The intensional semantic conceptual graph matching algorithm based on conceptual sub-graph weight self-adjustment

Abstract

Semantic computing is an important task in the research on natural language processing. On solving the problem of the inaccurate conceptual graph matching, this paper proposes an algorithm to compute the similarity of conceptual graphs, based on conceptual sub-graph weight self-adjustment. The algorithm works by basing itself on the intensional logic model of Chinese concept connotation, using intensional semantic conceptual graph as knowledge representation method and combining itself with the computation method of E-A-V structures. When computing the similarity of conceptual graphs, the algorithm can give the homologous weight to the sub-graph according to the proportion of how much information the sub-graph contains in the whole conceptual graph. Therefore, it can achieve better similarity results, which has also been proved in the experiments of this paper.