Concept vector extraction from Wikipedia category network

  • Authors:

  • Masumi Shirakawa;Kotaro Nakayama;Takahiro Hara;Shojiro Nishio

  • Affiliations:

  • Osaka Univ., Suita, Osaka, Japan;Tokyo Univ., Bunkyo-ku, Tokyo, Japan;Osaka Univ., Suita, Osaka, Japan;Osaka Univ., Suita, Osaka, Japan

  • Venue:
  • Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
  • Year:
  • 2009

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Abstract

The availability of machine readable taxonomy has been demonstrated by various applications such as document classification and information retrieval. One of the main topics of automated taxonomy extraction research is Web mining based statistical NLP and a significant number of researches have been conducted. However, existing works on automatic dictionary building have accuracy problems due to the technical limitation of statistical NLP (Natural Language Processing) and noise data on the WWW. To solve these problems, in this work, we focus on mining Wikipedia, a large scale Web encyclopedia. Wikipedia has high-quality and huge-scale articles and a category system because many users in the world have edited and refined these articles and category system daily. Using Wikipedia, the decrease of accuracy deriving from NLP can be avoided. However, affiliation relations cannot be extracted by simply descending the category system automatically since the category system in Wikipedia is not in a tree structure but a network structure. We propose concept vectorization methods which are applicable to the category network structured in Wikipedia.