{"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/probabilistic-case-based-reasoning-for-open","title":"Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion","arxiv_id":"2010.03548","date":"2020-10-07","proceeding":"Findings of the Association for Computational Linguistics 2020","authors":["Rajarshi Das","Ameya Godbole","Nicholas Monath","Manzil Zaheer","Andrew McCallum"],"abstract":"A case-based reasoning (CBR) system solves a new problem by retrieving `cases' that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an \"open-world\" setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method. Code available at https://github.com/ameyagodbole/Prob-CBR","url_abs":"https://arxiv.org/abs/2010.03548v2","url_pdf":"https://arxiv.org/pdf/2010.03548v2.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":"probabilistic-case-based-reasoning-for-open","repo_url":"https://github.com/ameyagodbole/Prob-CBR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"knowledge-graph-completion","task_name":"Knowledge Graph Completion"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"world-knowledge","task_name":"World Knowledge"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-fb122","task":"Link Prediction","dataset":"FB122","model":"Prob-CBR","rank_in_archive_order":1,"of":5,"metrics":{"HITS@3":"74.2","Hits@10":"78.2","Hits@5":"76.0","MRR":"72.7"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-nell-995","task":"Link Prediction","dataset":"NELL-995","model":"Prob-CBR","rank_in_archive_order":1,"of":4,"metrics":{"HITS@3":"0.85","Hits@1":"0.77","Hits@10":"0.89","MRR":"0.81"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wn18rr","task":"Link Prediction","dataset":"WN18RR","model":"ProbCBR","rank_in_archive_order":53,"of":75,"metrics":{"Hits@1":"0.43","Hits@10":"0.55","Hits@3":"0.49","MRR":"0.48"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}