Rule Induction in Knowledge Graphs Using Linear Programming

TL;DR

This paper introduces a linear programming approach to learn compact, interpretable rule sets for knowledge graph completion, achieving competitive accuracy and improved interpretability over existing methods.

Contribution

The paper proposes a novel LP-based method for rule induction in knowledge graphs that produces more compact and interpretable rule sets without sacrificing accuracy.

Findings

Achieves accuracy comparable to state-of-the-art methods.

Generates more compact rule sets for knowledge graph encoding.

Improves interpretability by reducing the number of rules.

Abstract

We present a simple linear programming (LP) based method to learn compact and interpretable sets of rules encoding the facts in a knowledge graph (KG) and use these rules to solve the KG completion problem. Our LP model chooses a set of rules of bounded complexity from a list of candidate first-order logic rules and assigns weights to them. The complexity bound is enforced via explicit constraints. We combine simple rule generation heuristics with our rule selection LP to obtain predictions with accuracy comparable to state-of-the-art codes, even while generating much more compact rule sets. Furthermore, when we take as input rules generated by other codes, we often improve interpretability by reducing the number of chosen rules, while maintaining accuracy.