WPI: Markov logic network-based statistical predicate invention

Abstract

Predicate Invention aims at discovering new emerging concepts in a logic theory. Since there is usually a combinatorial explosion of candidate concepts to be invented, only those that are really relevant should be selected, which cannot be done manually due to the huge number of candidates. While purely logical automatic approaches may be too rigid, statistical solutions provide more flexibility in assigning a degree of relevance to the various candidates in order to select the best ones. This paper proposes a new Statistical Relational Learning approach to Predicate Invention. It was implemented and tested on a traditional problem, yielding interesting results.