Learning relations without closing the world

Abstract

This paper describes Link, a heuristically guided learner that combines aspects of three major approaches to ILP — LGG, search heuristic and (declarative) structural bias. In the manner of LGG algorithms, Link generates sets of candidate premise literals by collecting facts about the terms that appear in goal examples. It uses a linked-enough heuristic to select amongst these candidates to form hypothesis clauses (conjunctions of literals), and uses structural criteria to select among possible hypotheses in the manner of declarative bias-based systems. This combination — together with a parametrized hypothesis evaluation function — allows Link to learn in realistic situations where many FOL learners have problems because they are forced to make assumptions about the data: when there are no negative examples, when information is sparse, and when the closed-world assumption cannot or should not be made on examples and/or background.

Longer version including comparison and empirical results is available from the author.