Data processing method, device and equipment and medium

Abstract

Embodiments of the invention provide a data processing method, device and equipment and a medium, and the method relates to an artificial intelligence technology, can be applied to the field of natural language processing, and comprises the steps of obtaining a target text and a standard text, and generating a target entity sub-graph corresponding to the target text and a standard entity sub-graphcorresponding to the standard text according to a knowledge graph, wherein a first entity in the target entity subgraph and a second entity in the standard entity subgraph belong to entities in the knowledge graph; according to the first entity and the second entity, generating a target graph structure feature corresponding to the target entity sub-graph and a standard graph structure feature corresponding to the standard entity sub-graph; and determining graph similarity between the target entity sub-graph and the standard entity sub-graph according to the target graph structure features andthe standard graph structure features, wherein the graph similarity is used for indicating the association degree between the target text and the standard text. By adopting the embodiment of the invention, the matching accuracy between the target text and the standard text can be improved.

Description

technical field [0001] The present application relates to the technical field of the Internet, and in particular to a data processing method, device, equipment and medium. Background technique [0002] With the continuous development of artificial intelligence technology, natural language processing technology has gradually become one of the important fields in the field of artificial intelligence, showing great role and potential in search, translation, recommendation and other directions. In the medical field, there are a large number of different types of medical text data such as medical records, medical textbooks, and medical questions and answers. During the processing of medical text data (such as medical record analysis), it is first necessary to structurally process the medical text data to extract medical information. Useful information in text data (such as case name, case symptoms, etc.). [0003] In the existing text structuring, entities matching the medical t...

AI Technical Summary

Problems solved by technology

However, there will be great differences when different users describe medical text data. For example, the medical text data written by a doctor is "abdominal pain", while the medical text data output by a patient may be "stomach pain". If there is a change, it is difficult to match the correct entity in the knowledge graph, which will cause the matching accuracy of medical text data in the knowledge graph to be too low

Images