Looking at the Bottom and the Top: A Hybrid Logical Relational Learning System Based on Answer Sets

Traditional machine learning algorithms require a dataset composed of homogeneous objects, randomly sampled from a single relation. However, real world tasks such as link prediction and entity resolution, require the representation of multiple relations, heterogeneous and structured data. Inductive Logic Programming (ILP) is a sub area of machine learning that induces structured hypotheses from multi-relational examples and background knowledge (BK) represented as logical clauses. With a few exceptions, most of the systems developed in ILP induce Horn-clauses and uses Prolog as their baseline inference engine. However, the recent development of efficient Answer Set Programming solvers points out that these can be a viable option to be the reasoning component of ILP systems, especially to address nonmonotonic reasoning. In this paper, we present dASBoT, a system that is capable of inducing extended normal rules mined from answer sets yielded from the examples and the BK. We show empirical evidence that dASBoT can support the task of relational identification by learning rules in three link prediction and two entity resolution tasks.