SEMCL: A Cross-Language Semantic Model for Knowledge Sharing

  • Authors:

  • Weisen Guo;Steven B. Kraines

  • Affiliations:

  • University of Tokyo, Japan;University of Tokyo, Japan

  • Venue:
  • International Journal of Knowledge and Systems Science
  • Year:
  • 2010

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Abstract

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing NLP technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model SEMCL for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.