  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.
