Enterprise associated person identity recognition and relation graph construction method and system

By using a multi-channel candidate recall engine and a multi-dimensional evidence weighting model, the problem of accuracy in identifying people with the same name was solved, and efficient and automated relationship graph construction was achieved, reducing the false positive rate and ensuring the timeliness of the graph.

CN121658664APending Publication Date: 2026-03-13SUZHOU XINGE TECH CO LTD
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Patent Information

Application Number
CN202511799627.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-02
Publication Date
2026-03-13

AI Technical Summary

Technical Problem

In the context of big data in business, existing technologies struggle to efficiently and accurately identify individuals with the same name and construct relationship graphs, resulting in a high rate of misjudgment and failing to meet the needs of risk control and business intelligence.

Method used

A multi-channel candidate recall engine is adopted, combined with multi-dimensional evidence weighting and similarity calculation model. Through strong correlation information, corporate equity network and core collaboration network fingerprint channels, people with the same name are identified, and the relationship graph is updated through graph database node merging operation.

Benefits of technology

It achieves high-precision and automated identification of people with the same name, reduces the false positive rate, ensures the consistency and timeliness of the relationship graph, and is adaptable to massive data processing.

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Abstract

The invention relates to the technical field of data processing, in particular to an enterprise associated person identity recognition and relation graph construction method and system. The method comprises the following steps: accessing entity data of a person to be identified and enterprise data associated with the entity data from a heterogeneous data source; through a multi-channel candidate recall engine, a candidate person and object entity set with the same name as the to-be-recognized person is retrieved from the relation graph; for each candidate person, calculating a comprehensive similarity score between the candidate person and the person to be identified by using a multi-dimensional evidence weighting and similarity calculation model; according to a comparison result of the comprehensive similarity score and a preset threshold value, judging whether the to-be-recognized person and the candidate person are the same entity or not; if it is judged that the to-be-recognized persons are the same entity, a graph database node merging operation is triggered, the relation associated with the to-be-recognized persons is migrated to existing candidate person nodes, redundant nodes are deleted, and a relation graph is updated; therefore, the problem of figure identity disambiguation can be automatically and intelligently solved with high precision.
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