Intelligent Upgrade of University Knowledge Graph Platform Empowered by Large Language Models

The emergence of large language models provides a new opportunity for the intelligent leap of knowledge graph platform. However, the theoretical mechanism and practical paths for how large language models can deeply empower the intelligent upgrade of knowledge graph platform remain unclear. Based on this, the paper conducted content coding on the construction methods, generation methods, and functional performances of 11 mainstream knowledge graph platforms, identifying the limitations of current platforms in terms of the accuracy of knowledge extraction, the depth of knowledge integration, and the ability of knowledge reasoning. In response to the aforementioned pain points, this paper explored the directions for enhancing knowledge graph platform services empowered by large language models. On this basis, the paper constructed an intelligent upgrade framework of university knowledge graph platform empowered by large language models from three levels of environmental construction, capability support, and scenario application. Based on this framework, the paper proposed the implementation paths for the deep integration of knowledge graph platform with university education and teaching from the perspectives of different educational subjects such as teachers, students, and administrators, with the aim of providing references for the intelligent construction and high-quality application of knowledge graphs in the era of large language models and helping higher education achieve connotative development in the intelligent era.