GARUM: A Semantic Similarity Measure Based on Machine Learning and Entity Characteristics

Abstract

Knowledge graphs encode semantics that describes entities in terms of several characteristics, e.g., attributes, neighbors, class hierarchies, or association degrees. Several data-driven tasks, e.g., ranking, clustering, or link discovery, require for determining the relatedness between knowledge graph entities. However, state-of-the-art similarity measures may not consider all the characteristics of an entity to determine entity relatedness. We address the problem of similarity assessment between knowledge graph entities and devise GARUM, a semantic similarity measure for knowledge graphs. GARUM relies on similarities of entity characteristics and computes similarity values considering simultaneously several entity characteristics. This combination can be manually or automatically defined with the help of a machine learning approach. We empirically evaluate the accuracy of GARUM on knowledge graphs from different domains, e.g., networks of proteins and media news. In the experimental study, GARUM exhibits higher correlation with gold standards than studied existing approaches. Thus, these results suggest that similarity measures should not consider entity characteristics in isolation; contrary, combinations of these characteristics are required to precisely determine relatedness among entities in a knowledge graph. Further, the combination functions found by a machine learning approach outperform the results obtained by the manually defined aggregation functions.