A Distance-Based Approach for Semantic Dissimilarity in Knowledge Graphs

Abstract

In this paper, we introduce a distance-based approach for measuring the semantic dissimilarity between two concepts in a knowledge graph. The proposed Normalized Semantic Web Distance (NSWD) extends the idea of the Normalized Web Distance, which is utilized to determine the dissimilarity between two textural terms, and utilizes additional semantic properties of nodes in a knowledge graph. We evaluate our proposal on the knowledge graph Freebase, where the NSWD achieves a correlation of up to 0.58 with human similarity assessments on the established Miller-Charles benchmark of 30 term-pairs. These preliminary results indicate that the proposed NSWD is a promising approach for assessing semantic dissimilarity in very large knowledge graphs.