Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases
Nikita Bhutani, Xinyi Zheng, Kun Qian, Yunyao Li, H. V. Jagadish · 2020
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.