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

Saisai Gong, Wei Hu, Haoxuan Li and Yuzhong Qu
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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

International Journal on Semantic Web and Information Systems (IJSWIS), 2018, vol. 14, issue 1, 31-70

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.

Date: 2018
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