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Leveraging Knowledge Graph Embeddings for Natural Language Question Answering

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Book cover Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11446))

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Abstract

A promising pathway for natural language question answering over knowledge graphs (KG-QA) is to translate natural language questions into graph-structured queries. During the translation, a vital process is to map entity/relation phrases of natural language questions to the vertices/edges of underlying knowledge graphs which can be used to construct target graph-structured queries. However, due to linguistic flexibility and ambiguity of natural language, the mapping process is challenging and has been a bottleneck of KG-QA models. In this paper, we propose a novel framework, called KemQA, which stands on recent advances in relation phrase dictionaries and knowledge graph embedding techniques to address the mapping problem and construct graph-structured queries of natural language questions. Extensive experiments were conducted on question answering benchmark datasets. The results demonstrate that our framework outperforms state-of-the-art baseline models in terms of effectiveness and efficiency.

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Notes

  1. 1.

    In this paper, we focus on the NLQs whose answers are vertices in the underlying KG. Therefore, we only consider vertex variables.

  2. 2.

    http://wiki.dbpedia.org/dbpedia-2016-04-statistics.

  3. 3.

    https://www.wikipedia.org.

  4. 4.

    http://wiki.dbpedia.org/develop/datasets.

  5. 5.

    http://www.mpi-inf.mpg.de/yago-naga/patty/.

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Acknowledgment

This work was supported by National Key Research and Development Program of China (2018YFB1004500), National Natural Science Foundation of China (61532015, 61532004, 61672419, 61672418, and U1736204), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Centre for Engineering Science and Technology, Teaching Reform Project of XJTU (No. 17ZX044), and China Scholarship Council (No. 201806280450).

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Authors and Affiliations

  1. National Engineering Lab for Big Data Analytics, Xi’an Jiaotong University, Xi’an, China

    Ruijie Wang & Jun Liu

  2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

    Ruijie Wang & Jun Liu

  3. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Meng Wang

  4. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Weitong Chen

  5. Fraunhofer FIT, 53754, Sankt Augustin, Germany

    Michael Cochez & Stefan Decker

  6. Informatik 5, RWTH University Aachen, Aachen, Germany

    Ruijie Wang, Michael Cochez & Stefan Decker

  7. Faculty of Information Technology, University of Jyvaskyla, Jyväskylä, Finland

    Michael Cochez

Authors
  1. Ruijie Wang
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  2. Meng Wang
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  3. Jun Liu
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  4. Weitong Chen
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  5. Michael Cochez
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  6. Stefan Decker
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Corresponding author

Correspondence to Meng Wang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Wang, R., Wang, M., Liu, J., Chen, W., Cochez, M., Decker, S. (2019). Leveraging Knowledge Graph Embeddings for Natural Language Question Answering. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_39

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  • DOI: https://doi.org/10.1007/978-3-030-18576-3_39

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  • Online ISBN: 978-3-030-18576-3

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