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SWARM: An Approach for Mining Semantic Association Rules from Semantic Web Data

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PRICAI 2016: Trends in Artificial Intelligence (PRICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9810))

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Abstract

The ever growing amount of Semantic Web data has made it increasingly difficult to analyse the information required by the users. Association rule mining is one of the most useful techniques for discovering frequent patterns among RDF triples. In this context, some statistical methods strongly rely on the user intervention that is time-consuming and error-prone due to a large amount of data. In these studies, the rule quality factors (e.g. Support and Confidence measures) consider only knowledge in the instance-level data. However, Semantic Web data contains knowledge in both instance-level and schema-level. In this paper, we introduce an approach called SWARM (Semantic Web Association Rule Mining) to automatically mine Semantic Association Rules from RDF data. We discuss how to utilize knowledge encode in the schema-level to enrich the semantics of rules. We also show that our approach is able to reveal common behavioral patterns associated with knowledge in the instance-level and schema-level. The proposed rule quality factors (Support and Confidence) consider knowledge not only in the instance-level but also schema-level. Experiments performed on the DBpedia Dataset (3.8) demonstrate the usefulness of the proposed approach.

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Notes

  1. 1.

    http://freebase.com.

  2. 2.

    http://dbpedia.org/page/George_Harrison.

  3. 3.

    http://dbpedia.org/page/John_Lennon.

  4. 4.

    http://wiki.dbpedia.org/services-resources/datasets/data-set-38/downloads-38/.

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Molood Barati & Quan Bai

  2. Data61, CSIRO, 15 College Road, Sandy Bay, Tasmania, 7005, Australia

    Qing Liu

Authors
  1. Molood Barati
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  2. Quan Bai
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  3. Qing Liu
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Corresponding authors

Correspondence to Molood Barati or Quan Bai .

Editor information

Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

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Barati, M., Bai, Q., Liu, Q. (2016). SWARM: An Approach for Mining Semantic Association Rules from Semantic Web Data. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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