Beyond ISA: Structures for Plausible Inference in Semantic Networks

Paul R. Cohen, Cynthia L. Loiselle

We present a method for automatically deriving plausible inference rules from relations in a knowledge base. We describe two empirical studies of these rules. First, we derived approximately 300 plausible inference rules, generated over 3000 specific inferences, and presented them to human subjects to discover which rules were plausible. The second study tested the hypothesis that the plausibility of these rules can be predicted by whether they obey a kind of transitivity. The paper discusses four sources of variance in subjects’ judgments, and concludes that relatively little knowledge is needed to achieve moderately accurate predictions of these judgments.


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