{"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/combining-axiom-injection-and-knowledge-base","title":"Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference","arxiv_id":"1811.06203","date":"2018-11-15","proceeding":null,"authors":["Masashi Yoshikawa","Koji Mineshima","Hiroshi Noji","Daisuke Bekki"],"abstract":"In logic-based approaches to reasoning tasks such as Recognizing Textual\nEntailment (RTE), it is important for a system to have a large amount of\nknowledge data. However, there is a tradeoff between adding more knowledge data\nfor improved RTE performance and maintaining an efficient RTE system, as such a\nbig database is problematic in terms of the memory usage and computational\ncomplexity. In this work, we show the processing time of a state-of-the-art\nlogic-based RTE system can be significantly reduced by replacing its\nsearch-based axiom injection (abduction) mechanism by that based on Knowledge\nBase Completion (KBC). We integrate this mechanism in a Coq plugin that\nprovides a proof automation tactic for natural language inference.\nAdditionally, we show empirically that adding new knowledge data contributes to\nbetter RTE performance while not harming the processing speed in this\nframework.","url_abs":"http://arxiv.org/abs/1811.06203v1","url_pdf":"http://arxiv.org/pdf/1811.06203v1.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":"combining-axiom-injection-and-knowledge-base","repo_url":"https://github.com/masashi-y/abduction_kbc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"knowledge-base-completion","task_name":"Knowledge Base Completion"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"rte","task_name":"RTE"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.06203","atlas_url":"https://app.syntology.ai/?focus=1811.06203","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}