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        <title>Knowledge Base Question Answering via Encoding of Complex Query Graphs</title>
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    <name type="personal">
        <namePart type="given">Kangqi</namePart>
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        <namePart type="given">Fengli</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">Jun’ichi</namePart>
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    <abstract>Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1242</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Knowledge Base Question Answering via Encoding of Complex Query Graphs
%A Luo, Kangqi
%A Lin, Fengli
%A Luo, Xusheng
%A Zhu, Kenny
%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 luo-etal-2018-knowledge
%X Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work very well on complex questions because they were not able to simultaneously represent the question and the corresponding complex query structure. In this work, we encode such complex query structure into a uniform vector representation, and thus successfully capture the interactions between individual semantic components within a complex question. This approach consistently outperforms existing methods on complex questions while staying competitive on simple questions.
%R 10.18653/v1/D18-1242
%U https://aclanthology.org/D18-1242
%U https://doi.org/10.18653/v1/D18-1242
%P 2185-2194

Markdown (Informal)

[Knowledge Base Question Answering via Encoding of Complex Query Graphs](https://aclanthology.org/D18-1242) (Luo et al., EMNLP 2018)

• Knowledge Base Question Answering via Encoding of Complex Query Graphs (Luo et al., EMNLP 2018)

ACL

• Kangqi Luo, Fengli Lin, Xusheng Luo, and Kenny Zhu. 2018. Knowledge Base Question Answering via Encoding of Complex Query Graphs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2185–2194, Brussels, Belgium. Association for Computational Linguistics.