Knowledge Graph Completion Technology and Its Application in Mining Traditional Chinese Medicine Classics
YU Jiatian
TONG Xu
TONG Lin
TANG Zhuhui
ZHANG Yupeng
ZHANG Huamin
Abstract：With the widespread application of knowledge graphs(KGs),the challenge of knowledge incompleteness during their construction has garnered significant research attention,positioning knowledge graph completion(KGC)as a pivotal frontier in artificial intelligence research.KGC methodologies automatically identify and supplement missing entities and relationships to enhance KG integrity and precision.This paper systematically categorizes mainstream KGC approaches,including traditional statistical models,deep learning architectures,graph neural networks,reinforcement learning frameworks,and meta-learning strategies,with critical analyses of their technical characteristics and scenario-specific applicability.Furthermore,we pioneer the exploration of KGC's transformative role in traditional Chinese medicine(TCM)Classics mining.The proposed intelligent completion mechanism synergistically integrates fragmented herbal knowledge from historical archives,facilitates modern reinterpretation of TCM theories through cognitive computing,and enables cross-modal knowledge fusion for intelligent inheritance of medical wisdom.Our research establishes a methodological foundation for systematizing TCM knowledge while providing an AI-driven paradigm for revitalizing ancient medical heritage.
Keywords：Knowledge graphKnowledge graph completionTraditional Chinese medicineClassical text mining
Publication Date：2025-08-28
Online Publishing Date：2025-08-28(First online date of this platform, not the publication date of the document)
Pages：8( 1339-1346 )