A food safety risk knowledge map and construction method supporting hazard identification
A knowledge graph, food safety technology, applied in the field of food safety risk knowledge graph construction that supports hazard identification, can solve the problems of lack of normative data sample sets, no clear application research norms, increased difficulty of knowledge graphs, etc., to achieve rich relationships, Relevance-rich, relativity-rich effects
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Embodiment 1
[0098] Such as figure 1 As shown, the method for building a food safety knowledge platform based on Neo4j knowledge map technology provided by the embodiment of the present invention includes the following steps:
[0099] S101, designing each node, relationship and attribute of the knowledge graph schema layer;
[0100] S102, establishing a Neo4j graph database;
[0101] S103. Summarize professional knowledge information into easy-to-understand information based on massive news information, forum tips, and institutional data, and condense and extract non-professional knowledge into useful knowledge, extracting and building a food safety knowledge platform.
Embodiment 2
[0103] 1. The design of the knowledge map model layer (such as figure 2 shown)
[0104] 2. Definition of each node, relationship and attribute of the schema layer
[0105] There are 8 entity classes in the pattern layer, and the entity relationships and attributes are as follows:
[0106] (1) food
[0107] Food - Food ID
[0108]The food ID is defined as F-XX-XXX, starting with the letter F, the middle part is the food category ID number, and the last part is the food number, which is combined into a unique ID for each food. This ID is uniquely corresponding to the food IDs of the other two subsystems .
[0109] Food - English name
[0110] The English name corresponding to the food, one food has only one English name.
[0111] food - aliases
[0112] Food aliases, one food can correspond to multiple aliases.
[0113] Food - Belongs to - Food Category
[0114] The food category corresponding to the food, one food only corresponds to one category
[0115] Food - Raw ...
Embodiment 3
[0166] 1. Database establishment
[0167] LOAD CSV WITH HEADERS FROM "file: / / / M.csv" AS line create(M: identification method {detailed method: line.Method, ID: line.M_ID})
[0168] LOAD CSV WITH HEADERS FROM "file: / / / A.csv" AS line create(A: additive {additive name: line.Additive})
[0169] LOAD CSV WITH HEADERS FROM "file: / / / F.csv" AS line create(F: Food {food name: line.Food})
[0170] LOAD CSV WITH HEADERS FROM "file: / / / C.csv" AS line create(C: Food category {category name: line.Category})
[0171] LOAD CSV WITH HEADERS FROM "file: / / / D.csv" AS line create(D: detailed hazard {detailed hazard: line.Damage})
[0172] LOAD CSV WITH HEADERS FROM "file: / / / E.csv" AS line create(E: detailed function {detailed function: line.Effecct})
[0173] LOAD CSV WITH HEADERS FROM "file: / / / FAM.csv"AS line match(A: additive {additive name: line['Additive']}) match(F: food {food name: line['Food']} )create(M: identification method {detailed method: line.Method, identification food: line.Food...
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