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

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

Saisai Gong, Wei Hu, Haoxuan Li, Yuzhong Qu
Copyright: © 2018 |Volume: 14 |Issue: 1 |Pages: 40
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781522542902|DOI: 10.4018/IJSWIS.2018010102
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MLA

Gong, Saisai, et al. "Property Clustering in Linked Data: An Empirical Study and Its Application to Entity Browsing." IJSWIS vol.14, no.1 2018: pp.31-70. http://doi.org/10.4018/IJSWIS.2018010102

APA

Gong, S., Hu, W., Li, H., & Qu, Y. (2018). Property Clustering in Linked Data: An Empirical Study and Its Application to Entity Browsing. International Journal on Semantic Web and Information Systems (IJSWIS), 14(1), 31-70. http://doi.org/10.4018/IJSWIS.2018010102

Chicago

Gong, Saisai, et al. "Property Clustering in Linked Data: An Empirical Study and Its Application to Entity Browsing," International Journal on Semantic Web and Information Systems (IJSWIS) 14, no.1: 31-70. http://doi.org/10.4018/IJSWIS.2018010102

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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.

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