Revista UIS Ingenierías

Vol. 11 No. 1 (2012): Revista UIS Ingenierías
Articles

Semantic expansion of queries for web search (MSEC)

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  • Miguel Angel Niño-Zambrano,
  • Iván Darío López-Gómez,
  • Carlos Adrian Andrade,
  • Carlos Alberto Cobos-Lozada,
  • Ramon Fabregat-Gesa
Miguel Angel Niño-Zambrano
Universidad del Cauca
Bio
Iván Darío López-Gómez
Universidad del Cauca
Bio
Carlos Adrian Andrade
Universidad del Cauca
Bio
Carlos Alberto Cobos-Lozada
Universidad del Cauca
Bio
Ramon Fabregat-Gesa
Universitat de Girona
Bio

Published 2012-06-22

Keywords

  • Web Search,
  • query expansion,
  • domain ontologies,
  • user profiles,
  • semantic similarity

How to Cite

Niño-Zambrano, M. A., López-Gómez, I. D., Andrade, C. A., Cobos-Lozada, C. A., & Fabregat-Gesa, R. (2012). Semantic expansion of queries for web search (MSEC). Revista UIS Ingenierías, 11(1), 11–20. Retrieved from https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/11-20
Copyright Notice

Abstract

Internet has become the largest repository of human knowledge, and the amount of stored information increases day by day. This increase of information affects the levels of precision reported by Web search engines regarding  documents retrieved for the user. One strategy being used to address this problem is a focus on a personalized resource recovery. Several projects currently offer semantic methods for improving the relevance of search results  through the use of ontologies, natural language processing, knowledge based systems, query specification languages, and user profile, among others. Results are generally better than for web search engines that do not use these  techniques. However, the high cost of these improvements in precision relate to use of more complex algorithms in carrying out the search and which are more wasteful of computational resources. This article describes a semantic  query expansion model called MSEC, which is based mostly on the concept of semantic similarity, starting from domain ontologies and on the use of user profile in order to customize user searches so to improve their precision. In order to evaluate the proposed model, a software prototype was created. Preliminary experimental results show an improvement compared to the traditional web search approach. Finally the model was compared against the best  state of the art semantic search engine, called GoPubMed, for the MEDLINE collection.

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