A Semantic Information Content Based Method for Evaluating FCA Concept Similarity

Hongtao Huang, Cunliang Liang and Haizhi Ye
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Hongtao Huang: Engineering Research Center of Henan Provincial Universities for Educational Information, Henan Normal University, Xinxiang, China
Cunliang Liang: Engineering Research Center of Henan Provincial Universities for Educational Information, Henan Normal University, Xinxiang, China
Haizhi Ye: Engineering Research Center of Henan Provincial Universities for Educational Information, Henan Normal University, Xinxiang, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2018, vol. 12, issue 2, 77-93

Abstract: Probability information content-based FCA concepts similarity computation method relies on the frequency of concepts in corpus, it takes only the occurrence probability as information content metric to compute FCA concept similarity, which leads to lower accuracy. This article introduces a semantic information content-based method for FCA concept similarity evaluation, in addition to the occurrence probability, it takes the superordinate and subordinate semantic relationship of concepts to measure information content, which makes the generic and specific degree of concepts more accurate. Then the semantic information content similarity can be calculated with the help of an ISA hierarchy which is derived from the domain ontology. The difference between this method and probability information content is that the evaluation of semantic information content is independent of corpus. Furthermore, semantic information content can be used for FCA concept similarity evaluation, and the weighted bipartite graph is also utilized to help improve the efficiency of the similarity evaluation. The experimental results show that this semantic information content based FCA concept similarity computation method improves the accuracy of probabilistic information content based method effectively without loss of time performance.

Date: 2018
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