High-quality and high-coverage rule sets are imperative to the success of Neuro-Symbolic Knowledge Graph Completion (NS-KGC) models, because they form the basis of all symbolic inferences. Recent literature builds neural models for generating rule sets, however, preliminary experiments show that they struggle with maintaining high coverage. In this work, we suggest three simple augmentations to existing rule sets: (1) transforming rules to their abductive forms, (2) generating equivalent rules that use inverse forms of constituent relations and (3) random walks that propose new rules. Finally, we prune potentially low quality rules. Experiments over four datasets and five ruleset-baseline settings suggest that these simple augmentations consistently improve results, and obtain up to 7.1 pt MRR and 8.5 pt Hits@1 gains over using rules without augmentations.

翻译：高质量、高覆盖度的规则集对于神经符号知识图谱补全（NS-KGC）模型的成功至关重要，因为它们构成了所有符号推理的基础。近期研究通过构建神经网络模型来生成规则集，但初步实验表明这些模型难以维持高覆盖度。本文针对现有规则集提出三种简单的增强方法：（1）将规则转换为其溯因形式，（2）生成使用构成关系逆形式的等价规则，以及（3）通过随机游走提出新规则。最后，我们对潜在的低质量规则进行剪枝。在四个数据集和五种规则集基线设置上的实验表明，这些简单增强方法能持续提升性能，相较于未增强的规则，最高可获得7.1个百分点的平均倒数排名（MRR）和8.5个百分点的Hits@1提升。