A Chinese Term Clustering Mechanism for Generating Semantic Concepts of a News Ontology

In order to efficiently manage and use knowledge, ontology technologies are widely applied to various kinds of domain knowledge. This paper proposes a Chinese term clustering mechanism for generating semantic concepts of a news ontology. We utilize the parallel fuzzy inference mechanism to infer the conceptual resonance strength of a Chinese term pair. There are four input fuzzy variables, consisting of a Part-of-Speech (POS) fuzzy variable, Term Vocabulary (TV) fuzzy variable, Term Association (TA) fuzzy variable, and Common Term Association (CTA) fuzzy variable, and one output fuzzy variable, the Conceptual Resonance Strength (CRS), in the mechanism. In addition, the CKIP tool is used in Chinese natural language processing tasks, including POS tagging, refining tagging, and stop word filtering. The fuzzy compatibility relation approach to the semantic concept clustering is also proposed. Simulation results show that our approach can effectively cluster Chinese terms to generate the semantic concepts of a news ontology.