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Theory completion using inverse entailment

Muggleton, SH and Bryant, CH 2000, 'Theory completion using inverse entailment' , in: Inductive Logic Programming , Lecture notes in artificial intelligence (subseries of Lecture notes in computer science) (1866) , Springer-Verlag, London, UK, pp. 130-146.

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    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
    Uncontrolled Keywords: inductive logic programming
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Science & Technology
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Publisher: Springer-Verlag
    Refereed: Yes
    ISBN: 03029743
    Related URLs:
    Depositing User: Dr Chris H. Bryant
    Date Deposited: 16 Feb 2009 15:45
    Last Modified: 20 Aug 2013 16:56
    URI: http://usir.salford.ac.uk/id/eprint/1762

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