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Measuring Semantic-Based Structural Similarity in Multi-Relational Networks

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yunchuan Sun & Rongfang Bie & Junsheng Zhang, 2016. "Measuring Semantic-Based Structural Similarity in Multi-Relational Networks," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 12(1), pages 20-33, January.
    • Handle: RePEc:igg:jdwm00:v:12:y:2016:i:1:p:20-33
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