Theory completion using inverse entailment

Muggleton, SH and Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 2000, 'Theory completion using inverse entailment' , in: Inductive Logic Programming , Lecture notes in artificial intelligence (subseries of Lecture notes in computer science) (1866) , Springer, London, UK, pp. 130-146.

Preview

PDF Download (247kB) | Preview

Abstract

The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called "Theory Completion using Inverse Entailment" (TCIE) is introduced which is applicable to non-OPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contra-positives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different data-sets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the function of unknown genes within a network of metabolic pathways. On both datasets near complete recovery of performance is achieved after relearning when randomly chosen portions of background knowledge are removed. Progol5.0's running times for experiments in this paper were typically under 6 seconds on a standard laptop PC.

Item Type:	Book Section
Editors:	Cussens, J and Frisch, A
Additional Information:	Paper originally presented at the 10th International Conference, ILP 2000 London, UK, July 24–27 2000
Themes:	Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Publisher:	Springer
Refereed:	Yes
Series Name:	Lecture notes in artificial intelligence (subseries of Lecture notes in computer science)
ISBN:	03029743
Related URLs:	Author
Depositing User:	Dr Chris H. Bryant
Date Deposited:	16 Feb 2009 15:45
Last Modified:	16 Feb 2022 08:16
URI:	https://usir.salford.ac.uk/id/eprint/1762

Actions (login required)

Edit record (repository staff only)