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Food safety risk knowledge graph supporting hazard identification and construction method

A knowledge graph, food safety technology, applied in the field of food safety risk knowledge graph that supports hazard identification, can solve problems such as no clear application research norms, lack of standardized data sample sets, and increased difficulty in knowledge graphs, and achieves the goal of reaching between entities. The effect of relational richness, relational richness, richness of associations

Active Publication Date: 2021-03-12
HUBEI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0010] (3) There is no clear application research specification for specific goals of traditional knowledge graphs, and there is also a lack of standardized data sample sets for comparative research
For the third flaw, the lack of a clear set of research specifications and sample sets for comparison during the construction process greatly increases the difficulty of building a knowledge map

Method used

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  • Food safety risk knowledge graph supporting hazard identification and construction method
  • Food safety risk knowledge graph supporting hazard identification and construction method
  • Food safety risk knowledge graph supporting hazard identification and construction method

Examples

Experimental program
Comparison scheme
Effect test

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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Abstract

The invention belongs to the field of artificial intelligence, and discloses a food safety risk knowledge graph supporting hazard identification and a construction method. The construction method comprises the following steps: designing each node, relationship and attribute of a knowledge graph mode layer; establishing a Neo4j graph database; summarizing professional knowledge information into information convenient to understand based on massive news information, forum pasters and institution data, enabling non-professional knowledge to be extracted into useful knowledge, and extracting and constructing a food safety knowledge platform. According to the invention, the relevance between entity relations and knowledge is enriched while the accuracy of the knowledge entities is guaranteed, and a knowledge graph mode layer with rich relations and close relations between the entities is constructed. Based on massive news information, forum pasters and institution data, professional knowledge information which is difficult to understand is summarized into information which is convenient to understand, and non-professional knowledge is extracted into useful knowledge, and on the basis, afood safety knowledge platform is extracted and constructed.

Description

technical field [0001] The invention belongs to the technical field of food safety knowledge map, and in particular relates to a food safety risk knowledge map supporting hazard identification and a construction method. Background technique [0002] At present, knowledge graph (Knowledge Graph), also known as knowledge domain visualization or knowledge domain mapping map, is a series of different graphs showing the relationship between knowledge structures. It uses visualization technology to describe knowledge resources and their carriers, and mines, analyzes, constructs, draws and displays knowledge and their interrelationships. [0003] Before the advent of knowledge graph technology, people usually use deep learning to form a more abstract high-level representation attribute category or feature by combining low-level features to discover the distributed feature representation of data. As deep learning technology is widely used in speech recognition, image recognition an...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06F40/211G06F40/242G06F40/284G06Q10/06G06Q50/26
CPCG06F16/367G06Q10/0635G06F40/242G06F40/211G06F40/284G06Q50/265
Inventor 游兰马传香
Owner HUBEI UNIV