Pruning Hypothesis Spaces Using Learned Domain Theories
We present a method to prune hypothesis spaces in the context of inductive logic programming. The main strategy of our method consists in removing hypotheses that are equivalent to already considered hypotheses. The distinguishing feature of our method is that we use learned domain theories to check for equivalence, in contrast to existing approaches which only prune isomorphic hypotheses. Specifically, we use such learned domain theories to saturate hypotheses and then check if these saturations are isomorphic. While conceptually simple, we experimentally show that the resulting pruning strategy can be surprisingly effective in reducing both computation time and memory consumption when searching for long clauses, compared to approaches that only consider isomorphism.
MS, GŠ and FŽ acknowledge support by project no. 17-26999S granted by the Czech Science Foundation. This work was done while OK was with Cardiff University and supported by a grant from the Leverhulme Trust (RPG-2014-164). SS is supported by ERC Starting Grant 637277. Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures”.
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