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.

这篇论文介绍了知识图谱补全中规则学习方法的高效、可解释且与纯神经模型竞争的特点，提出了规则聚合问题的概念，并探讨了其理论基础。研究证明了现有的聚合方法可以通过对预测规则进行边缘推断操作来表示，并提出了一种既有效又被忽视的基准方法，与计算复杂度更高的方法具有竞争力。