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Property Clustering in Linked Data: An Empirical Study and Its Application to Entity Browsing

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  • Saisai Gong

    (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China)

  • Wei Hu

    (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China)

  • Haoxuan Li

    (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China)

  • Yuzhong Qu

    (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China)

Abstract

Properties are used to describe entities, and a part of them are likely to be clustered together to constitute an aspect. For example, first name, middle name and last name are usually gathered to describe a person's name. However, existing automated approaches to property clustering remain far from satisfactory for an open domain like Linked Data. In this paper, the authors firstly investigated the relatedness between properties using 13 different measures. Then, they employed seven clustering algorithms and two combination methods for property clustering. Based on a sample set of Linked Data, the authors empirically studied property clustering in Linked Data and found that a proper combination of different measures and clustering algorithms gave rise to the best result. Additionally, they reported how property clustering can improve user experience in an entity browsing system.

Suggested Citation

  • Saisai Gong & Wei Hu & Haoxuan Li & Yuzhong Qu, 2018. "Property Clustering in Linked Data: An Empirical Study and Its Application to Entity Browsing," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 14(1), pages 31-70, January.
    • Handle: RePEc:igg:jswis0:v:14:y:2018:i:1:p:31-70
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