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Induction of Logic Programs Based on ψ-Terms

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Book cover Algorithmic Learning Theory (ALT 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1720))

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Abstract

This paper extends the traditional inductive logic programming (ILP) framework to a ψ-term capable ILP framework. Aït-Kaci’s ψ-terms have interesting and significant properties for markedly widening applicable areas of ILP. For example, ψ-terms allow partial descriptions of information, generalization and specialization of sorts (or types) placed instead of function symbols, and abstract descriptions of data using sorts; they have comparable representation power to feature structures used in natural language processing. We have developed an algorithm that learns logic programs based on -terms, made possible by a bottom-up approach employing the least general generalization (lgg) extended for ψ-terms. As an area of application, we have selected information extraction (IE) tasks in which sort information is crucial in deciding the generality of IE rules. Experiments were conducted on a set of test examples and background knowledge consisting of case frames of newspaper articles. The results showed high precision and recall rates for learned rules for the IE tasks.

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Author information

Authors and Affiliations

  1. NTT Communication Science Laboratories, 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan

    Yutaka Sasaki

Authors
  1. Yutaka Sasaki
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Editor information

Editors and Affiliations

  1. Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo, 152-8552, Japan

    Osamu Watanabe

  2. Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo, 169-8050, Japan

    Takashi Yokomori

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© 1999 Springer-Verlag Berlin Heidelberg

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Sasaki, Y. (1999). Induction of Logic Programs Based on ψ-Terms. In: Watanabe, O., Yokomori, T. (eds) Algorithmic Learning Theory. ALT 1999. Lecture Notes in Computer Science(), vol 1720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46769-6_14

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  • DOI: https://doi.org/10.1007/3-540-46769-6_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66748-3

  • Online ISBN: 978-3-540-46769-4

  • eBook Packages: Springer Book Archive

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