  Neural embedding-based machine learning models have shown promise for
predicting novel links in biomedical 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, its applicability to large-scale prediction tasks on complex
biomedical knowledge bases is limited by long inference times and difficulties
with aggregating predictions made by multiple rules. We improve upon AnyBURL by
introducing the SAFRAN rule application framework which aggregates rules
through a scalable clustering algorithm. SAFRAN yields new state-of-the-art
results for fully interpretable link prediction on the established
general-purpose benchmark FB15K-237 and the large-scale biomedical benchmark
OpenBioLink. Furthermore, it exceeds the results of multiple established
embedding-based algorithms on FB15K-237 and narrows the gap between rule-based
and embedding-based algorithms on OpenBioLink. We also show that SAFRAN
increases inference speeds by up to two orders of magnitude.
