACM Topic: http://www.acm.org/class/. [7] B. Aleman-Meza, F. Hakimpour, I. B. Arpinar, and A. P. Sheth, “SwetoDblp ontology of Computer Science publications,” Web Semantics: Science, Services and Agents on the World, pp. 151-155, 2007.