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A Brief Overview of Rule Learning

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Rule Technologies: Foundations, Tools, and Applications (RuleML 2015)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9202))

Abstract

In this paper, we provide a brief summary of elementary research in rule learning. The two main research directions are descriptive rule learning, with the goal of discovering regularities that hold in parts of the given dataset, and predictive rule learning, which aims at generalizing the given dataset so that predictions on new data can be made. We briefly review key learning tasks such as association rule learning, subgroup discovery, and the covering learning algorithm, along with their most important prototypes. The paper also highlights recent work in rule learning on the Semantic Web and Linked Data as an important application area.

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Author information

Authors and Affiliations

  1. Department of Computer Science, TU Darmstadt, Hochschulstraße 10, 64289, Darmstadt, Germany

    Johannes Fürnkranz

  2. Department of Information and Knowledge Engineering, University of Economics, Prague, nám. Winstona Churchilla 4, 13067, Prague, Czech Republic

    Tomáš Kliegr

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  1. Johannes Fürnkranz
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  2. Tomáš Kliegr
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Correspondence to Tomáš Kliegr .

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Editors and Affiliations

  1. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Nick Bassiliades

  2. University of Oxford, Oxford, United Kingdom

    Georg Gottlob

  3. Imperial College London, London, United Kingdom

    Fariba Sadri

  4. Freie Universität Berlin, Berlin, Germany

    Adrian Paschke

  5. SINTEF/University of Oslo, Oslo, Norway

    Dumitru Roman

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Fürnkranz, J., Kliegr, T. (2015). A Brief Overview of Rule Learning. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds) Rule Technologies: Foundations, Tools, and Applications. RuleML 2015. Lecture Notes in Computer Science(), vol 9202. Springer, Cham. https://doi.org/10.1007/978-3-319-21542-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-21542-6_4

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