IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1732-d1230536.html
   My bibliography  Save this article

Prediction of Tea Varieties’ “Suitable for People” Relationship: Based on the InteractE-SE+GCN Model

Author

Listed:
  • Qiang Huang

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    These authors contributed equally to this work.)

  • Zongyuan Wu

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    These authors contributed equally to this work.)

  • Mantao Wang

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Youzhi Tao

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Yinghao He

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Francesco Marinello

    (Department of Land, Environment, Agriculture and Forestry, University of Padua, 35020 Legnaro, Italy)

Abstract

This study proposes an improved link prediction model for predicting the “suitable for people” relationship within the knowledge graph of tea. The relationships between various types of tea and suitable target groups have yet to be fully explored, and the existing InteractE model still does not adequately capture a portion of the complex information around the interactions between entities and relationships. In this study, we integrate SENet into the feature layer of the InteractE model to enhance the capturing of helpful information in the feature channels. Additionally, the GCN layer is employed as the encoder, and the SENet-integrated InteractE model is used as the decoder to further capture the neighbour node information in the knowledge graph. Furthermore, our proposed improved model demonstrates significant improvements compared to several standard models, including the original model from public datasets (WN18RR, Kinship). Finally, we construct a tea dataset comprising 6698 records, including 330 types of tea and 29 relationship types. We predict the “suitable for people” relationship in the tea dataset through transfer learning. When comparing our model with the original model, we observed an improvement of 1.4% in H@10 for the WN18RR dataset, a 7.6% improvement in H@1 for the Kinship dataset, and a 5.2% improvement in MRR. Regarding the tea dataset, we achieved a 4.1% increase in H@3 and a 2.5% increase in H@10. This study will help to fully exploit the value potential of tea varieties and provide a reference for studies assessing healthy tea drinking.

Suggested Citation

  • Qiang Huang & Zongyuan Wu & Mantao Wang & Youzhi Tao & Yinghao He & Francesco Marinello, 2023. "Prediction of Tea Varieties’ “Suitable for People” Relationship: Based on the InteractE-SE+GCN Model," Agriculture, MDPI, vol. 13(9), pages 1-19, August.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1732-:d:1230536
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1732/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/13/9/1732/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1732-:d:1230536. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.