@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases
%A Bhutani, Nikita
%A Zheng, Xinyi
%A Qian, Kun
%A Li, Yunyao
%A Jagadish, H.
%Y Awadallah, Ahmed Hassan
%Y Su, Yu
%Y Sun, Huan
%Y Yih, Scott Wen-tau
%S Proceedings of the First Workshop on Natural Language Interfaces
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F bhutani-etal-2020-answering
%X 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.
%R 10.18653/v1/2020.nli-1.1
%U https://aclanthology.org/2020.nli-1.1
%U https://doi.org/10.18653/v1/2020.nli-1.1
%P 1-10
Markdown (Informal)
[Answering Complex Questions by Combining Information from Curated and Extracted Knowledge Bases](https://aclanthology.org/2020.nli-1.1) (Bhutani et al., NLI 2020)
ACL