@inproceedings{bhutani-etal-2020-answering,
    title = "Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases",
    author = "Bhutani, Nikita  and
      Zheng, Xinyi  and
      Qian, Kun  and
      Li, Yunyao  and
      Jagadish, H.",
    editor = "Awadallah, Ahmed Hassan  and
      Su, Yu  and
      Sun, Huan  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the First Workshop on Natural Language Interfaces",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.nli-1.1",
    doi = "10.18653/v1/2020.nli-1.1",
    pages = "1--10",
    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.",
}