SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models
This work addresses the need for interpretable link prediction methods in AI, offering a practical solution for domains requiring transparency, though it is incremental over existing rule-based approaches.
The paper tackled the problem of interpretability in link prediction for knowledge graphs by improving rule-based methods to reduce redundancies, achieving state-of-the-art results on benchmarks like FB15K-237 and WN18RR, and narrowing the performance gap with embedding-based models.
Neural embedding-based machine learning models have shown promise for predicting novel links in knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based algorithm AnyBURL yielded highly competitive results on many general-purpose link prediction benchmarks. However, current approaches for aggregating predictions made by multiple rules are affected by redundancies. We improve upon AnyBURL by introducing the SAFRAN rule application framework, which uses a novel aggregation approach called Non-redundant Noisy-OR that detects and clusters redundant rules prior to aggregation. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmarks FB15K-237, WN18RR and YAGO3-10. Furthermore, it exceeds the results of multiple established embedding-based algorithms on FB15K-237 and WN18RR and narrows the gap between rule-based and embedding-based algorithms on YAGO3-10.