Algahtani, Eyad and Kazakov, Dimitar Lubomirov orcid.org/0000-0002-0637-8106 (2020) CONNER:A Concurrent ILP Learner in Description Logic. In: Inductive Logic Programming:29th International Conference, ILP 2019. LNAI . Springer , pp. 1-15.
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.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | 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 |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 24 Oct 2019 09:40 |
Last Modified: | 06 Nov 2024 02:10 |
Published Version: | https://doi.org/10.1007/978-3-030-49210-6_1 |
Status: | Published |
Publisher: | Springer |
Series Name: | LNAI |
Identification Number: | 10.1007/978-3-030-49210-6_1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152616 |