Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion
This work addresses the need for high-coverage rule sets in knowledge graph completion, but it is incremental as it builds on existing rule sets with simple augmentations.
The paper tackles the problem of low coverage in rule sets for Neuro-Symbolic Knowledge Graph Completion by proposing three simple augmentations, resulting in gains of up to 7.1 pt MRR and 8.5 pt Hits@1 over unaugmented rules.
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.