Data labeling method and device based on knowledge graph, medium and equipment

Abstract

The embodiment of the invention discloses a data labeling method and device based on a knowledge graph, a medium and equipment. The method comprises the following steps: in response to a creation request of a labeling task, determining a graph ontology model associated with the labeling task, wherein the graph ontology model is used for constructing entity attributes and a relationship between entities; obtaining a tagged target document, and determining tagged item data according to the graph ontology model; according to the annotation item data, determining a training document with an annotation to train the pre-training model, and if an output result of the pre-training model in the training process accords with an acceptance standard, obtaining a standard model; and adopting the standard model to label the entity attributes of the labeled target document and the relationship between the entities. By adopting the technical scheme of the invention, the knowledge graph can be combined with the annotation data, so that the visualization of the annotation data is realized, the data can be reflected more clearly and intuitively, and meanwhile, the data annotation efficiency is improved.

Description

technical field [0001] The embodiments of the present application relate to the technical field of data processing, and in particular to a knowledge graph-based data labeling method, device, medium, and equipment. Background technique [0002] With the rapid development of deep learning methods, a new data processing method based on deep learning has emerged, which makes people put forward higher requirements for the accuracy and efficiency of data processing. [0003] In the process of data processing, the demand for data labeling also increases. The existing labeling methods can only rely on the experience of labelers, and use manual labeling to label data, so automatic data labeling cannot be achieved. [0004] However, the existing manual labeling methods are limited by the experience and work efficiency of the labelers, which leads to low efficiency of data labeling and the inability to realize data visualization. Contents of the invention [0005] Embodiments of th...

AI Technical Summary

Problems solved by technology

The existing labeling methods can only rely on the experience of labelers, and use manual labeling to label data, so automatic data labeling cannot be achieved

[0004]However, the existing manual labeling methods are limited by the experience and work efficiency of the labelers, resulting in low efficiency of data labeling, and at the same time, it is impossible to realize data visualization

Images