Nikita Bhutani author Xinyi Zheng author Kun Qian author Yunyao Li author H Jagadish author 2020-jul text Association for Computational Linguistics Online conference publication 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. bhutani-etal-2020-answering https://www.aclweb.org/anthology/2020.nli-1.1 2020-jul 1 10