{"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/relation-schema-induction-using-tensor","title":"Relation Schema Induction using Tensor Factorization with Side Information","arxiv_id":"1605.04227","date":"2016-05-12","proceeding":"EMNLP 2016 11","authors":["Madhav Nimishakavi","Uday Singh Saini","Partha Talukdar"],"abstract":"Given a set of documents from a specific domain (e.g., medical research\njournals), how do we automatically build a Knowledge Graph (KG) for that\ndomain? Automatic identification of relations and their schemas, i.e., type\nsignature of arguments of relations (e.g., undergo(Patient, Surgery)), is an\nimportant first step towards this goal. We refer to this problem as Relation\nSchema Induction (RSI). In this paper, we propose Schema Induction using\nCoupled Tensor Factorization (SICTF), a novel tensor factorization method for\nrelation schema induction. SICTF factorizes Open Information Extraction\n(OpenIE) triples extracted from a domain corpus along with additional side\ninformation in a principled way to induce relation schemas. To the best of our\nknowledge, this is the first application of tensor factorization for the RSI\nproblem. Through extensive experiments on multiple real-world datasets, we find\nthat SICTF is not only more accurate than state-of-the-art baselines, but also\nsignificantly faster (about 14x faster).","url_abs":"http://arxiv.org/abs/1605.04227v3","url_pdf":"http://arxiv.org/pdf/1605.04227v3.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":"relation-schema-induction-using-tensor","repo_url":"https://github.com/malllabiisc/sictf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"open-information-extraction","task_name":"Open Information Extraction"},{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.04227","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}