On the Aggregation of Rules for Knowledge Graph Completion

TL;DR

This paper explores the theoretical foundations of rule aggregation in knowledge graph completion, revealing probabilistic interpretations of existing methods and proposing a competitive, efficient baseline.

Contribution

It provides the first theoretical analysis of rule aggregation, connecting it to marginal inference, and introduces a new effective baseline method.

Findings

Max-aggregation has a probabilistic interpretation

Existing aggregation methods can be expressed as marginal inference

Proposed baseline is competitive and computationally efficient

Abstract

Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result in noisy and large rulesets, it is underrepresented in the literature and its theoretical foundations have not been studied before in this context. In this work, we demonstrate that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. In particular, we show that the common Max-aggregation strategy, which scores candidates based on the rule with the highest confidence, has a probabilistic interpretation. Finally, we propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches.