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Medical automatic question-answering system construction method based on knowledge graph

A knowledge graph, automatic question answering technology, applied in neural learning methods, medical data mining, computer-aided medical procedures, etc., can solve problems such as lack of interpretability

Inactive Publication Date: 2020-12-11
汪礼君
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AI Technical Summary

Problems solved by technology

Many current studies use deep neural networks as classifiers, but this classifier is a "black box" that lacks sufficient interpretability

Method used

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  • Medical automatic question-answering system construction method based on knowledge graph
  • Medical automatic question-answering system construction method based on knowledge graph
  • Medical automatic question-answering system construction method based on knowledge graph

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Embodiment Construction

[0061] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0062] By defining the entities, relationships and attributes in the knowledge map in the medical field, and using the automatic data labeling algorithm to mark the data in the medical field, and using the neural network model to complete and construct the knowledge map, based on the knowledge map constructed Automated question answering for the medical domain of learning. refer to figure 1 As shown, it is a schematic diagram of a construction method of a medical automatic question answering system based on a knowledge map provided by an embodiment of the present invention.

[0063] In this embodiment, the construction method of the medical automatic question answering system based on the knowledge map includes:

[0064] S1. Predefine the entities, relationships and attributes in the knowledge map in the medical fie...

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Abstract

The invention relates to the technical field of automatic question answering, and discloses a medical automatic question-answering system construction method based on a knowledge graph, which comprises the following steps of: pre-defining entities, relationships and attributes in a medical field knowledge graph; acquiring medical field data, and labeling the medical field data by using a data automatic labeling algorithm according to the predefined medical field knowledge graph; utilizing a knowledge graph construction model to construct a knowledge graph for the annotation data to obtain a medical field knowledge graph; encoding a dialogue text of a user by using a dialogue encoder to obtain dialogue text encoded data; and performing path inference on the text encoded data by utilizing aknowledge graph-based reinforcement learning network to obtain an intention path of the user, and taking a knowledge graph result pointed by the intention path as an automatic question-answering result. The invention further constructs a medical automatic question-answering system based on a knowledge graph. Automatic question answering in the medical field is realized.

Description

technical field [0001] The present invention relates to the technical field of automatic question answering, in particular to a method for constructing a medical automatic question answering system based on a knowledge graph. Background technique [0002] The rapid development of the Internet has brought about an explosive growth of information. Moreover, there are various sources of information, and the lack of a unified structure also brings about disordered information, making it impossible for people to quickly find useful information. With the continuous update and iteration of artificial intelligence technology, people expect computers to be able to understand their needs like humans and quickly and accurately help find useful information for people, thus triggering people's interest in a new data storage method - the knowledge map Research boom. [0003] Existing technologies based on knowledge storage have explored feature engineering methods based on different sem...

Claims

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Application Information

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IPC IPC(8): G16H50/70G06F40/211G06F40/247G06F40/284G06F40/295G06N3/08
CPCG06N3/08G16H50/70G06F40/211G06F40/247G06F40/284G06F40/295
Inventor 汪礼君
Owner 汪礼君
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