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Question Answering on Agricultural Knowledge Graph Based on Multi-label Text Classification

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Cognitive Systems and Information Processing (ICCSIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1787))

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Abstract

Traditional search engines retrieve relevant web pages based on keywords in the entered questions, while sometimes the required information may not be included in these keyword-based retrieved web pages. Compared to the search engines, the question answering system can provide more accurate answers. However, traditional question answering systems can only provide answers to users based on matching the questions in a question answering pair. At the same time, the number of question answering pairs remain somewhat limited. As a result, the user’s requirements cannot be met well. In contrast, knowledge graphs can store information such as entities and their relationships in a structured pattern. Therefore, the knowledge graph is highly scalable as the data is stored in a structured form. Besides, the relationship between entities and the knowledge graph structure allows the desired answer to be found quickly. Moreover, the process of relation classification can also be regarded as an operation of text classification. Therefore, this study proposed a new approach to knowledge graph-based question answering systems that require a named entity recognition method and a multi-label text classification method to search for the answers. The results of entity name and question type are turned into a Cypher query that searches for the answer in the knowledge graph. In this paper, three models, i.e., TextCNN, bi-LSTM, and bi-LSTM + Att, are used to examine the effectiveness of multi-label text classification, demonstrating our method’s feasibility. Among these three models, TextCNN worked best, attaining an F1 score of 0.88.

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Notes

  1. 1.

    https://www.markrweber.com/graph-deep-learning.

  2. 2.

    http://shuju.aweb.com.cn/breed/breed-1-1.shtml.

  3. 3.

    http://tupu.zgny.com.cn/.

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Acknowledgment

The authors would like to thank the anonymous reviewers for their helpful reviews. The work is supported by the National Natural Science Foundation of China (Grant No. 32071901, 31871521) and the database in National Basic Science Data Center (NO. NBSDC-DB-20).

Author information

Authors and Affiliations

  1. Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China

    Pengxuan Zhu, Yuan Yuan & Lei Chen

  2. University of Science and Technology of China, Hefei, 230026, China

    Pengxuan Zhu

  3. Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China

    Huarui Wu

Authors
  1. Pengxuan Zhu
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  2. Yuan Yuan
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  3. Lei Chen
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  4. Huarui Wu
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Corresponding author

Correspondence to Lei Chen .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. University of Manchester, Manchester, UK

    Angelo Cangelosi

  3. Universität Hamburg, Hamburg, Germany

    Jianwei Zhang

  4. Fuzhou University, Fuzhou, China

    Yuanlong Yu

  5. Tsinghua University, Beijing, China

    Huaping Liu

  6. Tsinghua University, Beijing, China

    Bin Fang

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Zhu, P., Yuan, Y., Chen, L., Wu, H. (2023). Question Answering on Agricultural Knowledge Graph Based on Multi-label Text Classification. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_14

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  • DOI: https://doi.org/10.1007/978-981-99-0617-8_14

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