This paper extends the traditional inductive logic programming (ILP) framework to a PSI -term capable ILP framework. Aiet-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 -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 resutls showed high precision and recall rates for learned rules for the IE tasks.
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