{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:54:45Z","timestamp":1760709285554},"reference-count":5,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Encyclopedia Semant. Comput. Robot. Int."],"published-print":{"date-parts":[[2017,3]]},"abstract":"<jats:p> Open information extraction (Open IE) systems aim to obtain relation tuples with highly scalable extraction in portable across domain by identifying a variety of relation phrases and their arguments in arbitrary sentences. The first generation of Open IE learns linear chain models based on unlexicalized features such as Part-of-Speech (POS) or shallow tags to label the intermediate words between pair of potential arguments for identifying extractable relations. Open IE currently is developed in the second generation that is able to extract instances of the most frequently observed relation types such as Verb, Noun and Prep, Verb and Prep, and Infinitive with deep linguistic analysis. They expose simple yet principled ways in which verbs express relationships in linguistics such as verb phrase-based extraction or clause-based extraction. They obtain a significantly higher performance over previous systems in the first generation. In this paper, we describe an overview of two Open IE generations including strengths, weaknesses and application areas. <\/jats:p>","DOI":"10.1142\/s2425038416300032","type":"journal-article","created":{"date-parts":[[2017,3,24]],"date-time":"2017-03-24T01:51:01Z","timestamp":1490320261000},"page":"1630003","source":"Crossref","is-referenced-by-count":12,"title":["Open information extraction"],"prefix":"10.1142","volume":"01","author":[{"given":"Duc-Thuan","family":"Vo","sequence":"first","affiliation":[{"name":"Laboratory for Systems, Software and Semantics (LS, Ryerson University, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebrahim","family":"Bagheri","sequence":"additional","affiliation":[{"name":"Laboratory for Systems, Software and Semantics (LS, Ryerson University, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2017,3,23]]},"reference":[{"key":"S2425038416300032BIB001","doi-asserted-by":"publisher","DOI":"10.1145\/1409360.1409378"},{"key":"S2425038416300032BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.12.006"},{"key":"S2425038416300032BIB017","volume-title":"A Comprehensive Grammar of the English Language","author":"Quirk R.","year":"1985"},{"key":"S2425038416300032BIB018","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00190"},{"key":"S2425038416300032BIB023","volume-title":"Routledge Studies in Germanic Linguistics","author":"Allerton D. J.","year":"2002"}],"container-title":["Encyclopedia with Semantic Computing and Robotic Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S2425038416300032","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T03:50:59Z","timestamp":1565149859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S2425038416300032"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3]]},"references-count":5,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2017,3,23]]},"published-print":{"date-parts":[[2017,3]]}},"alternative-id":["10.1142\/S2425038416300032"],"URL":"https:\/\/doi.org\/10.1142\/s2425038416300032","relation":{},"ISSN":["2529-7376","2529-7392"],"issn-type":[{"value":"2529-7376","type":"print"},{"value":"2529-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3]]}}}