Digital archive multi-modal data semantic enhancement fusion retrieval method and system

By constructing a policy cycle timeline and a terminology evolution map, identifying seal combination patterns and authority levels, and generating an evolution record of authority validity, the problem of retrieval misjudgment caused by the historical evolution of seal authority was solved, and the accuracy and recall rate of archive retrieval were improved.

CN122019797APending Publication Date: 2026-05-12MID-RANGE INFORMATION (GUANGDONG) CO LTD
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Patent Information

Application Number
CN202610104870.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-26
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Existing technologies cannot accurately determine the legal validity of seal authority in different historical periods when processing document retrieval, and cannot match historical documents with obsolete terminology, leading to missed detections and misjudgments.

Method used

By constructing a policy cycle timeline and terminology evolution map, we can identify seal combination patterns and authority levels, generate records of authority validity evolution, and fuse temporal authority feature vectors with content semantic vectors to achieve multimodal representation vectors for query expansion and filtering.

Benefits of technology

Accurately determine the legal effect of the same seal combination in different historical policy cycles, cover historical archives using obsolete terminology, and improve retrieval accuracy and recall rate.

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Abstract

The invention relates to the technical field of digital archive management and information retrieval, and discloses a digital archive multi-modal data semantic enhancement fusion retrieval method and system.The method comprises the steps that a policy cycle time axis and a policy term evolution graph are constructed, tense logical reasoning is conducted on archive seals, and permission effectiveness evolution is derived; the temporal permission feature vector and the content semantic vector are fused to generate a multi-modal representation vector, and cross-policy-cycle semantic enhancement retrieval is realized by combining query expansion and temporal permission filtering, so that the problems of missing detection and misjudgment of policy and regulation archives in seal permission historical evolution and term cross-cycle retrieval are solved.
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