By the same authors

CONNER: A Concurrent ILP Learner in Description Logic

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

Author(s)

Department/unit(s)

Publication details

Title of host publicationInductive Logic Programming
DateAccepted/In press - 26 Jul 2019
PublisherSpringer
Original languageEnglish

Publication series

NameLNAI
PublisherSpringer
Number11770

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.

Bibliographical note

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    Research areas

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

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