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.

翻译：知识图谱补全的规则学习方法高效、可解释且与纯神经模型具有竞争力。规则聚合问题涉及为同时被多条规则预测的候选事实寻找一个合理性分数。尽管该问题普遍存在（因为数据驱动的规则学习可能产生噪声且规模庞大的规则集），但在文献中却鲜有研究，且其理论基础此前从未在此背景下被探讨过。在本工作中，我们证明现有聚合方法可被表示为对预测规则进行的边际推断操作。特别地，我们指出常见的最大聚合策略（即基于置信度最高的规则对候选进行评分）具有概率解释。最后，我们提出了一种高效且被忽视的基线方法，该方法结合了先前策略，并可与计算复杂度更高的方法相媲美。