An automatic error correction method and system based on a knowledge graph

By using knowledge graph-based automatic error correction methods, the problem of traditional error correction techniques being unable to accurately distinguish the importance and suitability of errors in complex texts is solved. This achieves precise quantification of error priority and optimal adaptation of error correction paths, thereby improving the accuracy of error correction and the semantic integrity of the text.

CN121724031BActive Publication Date: 2026-05-29SHANGHAI XIRUAN TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANGHAI XIRUAN TECH CO LTD
Filing Date
2026-02-12
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Traditional automatic error correction technology cannot accurately distinguish the importance of errors when processing complex text, lacks flexibility, resulting in error correction results that do not meet user expectations, and cannot dynamically adapt to diverse contexts and error types.

Method used

The knowledge graph-based automatic error correction method extracts surrounding text fragments with potential errors through lexical semantic association rules and grammatical dependency rules, calculates semantic similarity and association weights, combines domain importance attributes and contextual sentiment analysis to generate a ranking of the degree of error impact, and selects the optimal error correction path through an error correction path library to finally correct the text data.

Benefits of technology

It achieves precise quantitative sorting of error priorities, improves the contextual adaptability and pertinence of error correction, ensures the semantic integrity and expressive coherence of the corrected text, and enhances the adaptability of the automatic error correction system in complex text scenarios.

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Abstract

The application relates to the technical field of text error correction, and discloses an automatic error correction method and system based on a knowledge graph. Based on the lexical semantic association and the syntax dependency rule of the knowledge graph, the surrounding fragments of potential errors of input text are extracted, and semantic similarity is calculated to obtain error association weight data; an influence ranking is generated in combination with the domain importance attribute of the error type, and a to-be-corrected error list is formed after context sentiment tendency analysis adjustment; global semantic representation vectors are constructed based on the knowledge graph, domain categories are recognized, and semantic constraint conditions are generated, a preliminary error correction path is screened, semantic fitness is calculated to determine an optimal path, text correction is completed in combination with a high-priority error correction rule, and a result is output. Through the method, semantic deviation can be effectively avoided, error correction accuracy and pertinence can be improved, the semantic integrity and expression coherence of the corrected text can be ensured, and the problem of insufficient adaptability of traditional error correction methods is solved.
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