KG-LLM-MCom:A Survey on Integration of Knowledge Graph and Large Language Model
The rise of the Large Language Model(LLM)has attracted wide attention in Natural Language Processing,and its emergent ability has also made some progress in various vertical fields(such as finance,healthcare,education,etc.).However,LLM faces numerous challenges,including inadequate interpretation,limited real-time knowledge,and the potential presence of false information in generated outcomes.In order to address these problems,integrating Knowledge Graphs(KG)and LLM has gradually become a research hotspot.As a structured knowledge model,KG has become a powerful tool for improving the interpre-tation and reasoning ability of LLM due to its authenticity and reliability.At the same time,LLM has the ability to understand se-mantics,which supports the construction and update of knowledge graphs.Therefore,KG and LLM are mutually complementary(KG-LLM-MCom is called in this article).The article provides a systematic introduction to KG and LLM integration methods.It conducts a comprehensive review and analysis from two perspectives:1)LLM-enhanced KG and 2)KG-enhanced LLM.Finally,the article introduces the field applications of KG-LLM-MCom from the viewpoint of Medical Diagnosis prediction and Temporal Knowledge Graphs.It discusses KG-LLM-MCom's future development direction,which can provide help for further research on KG and LLM.
large language modelknowledge graphnatural language processing
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