The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)

Korea Association of Information Systems (한국정보시스템학회)
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Using Ontologies for Semantic Text Mining

시맨틱 텍스트 마이닝을 위한 온톨로지 활용 방안

  • 유은지 (국민대학교 비즈니스IT전문대학원) ;
  • 김정철 (국민대학교 비즈니스IT전문대학원) ;
  • 이춘열 (국민대학교 경영정보학부) ;
  • 김남규 (국민대학교 경영정보학부)
  • Received : 2012.08.09
  • Accepted : 2012.09.06
  • Published : 2012.09.30
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Abstract

The increasing interest in big data analysis using various data mining techniques indicates that many commercial data mining tools now need to be equipped with fundamental text analysis modules. The most essential prerequisite for accurate analysis of text documents is an understanding of the exact semantics of each term in a document. The main difficulties in understanding the exact semantics of terms are mainly attributable to homonym and synonym problems, which is a traditional problem in the natural language processing field. Some major text mining tools provide a thesaurus to solve these problems, but a thesaurus cannot be used to resolve complex synonym problems. Furthermore, the use of a thesaurus is irrelevant to the issue of homonym problems and hence cannot solve them. In this paper, we propose a semantic text mining methodology that uses ontologies to improve the quality of text mining results by resolving the semantic ambiguity caused by homonym and synonym problems. We evaluate the practical applicability of the proposed methodology by performing a classification analysis to predict customer churn using real transactional data and Q&A articles from the "S" online shopping mall in Korea. The experiments revealed that the prediction model produced by our proposed semantic text mining method outperformed the model produced by traditional text mining in terms of prediction accuracy such as the response, captured response, and lift.

Keywords

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