Recent works in neural knowledge graph inference attempt to combine logic rules with knowledge graph embeddings to benefit from prior knowledge. However, they usually cannot avoid rule grounding, and injecting a diverse set of rules has still not been thoroughly explored. In this work, we propose InjEx, a mechanism to inject multiple types of rules through simple constraints, which capture definite Horn rules. To start, we theoretically prove that InjEx can inject such rules. Next, to demonstrate that InjEx infuses interpretable prior knowledge into the embedding space, we evaluate InjEx on both the knowledge graph completion (KGC) and few-shot knowledge graph completion (FKGC) settings. Our experimental results reveal that InjEx outperforms both baseline KGC models as well as specialized few-shot models while maintaining its scalability and efficiency.

翻译：近年来，神经知识图谱推理的研究尝试将逻辑规则与知识图谱嵌入相结合，以利用先验知识。然而，这些方法通常无法避免规则接地，且注入多样化规则的问题尚未得到充分探索。本文提出InjEx——一种通过简单约束注入多种类型规则的机制，该机制能够捕获确定的Horn规则。首先，我们从理论上证明InjEx可以注入此类规则。其次，为展示InjEx将可解释的先验知识融入嵌入空间，我们在知识图谱补全（KGC）和少样本知识图谱补全（FKGC）场景下对InjEx进行评估。实验结果表明，InjEx在保持可扩展性和高效性的同时，不仅超越了基线KGC模型，也优于专门的少样本模型。