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Construction and Recommendation of a Water Affair Knowledge Graph

Author

Listed:
  • Jianzhuo Yan

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

  • Tiantian Lv

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

  • Yongchuan Yu

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

Abstract

Water affair data mainly consists of structured data and unstructured data, and the storage methods of data are diverse and heterogeneous. To meet the needs of water affair information integration, a method of constructing a knowledge graph using a combination of water affair structured and unstructured data is proposed. To meet the needs of a water information search, an information recommendation system for constructing a water affair knowledge graph is proposed. In this paper, the edit distance algorithm and latent Dirichlet allocation (LDA) algorithm are used to construct a water affair structured data and unstructured data combination knowledge graph, and this graph is validated based on the semantic distance algorithm. Finally, this paper uses the recall rate, accuracy rate, and F comprehensive results to compare the algorithms. The evaluation results of the edit distance algorithm and the LDA algorithm exceed 90%, which is greater than the comparison algorithm, thus confirming the validity and accuracy of the construction of a water affair knowledge graph. Furthermore, a set of water affair verification sets is used to verify the recommendation method, which proves the effectiveness of the recommended method.

Suggested Citation

  • Jianzhuo Yan & Tiantian Lv & Yongchuan Yu, 2018. "Construction and Recommendation of a Water Affair Knowledge Graph," Sustainability, MDPI, vol. 10(10), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3429-:d:172156
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    Citations

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    Cited by:

    1. Qi Zhang & Yuanqiao Wen & Chunhui Zhou & Hai Long & Dong Han & Fan Zhang & Changshi Xiao, 2019. "Construction of Knowledge Graphs for Maritime Dangerous Goods," Sustainability, MDPI, vol. 11(10), pages 1-16, May.
    2. Ziwei Xiao & Chunxiao Zhang, 2021. "Construction of Meteorological Simulation Knowledge Graph Based on Deep Learning Method," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    3. Wenling Liu & Yuexiang Yang & Xinyu Tu & Wan Wang, 2022. "ERSDMM: A Standard Digitalization Modeling Method for Emergency Response Based on Knowledge Graph," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    4. Yukun Jiang & Xin Gao & Wenxin Su & Jinrong Li, 2021. "Systematic Knowledge Management of Construction Safety Standards Based on Knowledge Graphs: A Case Study in China," IJERPH, MDPI, vol. 18(20), pages 1-15, October.

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