The Chinese Conceptual Graph Matching Algorithm Based on Conceptual Sub-graph Weight Self-Adjustment
Hui Zeng, Liyan Xiong, Jianjun Chen · 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing · 2014
Semantic computing is an important task in the research of natural language processing. For the problem of the inaccurate conceptual graph matching, this paper proposed the algorithm based on Conceptual sub-Graph weight self-adjustment. Based on the in tensional logic model of Chinese concept connotation, using Recursive Conceptual Graph as knowledge representation method and combining with the computation method of E-A-V structures similarity, the algorithm computed the similarity of conceptual graphs. When using this algorithm to compute the Conceptual Graph similarity, it can give the homologous weight to the sub graph based on the proportion of how much information the sub graph contains in the whole Conceptual Graph. The experiment results show that this new algorithm achieve better results.