Semantic-distance based evaluation of ranking queries over relational databases

  • Authors:

  • Liang Zhu;Qin Ma;Chunnian Liu;Guojun Mao;Wenzhu Yang

  • Affiliations:

  • Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and Computer Science, Hebei University, Hebei, China 071002;Department of Foreign Language Teaching and Research, Hebei University, Hebei, China 071002;College of Computer Science and Technology, Beijing University of Technology, Beijing, China 100124;College of Computer Science and Technology, Beijing University of Technology, Beijing, China 100124;Key Laboratory of Machine Learning and Computational Intelligence, School of Mathematics and Computer Science, Hebei University, Hebei, China 071002

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2010

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Abstract

Traditional database search uses pattern match in the comparison process. For a query with some search words, tuples are selected only if the words of the tuples exactly match the query words. In this paper, we propose a new method for evaluating relational ranking queries (or top-N queries) with text attributes. This method defines semantic distance functions and utilizes semantic match between words in database search. The attempt is that tuples, not only exactly matching, but also close to the query according to semantic distances, can both be fetched. The basic idea of the method is to create an index based on WordNet to expand the tuple words semantically. The candidate results for a query are retrieved by the index and a simple SQL selection statement, and then top-N answers are obtained. Extensive experiments are carried out to measure the performance of this new strategy for the evaluation of ranking queries over relational databases.