Induction of Logic Programs Based on psi-Terms

Author: Yutaka Sasaki.

Source: Lecture Notes in Artificial Intelligence Vol. 1720, 1999, 169 - 181.

Abstract. This paper extends the traditional inductive logic programming (ILP) framework to a psi-term capable ILP framework. Aït-Kaci's psi-terms have interesting and significant properties for markedly widening applicable areas of ILP. For example, psi-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 psi-terms, made possible by a bottom-up approach employing the least general generalization (lgg) extended for psi-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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