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    <titleInfo>
        <title>A State-transition Framework to Answer Complex Questions over Knowledge Base</title>
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    <name type="personal">
        <namePart type="given">Sen</namePart>
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        <namePart type="given">Lei</namePart>
        <namePart type="family">Zou</namePart>
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    <name type="personal">
        <namePart type="given">Xinbo</namePart>
        <namePart type="family">Zhang</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions. To address that, in this paper, we propose a State Transition-based approach to translate a complex natural language question N to a semantic query graph (SQG), which is used to match the underlying knowledge graph to find the answers to question N. In order to generate SQG, we propose four primitive operations (expand, fold, connect and merge) and a learning-based state transition approach. Extensive experiments on several benchmarks (such as QALD, WebQuestions and ComplexQuestions) with two knowledge bases (DBpedia and Freebase) confirm the superiority of our approach compared with state-of-the-arts.</abstract>
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        <date>2018-oct-nov</date>
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            <start>2098</start>
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%0 Conference Proceedings
%T A State-transition Framework to Answer Complex Questions over Knowledge Base
%A Hu, Sen
%A Zou, Lei
%A Zhang, Xinbo
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F hu-etal-2018-state
%X Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions. To address that, in this paper, we propose a State Transition-based approach to translate a complex natural language question N to a semantic query graph (SQG), which is used to match the underlying knowledge graph to find the answers to question N. In order to generate SQG, we propose four primitive operations (expand, fold, connect and merge) and a learning-based state transition approach. Extensive experiments on several benchmarks (such as QALD, WebQuestions and ComplexQuestions) with two knowledge bases (DBpedia and Freebase) confirm the superiority of our approach compared with state-of-the-arts.
%R 10.18653/v1/D18-1234
%U https://aclanthology.org/D18-1234
%U https://doi.org/10.18653/v1/D18-1234
%P 2098-2108

Markdown (Informal)

[A State-transition Framework to Answer Complex Questions over Knowledge Base](https://aclanthology.org/D18-1234) (Hu et al., EMNLP 2018)

• A State-transition Framework to Answer Complex Questions over Knowledge Base (Hu et al., EMNLP 2018)

ACL

• Sen Hu, Lei Zou, and Xinbo Zhang. 2018. A State-transition Framework to Answer Complex Questions over Knowledge Base. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2098–2108, Brussels, Belgium. Association for Computational Linguistics.