Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method and system for constructing semantic knowledge base based on graph neural network

A semantic knowledge base and neural network technology, applied in the field of semantic knowledge base construction based on graph neural network, can solve the problems of insufficient multi-level semantic knowledge base and weak automation level, so as to improve logic and information integrity, and expand interpretation range effect

Active Publication Date: 2022-02-18
江苏省档案馆 +1
View PDF3 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the deficiencies in the prior art, the purpose of this application is to solve the problem of constructing a multi-level semantic knowledge base from a large-scale corpus in the prior art by providing a method and system for constructing a semantic knowledge base based on a graph neural network. The technical problems that are not perfect and the automation level is weak, have achieved the technical effect of automatically building a multi-level semantic knowledge base by automatically building a semantic knowledge network with things as nodes and relationships between things as edges

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method and system for constructing semantic knowledge base based on graph neural network
  • Method and system for constructing semantic knowledge base based on graph neural network
  • Method and system for constructing semantic knowledge base based on graph neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0029] Such as figure 1 As shown, the embodiment of the present application provides a method for constructing a semantic knowledge base based on a graph neural network, the method comprising:

[0030] Step S100: obtaining the first large-scale corpus information;

[0031] Specifically, the first large-scale corpus information is information obtained from a corpus. The corpus includes a variety of corpus collection types. By setting the corpus object domain, the corpus information corresponding to a larger corpus level is obtained, wherein , the corpus stores the language materials that have appeared in the actual use of the language. Through the processing and analysis of the electronic text library, with the help of the corpus, relevant language theories and applications can be carried out. The information is screened through the corpus to ensure the accuracy and validity of the first large-scale corpus information acquisition.

[0032] Step S200: The NLP technology-based ...

Embodiment 2

[0108] Based on the same inventive concept as the method for constructing a semantic knowledge base based on a graph neural network in the foregoing embodiments, the present invention also provides a system for constructing a semantic knowledge base based on a graph neural network, such as Figure 6 As shown, the system includes:

[0109] A first obtaining unit 11, the first obtaining unit 11 is used to obtain the first large-scale corpus information;

[0110] The second obtaining unit 12, the second obtaining unit 12 is used for the thing extractor based on NLP technology to perform textual thing recognition and extraction from the first large-scale corpus information, and obtain the first textual thing set;

[0111] A first construction unit 13, the first construction unit 13 is used to construct a thing hierarchical network;

[0112] The second construction unit 14, the second construction unit 14 is configured to perform semantic meta-analysis on each thing in the first t...

Embodiment 3

[0143] Refer below Figure 7 To describe the electronic device of this application.

[0144] Figure 7 A schematic structural diagram of an electronic device according to the present application is shown.

[0145] Based on the inventive concept of a method for constructing a semantic knowledge base based on a graph neural network in the foregoing embodiments, the present invention also provides a system for constructing a semantic knowledge base based on a graph neural network, on which a computer program is stored, and the program is processed The steps of implementing any method of the system for constructing a semantic knowledge base based on a graph neural network described above when the controller is executed.

[0146] Among them, in Figure 7 In, bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 will include one or more processors represented by processor 302 and various types of memory represented ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method and system for constructing a semantic knowledge base based on a graph neural network. The method comprises the steps of first large-scale corpus information is obtained; an object extractor based on the NLP technology performs text object recognition and extraction from the first large-scale corpus information to obtain a first text object set; an object level network is constructed; semantic element analysis is performed on each object in the first text object set according to the object hierarchical network to construct a first semantic element set; the first semantic element set is networked by using a graph neural network to obtain a first semantic knowledge network; reinforcement learning is performed on the first semantic knowledge network to obtain a second semantic knowledge network; and the second semantic knowledge network is output as a semantic knowledge base. The technical problems that in the prior art, construction of a multi-level semantic knowledge base from a large-scale corpus is not perfect, and the automation level is low are solved.

Description

technical field [0001] The invention relates to the field of semantic recognition, in particular to a method and system for constructing a semantic knowledge base based on a graph neural network. Background technique [0002] At present, with the deepening of Internet technology and the continuous development of artificial intelligence technology, some computing methods have been formed in language understanding, and semantic knowledge bases are widely used in various fields as the medium and carrier for understanding text content, thereby improving computer performance. The degree of intelligence, and a complete semantic knowledge base can make the data processing of the computer more intelligent. As an important part of the language processing system for the development of computer intelligence, the construction of a semantic knowledge base is still a research hotspot. [0003] However, there are technical problems in the prior art that the construction of a multi-level se...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06F40/30G06N3/08G06F16/35
CPCG06F40/30G06N3/08G06F16/35
Inventor 邹华姚军王楠丁原徐志国宋永生李军郭晓华周红
Owner 江苏省档案馆
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com