{"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/sygma-system-for-generalizable-modular","title":"SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases","arxiv_id":"2109.13430","date":"2021-09-28","proceeding":null,"authors":["Sumit Neelam","Udit Sharma","Hima Karanam","Shajith Ikbal","Pavan Kapanipathi","Ibrahim Abdelaziz","Nandana Mihindukulasooriya","Young-suk Lee","Santosh Srivastava","Cezar Pendus","Saswati Dana","Dinesh Garg","Achille Fokoue","G P Shrivatsa Bhargav","Dinesh Khandelwal","Srinivas Ravishankar","Sairam Gurajada","Maria Chang","Rosario Uceda-Sosa","Salim Roukos","Alexander Gray","Guilherme LimaRyan Riegel","Francois Luus","L Venkata Subramaniam"],"abstract":"Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizability, particularly on two dimensions: (a) acrossmultiple reasoning types where both datasets and systems haveprimarily focused on multi-hop reasoning, and (b) across mul-tiple knowledge bases, where KBQA approaches are specif-ically tuned to a single knowledge base. In this paper, wepresent SYGMA, a modular approach facilitating general-izability across multiple knowledge bases and multiple rea-soning types. Specifically, SYGMA contains three high levelmodules: 1) KB-agnostic question understanding module thatis common across KBs 2) Rules to support additional reason-ing types and 3) KB-specific question mapping and answeringmodule to address the KB-specific aspects of the answer ex-traction. We demonstrate effectiveness of our system by evalu-ating on datasets belonging to two distinct knowledge bases,DBpedia and Wikidata. In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper. We show that our generalizable approach has bettercompetetive performance on multiple datasets on DBpediaand Wikidata that requires both multi-hop and temporal rea-soning","url_abs":"https://arxiv.org/abs/2109.13430v1","url_pdf":"https://arxiv.org/pdf/2109.13430v1.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":[],"tasks":[{"task_slug":"knowledge-base-question-answering","task_name":"Knowledge Base Question Answering"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/knowledge-base-question-answering-on-4","task":"Knowledge Base Question Answering","dataset":"SimpleQuestionsWikiData","model":"SYGMA","rank_in_archive_order":4,"of":5,"metrics":{"F1":"44.0"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-tempqa-wd","task":"Question Answering","dataset":"TempQA-WD","model":"SYGMA","rank_in_archive_order":2,"of":2,"metrics":{"F1":"32"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.13430","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}