CONNER: A Concurrent ILP Learner in Description Logic

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 sped 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.
Original languageEnglish
Title of host publicationInductive Logic Programming
Subtitle of host publication29th International Conference, ILP 2019
Number of pages15
ISBN (Print)9783030492090
Publication statusPublished - 2020

Publication series

ISSN (Print)0302-9743

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.


  • Inductive logic programming
  • description logic
  • ontologies
  • parallel computing

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