2025 Volume 33 Pages 429-444
Various knowledge graphs are now available, and useful knowledge can be extracted from them. Measuring the similarity between two nodes in a knowledge graph is one of the fundamental steps required for extracting knowledge from it. In this paper, we propose a novel measure for representing the similarity between two nodes: LRoleSim. We define LRoleSim as an extension of RoleSim in a deductive manner, and we do not use inductive methods, such as embedding graphs into vector spaces. This means that LRoleSim inherits the axiomatic properties of RoleSim and functions as an admissible role similarity measure. The RoleSim measure was proposed for measuring the similarity between two nodes in homogeneous information networks, where nodes and edges are being treated either as of the same type or as of un-typed nodes or edges. In contrast, every node in a knowledge graph has its own type, and every edge has a direction and type. LRoleSim is designed so that it captures all of these types and directions as well as the topological structure of each node's neighbor. Experiments on real-world knowledge graph datasets verified that LRoleSim captures the semantic meaning of node similarity. Moreover, our experiments show that LRoleSim outperforms RoleSim in terms of computational efficiency.
