Methods › General › Rule-based systems › SAFRAN
SAFRAN - Scalable and fast non-redundant rule application
SAFRAN
Introduced by Simon Ott et al. in Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
SAFRAN is a rule application framework which aggregates rules through a scalable clustering algorithm.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models 16 Sep 2021 · 1 repository · arXiv:2109.08002
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Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs 10 Dec 2020 · 1 repository · arXiv:2012.05750
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Knowledge Graphs | 2 |
| Link Prediction | 2 |
| Clustering | 1 |
| Prediction | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections