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

Qiang Huang (), Zongyuan Wu, Mantao Wang, Youzhi Tao, Yinghao He and Francesco Marinello
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Qiang Huang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Zongyuan Wu: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
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

Agriculture, 2023, vol. 13, issue 9, 1-19

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.

Keywords: tea; suitable for people; knowledge graph; link prediction; knowledge graph completion; transfer learning; deep learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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