Novel Similarity Algorithm of Extended Topic Maps for Multi-Resource Knowledge Fusion
Xu Li · Xi'an Jiaotong Daxue xuebao · 2010
A novel similarity algorithm of extended topic map called ETMSC for multi-resource knowledge fusion is proposed to improve the drawbacks that the knowledge organization model based on metadata or traditional topic map can not represent knowledge multi-level and multi-granularity,and the low accuracy of existing similarity algorithms.Three principles of the correlation,levels corresponding,and the experimental determination in selecting threshold are presented.The algorithm combines the comprehensive information theory with the structure and semantic information of extended topic map.The syntactic matching,semantic matching,and pragmatic matching are comprehensively considered,in which not only the structural similarity of topic map elements are extended,but also the meaning and relevance in linguistic contexts are thoroughly taken into account.Topic map similarity criterions are related to a threshold,and the determination of the threshold is associated with the data sets.Experimental results and comparisons with the traditional algorithms that are purely based on the syntactic or semantic similarity show that the F-measure of ETMSC is improved by 9.2%-11.1%.