Semantic Similarity Computation in Knowledge Graphs: Comparisons and Improvements

Abstract

Computing semantic similarity between concepts is a fundamental task in natural language processing and has a large variety of applications. In this paper, first of all, we will review and analyze existing semantic similarity computation methods in knowledge graphs. Through the analysis of these methods, we find that existing works mainly focus on the context features of concepts which indicate the position or the frequency of the concepts in the knowledge graphs, such as the depth of terms, information content of the terms, or the distance between terms, while a fundamental part to describe the meaning of the concept, the synsets of concepts, are neglected for a long term. Thus, in this paper, we propose a new method to compute the similarity of concepts based on their extended synsets. Moreover, we propose a general hybrid framework, which can combine our new similarity measure based on extended synsets with any of existing context feature based semantic similarities to evaluate the concepts more accurately. We conducted experiments on five well-known datasets for semantic similarity evaluation, and the experimental results show that our general framework can improve most of existing methods significantly.