Special disease knowledge graph construction method and device

Abstract

The invention provides a special disease knowledge graph construction method and device. The method comprises the following steps of: on the basis of constructing a special disease knowledge system, extracting medical entity information by utilizing a pre-constructed entity extraction model; extracting attribute information and relation information of the medical entity on the basis of the specialdisease knowledge system; and based on the extracted information, building a knowledge map of the disease, wherein the entity extraction model is a model trained on the basis of a pre-labeled training data set, and the training data set is constructed by using medical word list matching in combination with manual auditing and labeling. When medical entities are extracted, the training data set required by entity extraction model training is constructed by using medical word list matching in combination with manual auditing and labeling, so that the workload of constructing the training data set can be reduced, the consumption of manpower resources and time resources is reduced, and meanwhile, the construction efficiency and knowledge rationality of a special disease knowledge map can alsobe improved.

Description

technical field [0001] The present application belongs to the technical field of natural language processing, and in particular relates to a method and device for constructing a knowledge graph of a specific disease. Background technique [0002] Specialized disease knowledge map, which belongs to medical knowledge map, is a semantic network that describes objectively existing entities, concepts and their relationships in a certain disease field in medicine. It uses semantic technology to express systematization, structure, and integration. The knowledge in the medical field can be applied to various aspects such as disease screening and prediction, auxiliary clinical diagnosis, medical insurance risk prediction and medical knowledge popularization in the medical field. [0003] In order to have a better application in the medical field, it is very necessary to accurately construct the required specialized disease knowledge map. The construction of the medical knowledge map ...

AI Technical Summary

Problems solved by technology

At present, when constructing a knowledge map of a specific disease, in the information extraction stage, the entity extraction model is generally constructed by manually labeling the data set to realize the extraction of entity information. low defects, and this method also has high requirements for the medical background of the labeling personnel. At the same time, the quality of the labeling results is often difficult to guarantee, correspondingly, it will have a negative impact on the training of the entity extraction model, and it is difficult to guarantee the accuracy of the entity recognition. Affects the knowledge rationality of the constructed disease knowledge map

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