A Framework for Set-Oriented Computation in Inductive Logic Programming and Its Application in Generalizing Inverse Entailment [chapter]

Héctor Corrada Bravo, David Page, Raghu Ramakrishnan, Jude Shavlik, Vitor Santos Costa
2005 Lecture Notes in Computer Science  
We propose a new approach to Inductive Logic Programming that systematically exploits caching and offers a number of advantages over current systems. It avoids redundant computation, is more amenable to the use of set-oriented generation and evaluation of hypotheses, and allows relational DBMS technology to be more easily applied to ILP systems. Further, our approach opens up new avenues such as probabilistically scoring rules during search and the generation of probabilistic rules. As a first
more » ... xample of the benefits of our ILP framework, we propose a scheme for defining the hypothesis search space through Inverse Entailment using multiple example seeds. ⋆ We seek to represent hypotheses in H(M, h) in such a way that, intuitively, the result of operating on a hypothesis is reused when operating on an extension of the hypothesis. For instance, when measuring the coverage of a hypothesis, the substitution found in proving that a hypothesis covers an example contains bindings which could potentially make an extension of the hypothesis cover the same example. Our representation should reuse those bindings when measuring the coverage of the extended hypothesis. 1. Compute projection of t i . We project the input hypothesis table to only those columns containing the bound arguments of the extension, following fid fields to
doi:10.1007/11536314_5 fatcat:ypdqsuvlevaobbysz4e3s3zm2e