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

Abstract

Abstract. 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 example of the benefits of our ILP framework, we propose a scheme for defining the hypothesis search space through Inverse Entailment using multiple example seeds.

Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment

“…The construction of a most-specific clause, as is done by MDIE, is necessary for the construction of a BotGNN. However, the construction of this clause need not be done by using logic programming technology: it has been shown, for example, in (Bravo et al, 2005) how this same formula can be obtained entirely using operations on relational databases. In many ways, such an implementation would be more convenient, since the information in modes can be incorporated (and even augmented) by the schema of a relational database.…”