{"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/analogical-inference-for-multi-relational","title":"Analogical Inference for Multi-Relational Embeddings","arxiv_id":"1705.02426","date":"2017-05-06","proceeding":"ICML 2017 8","authors":["Hanxiao Liu","Yuexin Wu","Yiming Yang"],"abstract":"Large-scale multi-relational embedding refers to the task of learning the\nlatent representations for entities and relations in large knowledge graphs. An\neffective and scalable solution for this problem is crucial for the true\nsuccess of knowledge-based inference in a broad range of applications. This\npaper proposes a novel framework for optimizing the latent representations with\nrespect to the \\textit{analogical} properties of the embedded entities and\nrelations. By formulating the learning objective in a differentiable fashion,\nour model enjoys both theoretical power and computational scalability, and\nsignificantly outperformed a large number of representative baseline methods on\nbenchmark datasets. Furthermore, the model offers an elegant unification of\nseveral well-known methods in multi-relational embedding, which can be proven\nto be special instantiations of our framework.","url_abs":"http://arxiv.org/abs/1705.02426v2","url_pdf":"http://arxiv.org/pdf/1705.02426v2.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":"analogical-inference-for-multi-relational","repo_url":"https://github.com/quark0/ANALOGY","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"link-prediction","task_name":"Link Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-wn18","task":"Link Prediction","dataset":"WN18","model":"ANALOGY","rank_in_archive_order":24,"of":37,"metrics":{"Hits@1":"0.939","Hits@10":"0.947","Hits@3":"0.944","MRR":"0.942"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.02426","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}