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An Empirical Study on Property Clustering in Linked Data

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Semantic Technology (JIST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10055))

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Abstract

Properties are used to describe entities, a part of which 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, we firstly investigated the relatedness between properties using five different measures. Then, we employed three clustering algorithms and two combination methods for property clustering. Based on a moderate-sized sample of Linked Data, we empirically studied the property clustering in Linked Data and found that a proper combination of different measures gave rise to the best result. Additionally, we showed how the property clustering can improve user experience in our entity browsing system.

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Notes

  1. 1.

    http://ws.nju.edu.cn/sview.

  2. 2.

    http://km.aifb.kit.edu/projects/btc-2011/.

  3. 3.

    http://ws.nju.edu.cn/sview/propcluster.zip.

  4. 4.

    https://developers.google.com/freebase/guide/basic_concepts.

  5. 5.

    http://dbpedia.org/resource/The_Pentagon.

  6. 6.

    http://www.wikidata.org.

  7. 7.

    http://www.freebase.com.

  8. 8.

    http://tools.wmflabs.org/reasonator.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61370019). We appreciate our students’ participation in the experiments.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Saisai Gong, Haoxuan Li, Wei Hu & Yuzhong Qu

Authors
  1. Saisai Gong
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  2. Haoxuan Li
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  3. Wei Hu
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  4. Yuzhong Qu
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Corresponding author

Correspondence to Wei Hu .

Editor information

Editors and Affiliations

  1. Information Technology, Monash University, Melbourne, Victoria, Australia

    Yuan-Fang Li

  2. Computer Science and Technology, Nanjing University, Nanjing, China

    Wei Hu

  3. Computer Science, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  4. University of Huddersfield, Huddersfield, United Kingdom

    Grigoris Antoniou

  5. Information and Communication Technology, Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  6. ISTD, Singapore University of Technology and Design, Singapore, Singapore

    Jun Sun

  7. Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

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Gong, S., Li, H., Hu, W., Qu, Y. (2016). An Empirical Study on Property Clustering in Linked Data. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-50112-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50111-6

  • Online ISBN: 978-3-319-50112-3

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