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

A construction method of a knowledge map question answering system in the field of electric power communication based on deep learning

A technology of electric power communication and domain knowledge, which is applied in the application field of knowledge map in the electric power communication industry, and can solve problems such as difficulty in building Chinese knowledge base and complicated construction process

Inactive Publication Date: 2019-01-25
WUHAN UNIV
View PDF1 Cites 76 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Compared with the English question answering system, the construction process of the Chinese question answering system is more complicated, because the construction of a large-scale Chinese knowledge base is very difficult

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
  • A construction method of a knowledge map question answering system in the field of electric power communication based on deep learning
  • A construction method of a knowledge map question answering system in the field of electric power communication based on deep learning
  • A construction method of a knowledge map question answering system in the field of electric power communication based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0065] Suppose we represent the natural language question posed by the user as q = ω 1 ... ω n , denote all candidate answers to the question as an answer candidate set C q , the working principle of the common question answering system based on deep learning is as follows figure 2 shown. The specific implementation of the question answering algorithm of the question answering system based on deep learning is as follows:

[0066] Step 1: Semantic parsing is the initialization stage of the question answering algorithm, that is, the preprocessing process of the natural language question q. We know that the grammatical structure of Chinese is more complicated than that of English, so it is necessary to perform word segmentation and part-of-speech tagging on the text, so as to retrieve the keywords of the question and the focus of attention of the question.

[0067] Step 1.1 Question segmentation and part-of-speech tagging

[0068] Chinese text word segmentation takes "words...

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 relates to a construction method of a knowledge map question answering system in the field of electric power communication based on deep learning, which comprises the following steps: Step 1, semantic analysis, that is, preprocessing a question q put forward by a user in a natural language, extracting a keyword of a user inquiry, a focus of a query sentence and the like as an entityw of a question sentence from the question q; Step 2: semantic expression, namely, vectorization of the natural language question after preprocessing, and vectorization of the candidate set of answera, are used to calculate the matching degree of the question q and the answer a; and Step 3, the answer a most matched with the question q and most accurate is found through the method of semantic matching degree calculation, query and inference, so that the score S (q, a) of the question-answer pair (q, a) is the highest. The invention studies the feasibility of the question answering system constructed by the knowledge map in the field of communication of the State Grid.

Description

technical field [0001] The invention belongs to the application category of knowledge graphs in the electric power communication industry, and in particular relates to a construction method of a knowledge graph question answering system (Knowledge base question answering) based on deep learning. Background technique [0002] Knowledge Graph: In essence, it is a structured semantic knowledge base formed by linking entities with attributes through relationships. It contains a large number of entity-to-relationships, which are used to describe concepts in the physical world and the interrelationships between concepts in symbolic form. From the perspective of the graph, the knowledge graph is actually a conceptual network. The nodes of the network are entities in the real world, and the edges of the network represent the connections (relationships) between entity pairs. [1] . [0003] The concept of knowledge graph was first proposed by Google in 2012 to enhance the knowledge ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06F16/33G06F16/36
Inventor 李石君马旭强杨济海余伟余放李宇轩
Owner WUHAN UNIV
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