A method, system, storage medium and computer equipment for big data label conflict detection based on semantic graph

Abstract

The invention belongs to the technical field of big data tags, and relates to a big data tag conflict detection method and system based on a semantic map, a storage medium and computer equipment. Themethod comprises the following steps: constructing a semantic map comprising synonym and antonym types; configuring a corresponding label conflict processing strategy; traversing a label set existingin the current system based on the semantic map, and searching for mutually exclusive labels and overlapped labels corresponding to all labels to obtain a label conflict rule set; detecting the labelconflict result according to the label conflict rule set to obtain a label conflict result data set; and performing label conflict processing on the label conflict result data set according to a labelconflict processing strategy to complete label updating. According to the method, the semantic map is constructed, the label conflict processing strategy is configured, the label conflict rule set isgenerated, label conflicts are automatically detected and processed when the labels are updated, and the accuracy of label results is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of big data tags, in particular to a semantic map-based big data tag conflict detection method, system, storage medium and computer equipment. Background technique [0002] After the advent of the big data era, tags have been widely used in scenarios such as enterprise precision marketing, high-quality service, and lean management, and have become a necessary means to support the digital transformation of enterprises. However, with the promotion of internal label applications and the enrichment of the label system, the consistency of the label system will face challenges due to problems such as wrong label rules, irregular label management, and data anomalies. The same business object portrait is prone to label overlap and Conflicts and other situations, the accuracy and application effect of the label cannot be effectively guaranteed. [0003] Currently, in the field of label conflict detection, conflict d...

AI Technical Summary

Problems solved by technology

However, with the promotion of internal label applications and the enrichment of the label system, the consistency of the label system will face challenges due to problems such as wrong label rules, irregular label management, and data anomalies. The same business object portrait is prone to label overlap and Conflicts and other situations, the accuracy and application effect of the label cannot be effectively guaranteed

[0003] Currently, in the field of label conflict detection, conflict detection is mainly detected and processed by manually configuring conflict rules. The method of manually configuring conflict rules is not only labor-intensive, but also prone to omissions, which affects the accuracy and real-time performance of label conflict detection, causing enterprises to After the label is applied for a period of time, the usage effect continues to decline

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