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2015•LiriasOpen access

Inducing probabilistic relational rules from probabilistic examples

Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke

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Abstract

We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be proba-bilistic. The setting is incorporated in the proba-bilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.

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We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be proba-bilistic. The setting is incorporated in the proba-bilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.

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Available abstract

We study the problem of inducing logic programs in a probabilistic setting, in which both the example descriptions and their classification can be proba-bilistic. The setting is incorporated in the proba-bilistic rule learner ProbFOIL+, which combines principles of the rule learner FOIL with ProbLog, a probabilistic Prolog. We illustrate the approach by applying it to the knowledge base of NELL, the Never-Ending Language Learner.

Key concepts: Probabilistic logic, Probabilistic argumentation, Probabilistic CTL, Probabilistic relevance model, Computer science, Probabilistic database, Prolog, Probabilistic logic network

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