Reliable Rule-Guided Augmentation for Knowledge Graph Completion
TL;DR
This method induces Horn rules from target-relation-guided random walks and assigns each candidate a reliability score that combines smoothed rule confidence, normalized support, path consistency, type validity, and redundancy.
Abstract
Knowledge graph completion (KGC) commonly relies on embedding models trained with observed triples. Multi-hop paths provide additional evidence, but rule-derived triples differ in reliability and should not receive equal training weight. We study how to select reliable candidates using only the training graph and how to control their influence across different scoring functions. Our method induces Horn rules from target-relation-guided random walks and assigns each candidate a reliability score. The score combines smoothed rule confidence, normalized support, path consistency, type validity, and redundancy. Selected candidates enter the embedding objective as weighted positive samples, while the embedding model remains the final link predictor. Across five scoring functions, the method increases MRR by 0.001–0.012 on WN18RR and 0.001–0.005 on FB15k-237 in matched Base/Aug comparisons. The sensitivity and ablation results show that unfiltered multi-hop triples can impair performance and candidate control is necessary in the evaluated setting.