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CONNER: A Concurrent ILP Learner in Description Logic

  • Eyad AlgahtaniEmail author
  • Dimitar Kazakov
Conference paper
  • 61 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11770)

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.

Keywords

Inductive logic programming Description logics Ontologies Parallel computing GPGPU 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of YorkYorkUK

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