A knowledge graph construction method and system for personalized health consultation

By generating user feature vectors and dynamically updating knowledge subgraphs, the problems of low reasoning efficiency and inflexible data management in personalized health consultation are solved, and efficient, safe, and interpretable personalized health consultation services are achieved.

CN122334423APending Publication Date: 2026-07-03JIANGSU MAYTECH MEDICAL TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JIANGSU MAYTECH MEDICAL TECH CO LTD
Filing Date
2026-04-07
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing technologies in personalized health consultation suffer from problems such as low efficiency of global knowledge graph reasoning, inflexible management of dynamic health data, and lack of reasoning basis for consultation answers.

Method used

By generating user feature vectors, filtering user-specific knowledge subgraphs, combining dynamic health data to generate personalized fusion knowledge graphs, and performing multi-hop reasoning on the personalized fusion knowledge graphs to generate personalized consultation answers, while dynamically updating user-specific knowledge subgraphs.

Benefits of technology

It improves reasoning efficiency, enables flexible integration of static knowledge and real-time data, protects user privacy and security, enhances the interpretability and credibility of services, and ensures the accuracy of personalized services.

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Abstract

This invention discloses a method and system for constructing a knowledge graph for personalized health consultation, relating to the field of medical information processing technology. The method includes: acquiring user static profile data and generating user feature vectors; based on a global medical knowledge graph, calculating node relevance scores using user feature vectors to filter and generate a user-specific knowledge subgraph; acquiring user dynamic health data, converting it into temporary nodes, and establishing associations with the specific knowledge subgraph to generate a personalized fusion knowledge graph; receiving user consultation questions and performing multi-hop reasoning on the personalized fusion knowledge graph to obtain reasoning paths; generating personalized consultation answers based on the reasoning paths and answer templates; removing temporary nodes after the session ends, and updating the specific knowledge subgraph when the user's static profile changes. This invention constructs a personalized knowledge graph by fusing user static features and dynamic data, achieving accurate and efficient health consultation services, significantly improving the personalization of answers and reasoning efficiency.
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