A general knowledge graph dynamic construction and reasoning method and system based on multi-source medical data fusion
By constructing and reasoning a general practice knowledge graph through the fusion and dynamic updating of multi-source medical data, the problem of insufficient interpretability in the fusion, dynamic updating, and reasoning of multi-source heterogeneous data in general practice knowledge graphs is solved, thus realizing efficient and interpretable auxiliary diagnosis and treatment support of general practice knowledge.
Patent Information
- Application Number
- CN202610513814.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-17
- Publication Date
- 2026-07-21
AI Technical Summary
Existing medical knowledge graph construction technologies are ill-suited to the complex needs of general practice medicine, which involves cross-specialty and multi-disease associations. They suffer from weak multi-source heterogeneous data fusion capabilities, lack of dynamic updates, insufficient interpretability of reasoning, and difficulty in adapting to the hierarchical diagnosis and treatment logic of general practitioners.
By employing a multi-source medical data fusion approach, a comprehensive knowledge graph is constructed through data preprocessing, knowledge fusion, dynamic updating, and reasoning steps. This includes entity alignment, conflict resolution, knowledge completion, causal subgraph construction, and a hybrid reasoning engine. This achieves efficient fusion and dynamic updating of multi-source data and generates accurate reasoning results through the combination of symbolic reasoning and deep learning.
It has achieved a knowledge graph that covers all disciplines and has strong timeliness and interpretability, which significantly reduces the workload of manual construction and updating, adapts to the diagnosis and treatment process of general practitioners, and provides continuously optimized auxiliary decision-making capabilities.
Smart Images

Figure CN122436264A_ABST