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Data Extraction and Preprocessing for Automated Question Answering Based on Knowledge Graphs

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1159))

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Abstract

This article discusses the algorithms and methods of data representation for knowledge graphs. The proposed algorithms make it possible to automate the process of extracting and processing data from users’ requests within the mixed learning process and reduce the role of an expert in the preparation of question-answering data sets necessary for training models of dialogue systems. The results show that the method of enrichment of the knowledge graph leads to an increase in the number of links and the accuracy of vector representation models.

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Acknowledgments

This work was supported by the Government of Russian Federation (Grant 08-08).

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Authors and Affiliations

  1. ITMO University, St. Petersburg, Russia

    Aleksei Romanov, Dmitry Volchek & Dmitry Mouromtsev

Authors
  1. Aleksei Romanov
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  2. Dmitry Volchek
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  3. Dmitry Mouromtsev
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Corresponding author

Correspondence to Aleksei Romanov .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. FEUP, Universidade do Porto, Porto, Portugal

    Luís Paulo Reis

  4. DIMES, Università della Calabria, Arcavacata, Italy

    Sandra Costanzo

  5. Faculty of Electrical Engineering, University of Montenegro, Podgorica, Montenegro

    Irena Orovic

  6. Universidade Portucalense, Porto, Portugal

    Fernando Moreira

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© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Romanov, A., Volchek, D., Mouromtsev, D. (2020). Data Extraction and Preprocessing for Automated Question Answering Based on Knowledge Graphs. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_27

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