Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Measuring graph similarity is a primary issue for graph-related applications. Many works have been proposed on simple topology-based structural similarity measuring for networks. It is not enough for semantic-rich networks like semantic networks, semantic link networks, and event-linked networks where semantic-based structural similarity measuring is more important than topology-based structure similarity measuring. In this paper, the authors introduce a semantic-based structural similarity for the first time and then propose an approach to measure the semantic-based structural similarity between networks with the computing theory for semantic relations as the foundation. A case study in semantic link network of the scientific research is also presented to show the feasibility of the proposed approach.