{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/safran-an-interpretable-rule-based-link","title":"SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models","arxiv_id":"2109.08002","date":"2021-09-16","proceeding":"AKBC 2021 10","authors":["Simon Ott","Christian Meilicke","Matthias Samwald"],"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.","url_abs":"https://arxiv.org/abs/2109.08002v1","url_pdf":"https://arxiv.org/pdf/2109.08002v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"safran-an-interpretable-rule-based-link","repo_url":"https://github.com/OpenBioLink/SAFRAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"}],"methods":[{"method_slug":"safran","method_name":"SAFRAN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb15k-237","task":"Link Prediction","dataset":"FB15k-237","model":"SAFRAN","rank_in_archive_order":6,"of":75,"metrics":{"Hits@1":"0.298","Hits@10":"0.537","MRR":"0.389"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"SAFRAN (white box, rule based)","rank_in_archive_order":30,"of":75,"metrics":{"Hits@1":"0.459","Hits@10":"0.578","MRR":"0.502"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-yago3-10","task":"Link Prediction","dataset":"YAGO3-10","model":"SAFRAN (white box, rule based)","rank_in_archive_order":7,"of":18,"metrics":{"Hits@1":"0.492","Hits@10":"0.693","MRR":"0.564"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.08002","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}