Construction of Knowledge Graph for Food Safety Regulations
China has established a complete food safety regulatory system,which is characterized by massive and fragmented nature and difficult to retrieve and analyze.Therefore,we conduct research on the construction of a Knowledge Graph of food safety regulations through a top-down and bottom-up approach based on the textual data of food safety regulations.First,we obtain multi-source heterogeneous food safety laws and regulations and Q&A data corpus,and analyzes user needs.Then,we define the ontology layer and attributes of the food safety Knowledge Graph.We extract the knowledge by using a rule-based method,and complete the domain named entity recognition by using Machine Learning-based methods for the knowledge with weak regularity.Finally,we realize the construction of Knowledge Graph for the food safety regulations.
food safety regulationKnowledge Graphnatural language processingMachine Learningnamed entity recognitionBERT model
国家科技期刊平台
NSTL
万方数据
