{"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/a-simple-approach-to-case-based-reasoning-in","title":"A Simple Approach to Case-Based Reasoning in Knowledge Bases","arxiv_id":"2006.14198","date":"2020-06-25","proceeding":"AKBC 2020 6","authors":["Rajarshi Das","Ameya Godbole","Shehzaad Dhuliawala","Manzil Zaheer","Andrew McCallum"],"abstract":"We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \\emph{no training}, and is reminiscent of case-based reasoning in classical artificial intelligence (AI). Consider the task of finding a target entity given a source entity and a binary relation. Our non-parametric approach derives crisp logical rules for each query by finding multiple \\textit{graph path patterns} that connect similar source entities through the given relation. Using our method, we obtain new state-of-the-art accuracy, outperforming all previous models, on NELL-995 and FB-122. We also demonstrate that our model is robust in low data settings, outperforming recently proposed meta-learning approaches","url_abs":"https://arxiv.org/abs/2006.14198v2","url_pdf":"https://arxiv.org/pdf/2006.14198v2.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":"a-simple-approach-to-case-based-reasoning-in","repo_url":"https://github.com/rajarshd/CBR-AKBC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2006.14198","atlas_url":"https://app.syntology.ai/?focus=2006.14198","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}