SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models

TL;DR

SAFRAN is an interpretable, rule-based link prediction method that improves aggregation of rules, achieving state-of-the-art results and surpassing many embedding models on key knowledge graph benchmarks.

Contribution

SAFRAN introduces a novel aggregation approach called Non-redundant Noisy-OR, enhancing rule-based link prediction performance and interpretability.

Findings

Achieves state-of-the-art results on FB15K-237, WN18RR, and YAGO3-10.

Outperforms multiple embedding-based algorithms on FB15K-237 and WN18RR.

Narrows the gap between rule-based and embedding-based link prediction methods.

Abstract

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.