%0 Journal Article %A XIE Tianyang %A CHEN Ming %A XI Xiaotao %T Research on Quantitative Evaluation of Knowledge Fusion in News Knowledge Graph %D 2022 %R 10.3778/j.issn.1002-8331.2104-0286 %J Computer Engineering and Applications %P 294-300 %V 58 %N 21 %X Most methods to judge the completeness and consistency of knowledge graph are manual methods, which are based on syntax checking and instance checking, or reasoning the logical rules in the knowledge base. But there is no specific method to judge the degree of fusion of knowledge graph. This paper presents a quantitative evaluation method based on power law, and the fusion results of same topic news knowledge graph are verified. It is found that with the increase of keyword node similarity, the degree of integration of nodes increases, the number of nodes after fusion drops sharply, the power law model fits good, and the adjusted coefficient of determination are all greater than 0.98. This test method can provide a new standard for testing the fusion degree of same topic news knowledge graph. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2104-0286