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Healthy diet knowledge network construction method based on neural network and graph structure

A technology of neural network and knowledge network, applied in the direction of biological neural network model, neural architecture, special data processing application, etc., can solve the problems of obvious domain limitation and low degree of automation

Inactive Publication Date: 2017-11-24
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0009] The purpose of the present invention is to provide a method for constructing a healthy diet knowledge network based on a neural network and a graph structure. The traditional healthy diet knowledge base is not highly automated and has obvious field limitations, which greatly reduces the construction and maintenance costs of the healthy diet knowledge base. Fully automatic linking and labeling of the relationship between diseases and food entities, and no specific field restrictions

Method used

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  • Healthy diet knowledge network construction method based on neural network and graph structure
  • Healthy diet knowledge network construction method based on neural network and graph structure
  • Healthy diet knowledge network construction method based on neural network and graph structure

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Embodiment 1

[0072] The database used by the healthy diet knowledge network in this embodiment is the NoSQL map database Neo4J. Compared with the relational database used in the traditional knowledge base, the map database can store entities and the relationship between entities in a richer form, and at the same time provide more It is a convenient and fast query method.

[0073] like figure 1 As shown, this embodiment provides a method for constructing a health knowledge network based on a neural network and a map structure, and the method includes the following steps:

[0074] (1) Carry out word vector modeling on all text corpora participating in learning and training

[0075] The "word vector" in this embodiment refers to the neural network word embedding modeling algorithm and its supporting modeling tool word2vec proposed by Google in 2013. Physique Description These text corpora are input into the word vector tool word2vec, and the word vector modeling is performed on it. The resu...

Embodiment 2

[0116] This embodiment is a specific application example, which is called by relevant researchers and application developers in the form of middleware, and is composed of the following components: 1) Graph database maintenance component P01, including querying the graph database, adding and deleting nodes and edges , security backup and other functions; 2) word vector operation component P02; 3) association detection and representation component P03; 4) neural network relationship classification component P04; 5) auxiliary function component P05, including text preprocessing, text original data management, stage Functions such as result cache management. The overall architecture of the method described in the invention is as follows Image 6 As shown, the specific functions and usage techniques of each component are shown in Table 1 below.

[0117]

[0118] Table 1 Specific functions and usage technical table of each component

[0119] The text data processed by the metho...

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Abstract

The invention discloses a healthy diet knowledge network construction method based on a neural network and a graph structure. The method comprises the steps that word vector modeling is performed on a text corpus, so that each non-stop word in the text corpus corresponds to one word vector with a fixed length; a cosine similarity between two word vectors is used to measure the relational degree between entities corresponding to the two word vectors; food material entity nodes and symptom entity nodes are extracted, the two types of entity nodes are regarded as entity nodes in a topological structure, edge relations between the entity nodes are constructed to form the graph structure, and all the edge relations between the entity nodes are described by one group of representative words; vector expressions corresponding to each representative word are arranged to obtain a representative matrix of the edge relations between the entity nodes; and a classification framework based on a deep neural network is designed, the representative matrix is input, and polarities of the edge relations between the entity nodes are classified. Through the method, the problems that a traditional healthy diet knowledge base is not high in automation degree and obvious in domain limitation are effectively solved.

Description

technical field [0001] The invention relates to a method for constructing a healthy diet knowledge network, in particular to a method for constructing a healthy diet knowledge network based on a neural network and a map structure, and belongs to the technical field of knowledge representation and knowledge base construction. Background technique [0002] With the improvement of people's living standards, more and more people begin to pay attention to their own dietary health issues. Since ancient times, traditional Chinese medicine has been emphasizing that "medicine is not as good as food", and "medicine is three-point poison". Naturally, medicine cannot be taken frequently in daily life. Reasonable and healthy diet and combination are a good way to maintain a healthy life. However, in the fast-paced and stressful modern society, it is difficult for people to have time to consult doctors or nutritionists. It is an urgent need for people to get accurate and effective healthy...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06F17/27G06F17/30G06N3/04
CPCG06F16/3344G06F16/367G06F16/9024G06F40/295G06N3/045
Inventor 文贵华胡杨
Owner SOUTH CHINA UNIV OF TECH
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