{"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/row-less-universal-schema","title":"Row-less Universal Schema","arxiv_id":"1604.06361","date":"2016-04-21","proceeding":"WS 2016 6","authors":["Patrick Verga","Andrew McCallum"],"abstract":"Universal schema jointly embeds knowledge bases and textual patterns to\nreason about entities and relations for automatic knowledge base construction\nand information extraction. In the past, entity pairs and relations were\nrepresented as learned vectors with compatibility determined by a scoring\nfunction, limiting generalization to unseen text patterns and entities.\nRecently, 'column-less' versions of Universal Schema have used compositional\npattern encoders to generalize to all text patterns. In this work we take the\nnext step and propose a 'row-less' model of universal schema, removing explicit\nentity pair representations. Instead of learning vector representations for\neach entity pair in our training set, we treat an entity pair as a function of\nits relation types. In experimental results on the FB15k-237 benchmark we\ndemonstrate that we can match the performance of a comparable model with\nexplicit entity pair representations using a model of attention over relation\ntypes. We further demonstrate that the model per- forms with nearly the same\naccuracy on entity pairs never seen during training.","url_abs":"http://arxiv.org/abs/1604.06361v1","url_pdf":"http://arxiv.org/pdf/1604.06361v1.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":"row-less-universal-schema","repo_url":"https://github.com/patverga/torch-relation-extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"knowledge-base-construction","task_name":"Knowledge Base Construction"},{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.06361","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}