基于本体概念群组划分的语义距离计算方法

概念的语义相似度计算是自然语言处理等领域的重要研究内容，基于语义距离的概念相似度计算是其主要方法。在分析现有算法存在弊端的基础上，提出基于领域本体群组划分的概念语义距离计算方法。首先给出多概念群组下概念语义距离的计算规则，然后分别提出群组内和群组间的概念语义距离计算方法，通过引入正向和反向的语义距离来解决上下位关系概念对的语义相似度非对称性，并通过概念节点的位置动态分配关系的权值来处理其他非上下位的二元关系。实验表明，基于领域本体群组划分的概念语义距离计算方法是有效的，与其他典型的同类方法相比，具有明显的优势。

An Ontology Concept-Based Cluster Partition Approach for Computing the Semantic Distance between Concepts

The semantic similarity computing between concepts is an important component in natural language processing etc., and the semantic similarity computing between concepts based on semantic distance is currently dominant technique. In this paper, the ontology based cluster partition approach for computing the semantic distance between concepts is proposed on the basis of the analysis of the lacks in the existing algorithms. The rules for computing the semantic distance between concepts are given under the situation of multi concept clusters, and then the approach for computing the semantic distance between concepts within single cluster as well as cross cluster is put forward. In the proposed approach, the nonsymmetry of semantic similarities in the pairs of hyponymy concepts is worked out by introducing the forward semantic distance and the reverse semantic distance, and the other binary relationships of the pairs of non hyponymy concepts are deal with by dynamically allocating the relation weights in the light of the locations of concept nodes. Experimented results shows that the proposed approach is effective and it is preferable to other typical similar ones.