Temporal Knowledge Graph for Food Risk Prediction

Accurate prediction of food-safety risk is an effective measure to improve food risk prevention. The complex and continuous characteristics of food safety influence various factors, thus, a food-safety risk prediction model based on a temporal knowledge graph is proposed in this paper. First, a food-safety risk dataset is constructed by collecting food supervision and management sampling data used in daily life from 2018 to 2021 from the State Administration for Market Regulation. The dataset contains five categories: fruits, vegetables, meat, aquatic products, and dairy products. Then, a novel food-safety temporal knowledge graph is designed based on the proposed index system because food-safety data have temporal characteristics. A temporal knowledge graph network is proposed to build a food safety risk prediction model, which comprises historical learning and generation methods. The proposed food-safety temporal knowledge graph can predict the food risk level and types of hazardous substances in a certain period. Finally, the comparative experiments on the food safety dataset constructed in this article showed that the proposed model achieved the accuracy of 86.15%, the Mean Reciprocal Rank (MRR) of 88.64%, and the recall of 85.13%. This demonstrated that the proposed food safety risk prediction method based on temporal knowledge graph networks has higher accuracy and stronger interpretability compared to some existing data prediction methods