Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250900023-9.doi: 10.11896/jsjkx.250900023

• Artificial Intelligence • Previous Articles     Next Articles

Design and Application of Semantic Model for Medical Record Knowledge Graph Querying

CHU Xiaolong, DU Jinlian, LUO Fangyuan, JIN Xueyun   

  1. College of Computer Science,Beijing University of Technology,Beijing 100124,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:CHU Xiaolong,born in 2001,postgraduate.His main research interests include semantic model design and so on.
    DU Jinlian,born in 1972,Ph.D,associate professor,master's supervisor.Her main research interests include database,data analysis and visualization,and software and theory.
  • Supported by:
    Project of Construction and Support for High-level Teaching Teams of Beijing Municipal Institutions(BPHR20220201).

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Abstract: Cypher language is widely applied in knowledge graph querying.However,it exhibits inherent limitations:insufficient semantic support in specific-domain scenarios and cumbersome expression of complex queries,which are unfavorable for the design of query engines.By analyzing the characteristics of clinical diagnosis and treatment query application based on medical record knowledge graph,a query meta operation for medical record(MR) knowledge graph and a query application semantic model generated by the combination of these meta operations are designed.Combined into a hierarchical semantic model,these meta-ope-rations provide a clear and structured way to represent clinical queries.This model serves as an effective logical-layer abstraction for query engines,leveraging a production rule system to efficiently convert queries into Cypher execution plans.The rules define direct mappings from the model's constructs to Cypher syntax,enabling automatic translation of high-level query intentions into executable statements.Experiments show that this semantic model performs well in terms of expressiveness and accuracy for queryapplications oriented to the medical record knowledge graph,as well as in the operational robustness of the query engine built on it.

Key words: Medical record knowledge graph, Meta-operation, Query semantic model, Cypher language

CLC Number: 

  • TP391
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