Rule Confidence Aggregation for Knowledge Graph Completion.

Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule confidence aggregation problem aims to find a single plausibility score for a candidate fact predicted by multiple rules. Despite its ubiquity due to noisy and large rule sets from data-driven learning, the problem is underrepresented in the literature and lacks a theoretical foundation. 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 that is slightly superior over the simple strategies.