{"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/knowledge-graph-generation-from-text","title":"Knowledge Graph Generation From Text","arxiv_id":"2211.10511","date":"2022-11-18","proceeding":null,"authors":["Igor Melnyk","Pierre Dognin","Payel Das"],"abstract":"In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages. The graph nodes are generated first using pretrained language model, followed by a simple edge construction head, enabling efficient KG extraction from the text. For each stage we consider several architectural choices that can be used depending on the available training resources. We evaluated the model on a recent WebNLG 2020 Challenge dataset, matching the state-of-the-art performance on text-to-RDF generation task, as well as on New York Times (NYT) and a large-scale TekGen datasets, showing strong overall performance, outperforming the existing baselines. We believe that the proposed system can serve as a viable KG construction alternative to the existing linearization or sampling-based graph generation approaches. Our code can be found at https://github.com/IBM/Grapher","url_abs":"https://arxiv.org/abs/2211.10511v1","url_pdf":"https://arxiv.org/pdf/2211.10511v1.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":"knowledge-graph-generation-from-text","repo_url":"https://github.com/ibm/grapher","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-generation","task_name":"Graph Generation"},{"task_slug":"joint-entity-and-relation-extraction","task_name":"Joint Entity and Relation Extraction"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-8","task":"Joint Entity and Relation Extraction","dataset":"WebNLG 3.0","model":"ReGen","rank_in_archive_order":2,"of":10,"metrics":{"F1":"72.3"},"uses_additional_data":false},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-8","task":"Joint Entity and Relation Extraction","dataset":"WebNLG 3.0","model":"Grapher (Text Nodes and Class Edges)","rank_in_archive_order":3,"of":10,"metrics":{"F1":"72.2"},"uses_additional_data":false},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-8","task":"Joint Entity and Relation Extraction","dataset":"WebNLG 3.0","model":"Amazon AI","rank_in_archive_order":6,"of":10,"metrics":{"F1":"68.9"},"uses_additional_data":false},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-8","task":"Joint Entity and Relation Extraction","dataset":"WebNLG 3.0","model":"BTS","rank_in_archive_order":8,"of":10,"metrics":{"F1":"68.2"},"uses_additional_data":false},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-8","task":"Joint Entity and Relation Extraction","dataset":"WebNLG 3.0","model":"CycleGT","rank_in_archive_order":9,"of":10,"metrics":{"F1":"34.2"},"uses_additional_data":false},{"leaderboard":"/sota/joint-entity-and-relation-extraction-on-8","task":"Joint Entity and Relation Extraction","dataset":"WebNLG 3.0","model":"Stanford OIE","rank_in_archive_order":10,"of":10,"metrics":{"F1":"15.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.10511","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}