  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.
