On the Aggregation of Rules for Knowledge Graph Completion
This work addresses a common but understudied issue in knowledge graph completion, offering incremental improvements for researchers and practitioners using rule-based methods.
The paper tackles the rule aggregation problem in knowledge graph completion, where multiple rules predict the same fact, by demonstrating that existing aggregation methods can be expressed as marginal inference operations and proposing an efficient baseline that is competitive with more expensive approaches.
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.