{"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/190412606","title":"OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference","arxiv_id":"1904.12606","date":"2019-04-12","proceeding":"NAACL 2019 6","authors":["Dongxu Zhang","Subhabrata Mukherjee","Colin Lockard","Xin Luna Dong","Andrew McCallum"],"abstract":"In this paper, we consider advancing web-scale knowledge extraction and\nalignment by integrating OpenIE extractions in the form of (subject, predicate,\nobject) triples with Knowledge Bases (KB). Traditional techniques from\nuniversal schema and from schema mapping fall in two extremes: either they\nperform instance-level inference relying on embedding for (subject, object)\npairs, thus cannot handle pairs absent in any existing triples; or they perform\npredicate-level mapping and completely ignore background evidence from\nindividual entities, thus cannot achieve satisfying quality. We propose OpenKI\nto handle sparsity of OpenIE extractions by performing instance-level\ninference: for each entity, we encode the rich information in its neighborhood\nin both KB and OpenIE extractions, and leverage this information in relation\ninference by exploring different methods of aggregation and attention. In order\nto handle unseen entities, our model is designed without creating\nentity-specific parameters. Extensive experiments show that this method not\nonly significantly improves state-of-the-art for conventional OpenIE\nextractions like ReVerb, but also boosts the performance on OpenIE from\nsemi-structured data, where new entity pairs are abundant and data are fairly\nsparse.","url_abs":"http://arxiv.org/abs/1904.12606v1","url_pdf":"http://arxiv.org/pdf/1904.12606v1.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":"190412606","repo_url":"https://github.com/zhangdongxu/relation-inference-naacl19","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=1904.12606","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}