CONNER: A Concurrent ILP Learner in Description Logic

Eyad Algahtani; Dimitar Lubomirov Kazakov

doi:10.1007/978-3-030-49210-6_1

CONNER: A Concurrent ILP Learner in Description Logic

Eyad Algahtani, Dimitar Lubomirov Kazakov

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 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 language	English
Title of host publication	Inductive Logic Programming
Subtitle of host publication	29th International Conference, ILP 2019
Publisher	Springer
Pages	1-15
Number of pages	15
ISBN (Print)	9783030492090
DOIs	https://doi.org/10.1007/978-3-030-49210-6_1
Publication status	Published - 2020

Publication series

Name	LNAI
Publisher	Springer
Number	11770
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.

Keywords

Inductive logic programming
description logic
ontologies
parallel computing
GPGPU

Access to Document

10.1007/978-3-030-49210-6_1

ILP_2019___CONNER__CRC
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.
Accepted author manuscript, 837 KB

Cite this

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