Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Yunchuan Sun, Rongfang Bie and Junsheng Zhang
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Yunchuan Sun: Beijing Normal University, Beijing, China
Rongfang Bie: Beijing Normal University, Beijing, China
Junsheng Zhang: IT Support Center, Institute of Scientific and Technical Information of China, Beijing, China

International Journal of Data Warehousing and Mining (IJDWM), 2016, vol. 12, issue 1, 20-33

Abstract: 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.

Date: 2016
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