{"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/open-domain-question-answering-using-early","title":"Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text","arxiv_id":"1809.00782","date":"2018-09-04","proceeding":"EMNLP 2018 10","authors":["Haitian Sun","Bhuwan Dhingra","Manzil Zaheer","Kathryn Mazaitis","Ruslan Salakhutdinov","William W. Cohen"],"abstract":"Open Domain Question Answering (QA) is evolving from complex pipelined\nsystems to end-to-end deep neural networks. Specialized neural models have been\ndeveloped for extracting answers from either text alone or Knowledge Bases\n(KBs) alone. In this paper we look at a more practical setting, namely QA over\nthe combination of a KB and entity-linked text, which is appropriate when an\nincomplete KB is available with a large text corpus. Building on recent\nadvances in graph representation learning we propose a novel model, GRAFT-Net,\nfor extracting answers from a question-specific subgraph containing text and KB\nentities and relations. We construct a suite of benchmark tasks for this\nproblem, varying the difficulty of questions, the amount of training data, and\nKB completeness. We show that GRAFT-Net is competitive with the\nstate-of-the-art when tested using either KBs or text alone, and vastly\noutperforms existing methods in the combined setting. Source code is available\nat https://github.com/OceanskySun/GraftNet .","url_abs":"http://arxiv.org/abs/1809.00782v1","url_pdf":"http://arxiv.org/pdf/1809.00782v1.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":"open-domain-question-answering-using-early","repo_url":"https://github.com/OceanskySun/GraftNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"open-domain-question-answering-using-early","repo_url":"https://github.com/RicardoL1u/GraftNet-Unofficial","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"open-domain-question-answering","task_name":"Open-Domain Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.00782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.00782"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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