Similarity of Concepts in Weighted Knowledge Graphs

Knowledge graphs are recognized as a valuable format for representing data and information. Their ability to represent semantics using different types of relations between the concepts and denoting information at different levels of abstraction creates a demand for algorithms taking advantage of such data format. In this paper, we propose a method for determining the similarity between concepts in weighted knowledge graphs. The method uses a hierarchical approach to determine the degree of similarity at different levels of ‘distance’ from the considered graph concepts. The proposed technique employs the T-norm and OWA operator. Similarities between concepts account for edge weights, while OWA aggregates similarities between nodes at different levels of distance from the compared nodes. The method is explained, and its merits are discussed.