SEMCL: A Cross-Language Semantic Model for Knowledge Sharing

SEMCL: A Cross-Language Semantic Model for Knowledge Sharing

Weisen Guo, Steven B. Kraines
Copyright: © 2010 |Pages: 19
DOI: 10.4018/jkss.2010070101
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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.
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Knowledge Sharing Models

There are many kinds of knowledge in our daily life, and there are many methods for classifying that knowledge. In this paper, we focus on knowledge stored in the Internet, which is explicitly described in formats such as text, pictures, videos, and databases. The Internet environment makes it possible to share knowledge transparently across time and space. However, to make knowledge sharing and searching on the Internet more effective, we need to consider questions such as what knowledge sharing is, what the goal of the knowledge sharing is, and who participates in the knowledge sharing.

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