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Reasoning Web International Summer School

Reasoning Web 2018: Reasoning Web. Learning, Uncertainty, Streaming, and Scalability pp 142–172Cite as

Rule Induction and Reasoning over Knowledge Graphs

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11078))

Abstract

Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue. We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete data. Finally, we discuss possible extensions of classical rule induction techniques to account for unstructured resources (e.g., text) along with the structured ones.

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Notes

  1. 1.

    The superscript i stands for ideal.

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Authors and Affiliations

  1. Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany

    Daria Stepanova, Mohamed H. Gad-Elrab & Vinh Thinh Ho

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  1. Daria Stepanova
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  2. Mohamed H. Gad-Elrab
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  3. Vinh Thinh Ho
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Correspondence to Daria Stepanova .

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Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Claudia d’Amato

  2. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Martin Theobald

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Stepanova, D., Gad-Elrab, M.H., Ho, V.T. (2018). Rule Induction and Reasoning over Knowledge Graphs. In: d’Amato, C., Theobald, M. (eds) Reasoning Web. Learning, Uncertainty, Streaming, and Scalability. Reasoning Web 2018. Lecture Notes in Computer Science(), vol 11078. Springer, Cham. https://doi.org/10.1007/978-3-030-00338-8_6

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