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Improving the Quality of Linked Data Using Statistical Distributions

Author

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  • Heiko Paulheim

    (Data and Web Science Group, University of Mannheim, Mannheim, Germany)

  • Christian Bizer

    (Data and Web Science Group, University of Mannheim, Mannheim, Germany)

Abstract

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

Suggested Citation

  • Heiko Paulheim & Christian Bizer, 2014. "Improving the Quality of Linked Data Using Statistical Distributions," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 10(2), pages 63-86, April.
    • Handle: RePEc:igg:jswis0:v:10:y:2014:i:2:p:63-86
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    Cited by:

    1. Bogdan Vasile Ileanu & Marcel Ausloos & Claudiu Herteliu & Marian Pompiliu Cristescu, 2019. "Intriguing behavior when testing the impact of quotation marks usage in Google search results," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2507-2519, September.

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