Applications of knowledge graph in medical and financial fields: Data integration and intelligent decision-making from an interdisciplinary perspective

Abstract

With the rapid advancement of AI and deep learning technologies, knowledge graphs have emerged as a key technology for improving the performance of intelligent decision-making systems and driving interdisciplinary innovation. This article outlines the core principles and structure of knowledge graphs, including how they construct knowledge networks to support complex queries and intelligent reasoning. It reviews their innovative applications in the healthcare and financial industries, emphasizing their significant roles in data integration, decision support, and risk assessment. In the healthcare domain, knowledge graphs contribute to improving the accuracy of medical diagnoses, accelerating drug discovery, and enabling intelligent semantic searches. In the financial sector, they optimize risk management and aid in fraud prevention. The article also looks ahead to the future potential of knowledge graphs, stressing the importance of interdisciplinary collaboration and technological innovation in their development. It aims to provide valuable references for further research and application of knowledge graphs.

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