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

IF 3.3 2区 农林科学 Q1 AGRONOMY
Qiang Huang, Zongyuan Wu, Mantao Wang, Youzhi Tao, Yinghao He, F. Marinello
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引用次数: 0

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.
基于interact - se +GCN模型的茶叶品种“适人”关系预测
本文提出了一种改进的链接预测模型,用于预测茶叶知识图谱中的“适人”关系。各种类型的茶和合适的目标群体之间的关系尚未得到充分的探索,现有的InteractE模型仍然没有充分捕捉到实体和关系之间相互作用的一部分复杂信息。在本研究中,我们将SENet集成到InteractE模型的特征层中,以增强对特征通道中有用信息的捕获。此外,采用GCN层作为编码器,采用senet集成的InteractE模型作为解码器,进一步捕获知识图中的邻居节点信息。此外,我们提出的改进模型与几个标准模型(包括来自公共数据集的原始模型(WN18RR, Kinship))相比有显著改进。最后,我们构建了一个包含6698条记录的茶叶数据集,其中包括330种茶叶和29种关系类型。我们通过迁移学习预测茶叶数据集中的“适合人”关系。当将我们的模型与原始模型进行比较时,我们观察到WN18RR数据集的H@10改进了1.4%,亲属关系数据集的H@1改进了7.6%,MRR改进了5.2%。对于茶叶数据集,我们实现了H@3的4.1%增长和H@10的2.5%增长。本研究将有助于充分挖掘茶叶品种的价值潜力,为健康饮茶的研究提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Agriculture-Basel
Agriculture-Basel Agricultural and Biological Sciences-Food Science
CiteScore
4.90
自引率
13.90%
发文量
1793
审稿时长
11 weeks
期刊介绍: Agriculture (ISSN 2077-0472) is an international and cross-disciplinary scholarly and scientific open access journal on the science of cultivating the soil, growing, harvesting crops, and raising livestock. We will aim to look at production, processing, marketing and use of foods, fibers, plants and animals. The journal Agriculturewill publish reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
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