00013

abstract bibtex

abstract bibtex

Learning recursive rules and inventing predicates are difficult tasks for Inductive Logic Programming techniques. We propose an approach where given a set of examples and counter-examples, and a background knowledge, a human expert must propose constructive rules in order to parse the examples. These rules are used to associate with each example (or counter-example) a tree. Through type inference each tree is transformed into a many-sorted term. These are then used as input for a grammatical inference algorithm that returns a deterministic tree automaton. The automaton is finally combined with the expert knowledge in order to obtain a logic program for the concept described by the examples. We report in this paper the general construction of GIFT, it's main algorithms, argue the necessity of the human expert, and show how it performs on some benchmarks.

@book{ bernard_gift:_????, title = {{GIFT}: {Grammatical} {Inference} for {Terms}}, shorttitle = {{GIFT}}, abstract = {Learning recursive rules and inventing predicates are difficult tasks for Inductive Logic Programming techniques. We propose an approach where given a set of examples and counter-examples, and a background knowledge, a human expert must propose constructive rules in order to parse the examples. These rules are used to associate with each example (or counter-example) a tree. Through type inference each tree is transformed into a many-sorted term. These are then used as input for a grammatical inference algorithm that returns a deterministic tree automaton. The automaton is finally combined with the expert knowledge in order to obtain a logic program for the concept described by the examples. We report in this paper the general construction of GIFT, it's main algorithms, argue the necessity of the human expert, and show how it performs on some benchmarks.}, author = {Bernard, Marc and {Colin de la Higuera} and Saint-etienne, Monnet}, note = {00013} }

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