A web-based novel term similarity framework for ontology learning

  • Authors:

  • Seokkyung Chung;Jongeun Jun;Dennis McLeod

  • Affiliations:

  • Yahoo! Inc., Santa Clara, CA;Department of Computer Science, University of Southern California, Los Angeles, CA;Department of Computer Science, University of Southern California, Los Angeles, CA

  • Venue:
  • ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
  • Year:
  • 2006

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Abstract

Given that pairwise similarity computations are essential in ontology learning and data mining, we propose a similarity framework that is based on a conventional Web search engine There are two main aspects that we can benefit from utilizing a Web search engine First, we can obtain the freshest content for each term that represents the up-to-date knowledge on the term This is particularly useful for dynamic ontology management in that ontologies must evolve with time as new concepts or terms appear Second, in comparison with the approaches that use the certain amount of crawled Web documents as corpus, our method is less sensitive to the problem of data sparseness because we access as much content as possible using a search engine At the core of our proposed methodology, we present two different measures for similarity computation, a mutual information based and a feature-based metric Moreover, we show how the proposed metrics can be utilized for modifying existing ontologies Finally, we compare the extracted similarity relations with semantic similarity using WordNet Experimental results show that our method can extract topical relations between terms that are not present in conventional concept-based ontologies.