Research And Implementation Of Medical Knowledge Graph Representation Learning And Cognitive Reasoning Model

This paper discusses the intelligent diagnosis system based on medical cognitive graph,and analyzes the application status and existing problems of knowledge representation learning model,complex logic reasoning model and intelligent diagnosis system.In the medical field,one of the main problems faced by knowledge representation learning is the diversity and ambiguity of medical knowledge.In terms of knowledge representation,more flexible and diversified representation forms need to be developed to better represent medical knowledge.In terms of complex logical reasoning,the model needs to be able to handle knowledge representations containing multiple logical operations,and to complete incomplete knowledge.Intelligent diagnostic systems can help doctors diagnose and treat patients more quickly and accurately,but they face multiple challenges in medical scenarios.Aiming at these problems,this paper proposes a series of solutions.First,this paper introduces quantum computing into the field of knowledge graph learning and proposes a quantum-based knowledge embedding representation method QubitE.Through the quantum embedding of entities and relationships,medical knowledge can be more flexibly represented.This is a parametric approach that utilizes complex matrix multiplication and kernel methods for optimization.The method has linear computational complexity,inclusiveness,full expressiveness,and a good ability to model relations with complex reasoning patterns.Second,this paper introduces fuzzy logic and proposes a complex logic reasoning model FLEX based on logic embedding representation.This model can handle a variety of logical operations and fuzzy semantic reasoning,can identify different contexts,and complete the reasoning of incomplete knowledge.Finally,an intelligent diagnosis system based on medical cognitive maps is constructed by using the inference rule interpreter built on the basis of the above work.The theoretical analysis and experiments in this paper show that these methods have good results and performance in the representation and reasoning involving complex medical concepts.This work has positive significance for the development of intelligent diagnosis systems and knowledge representation learning models in the medical field.These methods can be widely used in the medical field to help doctors better diagnose and treat patients,improve medical efficiency and patient treatment quality.At the same time,these methods also provide new ideas and directions for the research of knowledge representation learning models.Through these research works,we can better understand and process knowledge and data in the medical field,and promote the development of medical intelligence.