Nikita Bhutani, Xinyi Zheng, Xinyi Zheng, Kun Qian, Yunyao Li, H. Jagadish
{"title":"Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases","authors":"Nikita Bhutani, Xinyi Zheng, Xinyi Zheng, Kun Qian, Yunyao Li, H. Jagadish","doi":"10.18653/v1/2020.nli-1.1","DOIUrl":"https://doi.org/10.18653/v1/2020.nli-1.1","url":null,"abstract":"Knowledge-based question answering (KB_QA) has long focused on simple questions that can be answered from a single knowledge source, a manually curated or an automatically extracted KB. In this work, we look at answering complex questions which often require combining information from multiple sources. We present a novel KB-QA system, Multique, which can map a complex question to a complex query pattern using a sequence of simple queries each targeted at a specific KB. It finds simple queries using a neural-network based model capable of collective inference over textual relations in extracted KB and ontological relations in curated KB. Experiments show that our proposed system outperforms previous KB-QA systems on benchmark datasets, ComplexWebQuestions and WebQuestionsSP.","PeriodicalId":427040,"journal":{"name":"Proceedings of the First Workshop on Natural Language Interfaces","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134268395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}