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Knowledge graph construction method and system for nutrition and health

A knowledge map and nutrition technology, applied in the field of knowledge map construction for nutrition and health, can solve the problems that the knowledge map cannot meet the practical standards, and achieve the effect of improving practicability, good processing effect, and good logical relationship

Pending Publication Date: 2020-04-07
JOYOUNG CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In recent years, although many new systems have been developed at home and abroad, there is still no comprehensive knowledge map for recipes, ingredients, nutrients, disease symptoms, crowds, TCM physique, and sports. Only the existing field-independent maps cannot be mined. There is still no synthesis of deeper information on recipes, ingredients, nutrients, disease symptoms, populations, TCM physique, and sports, so the created knowledge graph does not meet practical standards

Method used

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  • Knowledge graph construction method and system for nutrition and health
  • Knowledge graph construction method and system for nutrition and health
  • Knowledge graph construction method and system for nutrition and health

Examples

Experimental program
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Embodiment 1

[0140] Such as Figure 7 As shown, this example illustrates the process of building a knowledge map for the field of nutrition and health:

[0141] 1) Construct ontology structure:

[0142] 1a) Obtain ontology data from websites and books of recipes, ingredients, nutrients, disease symptoms, crowds, TCM physique and sports through crawler and ORC technology;

[0143] 1b) Establish an ontology structure for each data source through the structure of the website or book, and the ontology structure in this embodiment is a structure tree;

[0144] 1c) if Figure 8 As shown, multiple structure trees are merged into one structure tree by simple merging;

[0145] 1d) if Figure 9 As shown, through logical merging, redundant nodes are removed from the structure tree obtained in step 1c), and the structure tree is constructed. It is found that the entity "vegetable" has a "green" label under the two data sources, and the corresponding description refers to the color and For the pla...

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Abstract

The invention provides a knowledge graph construction method and system for nutrition and health, and the method comprises the steps: determining data sources related to nutrition and health, collecting ontology data related to nutrition and health from the data sources, and building an ontology structure for each data source; extracting data in a corresponding data source according to the ontology structure so as to map and form entities corresponding to nodes in the ontology structure; determining an association relationship between the entities according to the index information of each data source; and establishing the knowledge graph according to the entities and the association relationship between the entities. The construction of the knowledge graph for the field of nutrition and health is completed, and the perfect knowledge graph is established.

Description

technical field [0001] The present invention relates to the field of big data processing, in particular to a method and system for constructing knowledge graphs oriented to nutrition and health. Background technique [0002] At present, with the continuous development of the Semantic Web, the World Wide Web contains more and more knowledge in the form of ontology. Ontology, as a novel form of knowledge representation, plays an extremely important role in the development of Semantic Web, and more and more applications based on ontology. With the continuous increase of ontologies in the Semantic Web, there must be similar or identical ontologies in related or identical application fields. Because these ontologies come from different sources and are constructed in different ways, these ontologies can complement each other in information. Therefore, To build a complete knowledge map, it must be based on a variety of different data sources. [0003] Due to different data source...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H20/60G06F16/36G06F16/31G06F16/953G06F40/30G06F40/295
CPCG16H20/60
Inventor 朱泽春钟敬德刘旭
Owner JOYOUNG CO LTD
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