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Inverse Entailment

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Encyclopedia of Machine Learning and Data Mining
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Definition

Inverse entailment is a generality relation in inductive logic programming. More specifically, when learning from entailment using a background theory B, a hypothesis H covers an example e, relative to the background theory B if and only if B ∧ H⊧e, that is, the background theory B and the hypothesis H together entail the example (see entailment). For instance, consider the background theory B:

  • bird :- blackbird.

  • bird :- ostrich.

and the hypothesis H:

  • flies :- bird.

Together B ∧ H entail the example e:

  • flies :- blackbird, normal.

This can be decided through deductive inference. Now when learning from entailment in inductive logic programming, one starts from the example e and the background theory B, and the aim is to induce a rule H that together with B entails the example. Inverting entailment is based on the observation that B ∧ H⊧e is logically equivalent to B ∧¬ e⊧¬ H, which in turn can be used to compute a hypothesis Hthat will cover the example relative to the...

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(2017). Inverse Entailment. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_415

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