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CONNER: A Concurrent ILP Learner in Description Logic

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Inductive Logic Programming (ILP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11770))

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Abstract

Machine Learning (ML) approaches can achieve impressive results, but many lack transparency or have difficulties handling data of high structural complexity. The class of ML known as Inductive Logic Programming (ILP) draws on the expressivity and rigour of subsets of First Order Logic to represent both data and models. When Description Logics (DL) are used, the approach can be applied directly to knowledge represented as ontologies. ILP output is a prime candidate for explainable artificial intelligence; the expense being computational complexity. We have recently demonstrated how a critical component of ILP learners in DL, namely, cover set testing, can be speeded up through the use of concurrent processing. Here we describe the first prototype of an ILP learner in DL that benefits from this use of concurrency. The result is a fast, scalable tool that can be applied directly to large ontologies.

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Notes

  1. 1.

    Here we use the CUDA built-in atomic function atomicOR(A,B), which implements the (atomic) Boolean operation A := A OR B.

  2. 2.

    Note that this implementation returns the correct result for the following special case: if \(\not \exists IndvB: Role(IndvA,IndvB)\) then \(IndvA \in \forall Role{.}Concept\).

  3. 3.

    The dataset is available from https://osf.io/kf4h6/.

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Authors and Affiliations

  1. University of York, Heslington, York, YO10 5GH, UK

    Eyad Algahtani & Dimitar Kazakov

Authors
  1. Eyad Algahtani
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  2. Dimitar Kazakov
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Correspondence to Eyad Algahtani .

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Editors and Affiliations

  1. Department of Computer Science, University of York, Heslington, UK

    Dimitar Kazakov

  2. Department of Computer Science, University of York, Heslington, UK

    Can Erten

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Algahtani, E., Kazakov, D. (2020). CONNER: A Concurrent ILP Learner in Description Logic. In: Kazakov, D., Erten, C. (eds) Inductive Logic Programming. ILP 2019. Lecture Notes in Computer Science(), vol 11770. Springer, Cham. https://doi.org/10.1007/978-3-030-49210-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-49210-6_1

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