Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases

Nikita Bhutani, Xinyi Zheng, Kun Qian, Yunyao Li, H. Jagadish


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.
Anthology ID:
2020.nli-1.1
Volume:
Proceedings of the First Workshop on Natural Language Interfaces
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | NLI | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://www.aclweb.org/anthology/2020.nli-1.1
DOI:
10.18653/v1/2020.nli-1.1
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nli-1.1.pdf
Video:
 http://slideslive.com/38929797