{"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/graphene-semantically-linked-propositions-in","title":"Graphene: Semantically-Linked Propositions in Open Information Extraction","arxiv_id":"1807.11276","date":"2018-07-30","proceeding":"COLING 2018 8","authors":["Matthias Cetto","Christina Niklaus","André Freitas","Siegfried Handschuh"],"abstract":"We present an Open Information Extraction (IE) approach that uses a\ntwo-layered transformation stage consisting of a clausal disembedding layer and\na phrasal disembedding layer, together with rhetorical relation identification.\nIn that way, we convert sentences that present a complex linguistic structure\ninto simplified, syntactically sound sentences, from which we can extract\npropositions that are represented in a two-layered hierarchy in the form of\ncore relational tuples and accompanying contextual information which are\nsemantically linked via rhetorical relations. In a comparative evaluation, we\ndemonstrate that our reference implementation Graphene outperforms\nstate-of-the-art Open IE systems in the construction of correct n-ary\npredicate-argument structures. Moreover, we show that existing Open IE\napproaches can benefit from the transformation process of our framework.","url_abs":"http://arxiv.org/abs/1807.11276v1","url_pdf":"http://arxiv.org/pdf/1807.11276v1.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":"graphene-semantically-linked-propositions-in","repo_url":"https://github.com/Lambda-3/Graphene","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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.11276","atlas_url":"https://app.syntology.ai/?focus=1807.11276","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}