Chinese medical knowledge atlas construction method based on deep learning

Abstract

The invention relates to the technology of a knowledge atlas, and aims to provide a Chinese medical knowledge atlas construction method based on deep learning. The Chinese medical knowledge atlas construction method comprises the following steps: obtaining relevant data of a medical field from a data source; using a word segmentation tool to carry out word segmentation on unstructured data, and using an RNN (Recurrent Neural Network) to finish a sequence labeling task to identify entities related to medical care, so as to realize the extraction of knowledge units; carrying out feature vector construction on the entity, and utilizing the RNN to carry out sequence labeling and finish the identification of a relationship among the knowledge units; carrying out entity alignment, and then utilizing the extracted entities and the relationship between the entities to construct the knowledge atlas. According to the Chinese medical knowledge atlas construction method, a recurrent neural network is artfully used for extracting the knowledge units and identifying the relationship among the knowledge units so as to favorably finish the processing of the unstructured data. According to the Chinese medical knowledge atlas construction method, features suitable for the medical care field are put forward to carry out a training task of a network. Compared with general features, the features put forward by the method can better represent a medical entity, and therefore, the relationship among the extracted knowledge units can be more accurate and comprehensive.

Description

technical field [0001] The present invention relates to knowledge map technology, in particular to a method for constructing a Chinese medical knowledge map based on deep learning. Background technique [0002] As more and more semantic WWW data is opened on the Internet, various Internet search engine companies at home and abroad have begun to build knowledge graphs based on this to improve service quality, such as Google Knowledge Graph (Google Knowledge Graph), Baidu "Zhixin" and so on. Knowledge Graph is essentially a semantic network. Its nodes represent entities or concepts, and edges represent various semantic relationships between entities or concepts. It is a service model of knowledge management, which can connect trivial and scattered knowledge in various fields to form a huge, networked knowledge system built with the "semantic network" as the skeleton. Now, people have begun to apply knowledge graphs to intelligent systems such as comprehensive knowledge retri...

AI Technical Summary

Problems solved by technology

Invention 3 Although the word clustering model of deep learning is used to complete the task of extracting entity relations, it is only aimed at the news field, and there are relatively few entity relations

For the medical field with many entities and entity relationships, the processing of contextual relationships is also lacking, so this model is not applicable

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