CROSS-LANGUAGE KNOWLEDGE SHARING MODEL BASED ON ONTOLOGIES AND LOGICAL INFERENCE

Vast amounts of new knowledge are created on the Internet in many different languages every day. How to share and search this knowledge across different languages efficiently is a critical problem for information science and knowledge management. Conventional cross-language knowledge sharing models are based on natural language processing (NLP) technologies. However, natural language ambiguity, which is a problem even for single language NLP, is exacerbated when dealing with multiple languages. Semantic web technologies can circumvent the problem of natural language ambiguity by enabling human authors to specify meaning in a computer-interpretable form. In particular, description logics ontologies provide a way for authors to describe specific relationships between conceptual entities in a way that computers can process to infer implied meaning. This paper presents a new cross-language knowledge sharing model, SEMCL, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. We first describe the methods used to support searches at the semantic predicate level in our model. Next, we describe how our model realizes a cross-language approach. We present an implementation of the model for the general engineering domain and give a scenario describing how the model implementation handles semantic cross-language knowledge sharing. We conclude with a discussion of related work.