{"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/question-answering-over-knowledge-bases-by","title":"Leveraging Abstract Meaning Representation for Knowledge Base Question Answering","arxiv_id":"2012.01707","date":"2020-12-03","proceeding":"Findings (ACL) 2021 8","authors":["Pavan Kapanipathi","Ibrahim Abdelaziz","Srinivas Ravishankar","Salim Roukos","Alexander Gray","Ramon Astudillo","Maria Chang","Cristina Cornelio","Saswati Dana","Achille Fokoue","Dinesh Garg","Alfio Gliozzo","Sairam Gurajada","Hima Karanam","Naweed Khan","Dinesh Khandelwal","Young-suk Lee","Yunyao Li","Francois Luus","Ndivhuwo Makondo","Nandana Mihindukulasooriya","Tahira Naseem","Sumit Neelam","Lucian Popa","Revanth Reddy","Ryan Riegel","Gaetano Rossiello","Udit Sharma","G P Shrivatsa Bhargav","Mo Yu"],"abstract":"Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training datasets. In this work, we propose Neuro-Symbolic Question Answering (NSQA), a modular KBQA system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question understanding; (2) a simple yet effective graph transformation approach to convert AMR parses into candidate logical queries that are aligned to the KB; (3) a pipeline-based approach which integrates multiple, reusable modules that are trained specifically for their individual tasks (semantic parser, entity andrelationship linkers, and neuro-symbolic reasoner) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia (QALD-9 and LC-QuAD1.0). Furthermore, our analysis emphasizes that AMR is a powerful tool for KBQA systems.","url_abs":"https://arxiv.org/abs/2012.01707v2","url_pdf":"https://arxiv.org/pdf/2012.01707v2.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":"question-answering-over-knowledge-bases-by","repo_url":"https://github.com/IBM/transition-amr-parser","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"abstract-meaning-representation","task_name":"Abstract Meaning Representation"},{"task_slug":"entity-linking","task_name":"Entity Linking"},{"task_slug":"knowledge-base-question-answering","task_name":"Knowledge Base Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2012.01707","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}