Review of key technologies in knowledge graphs powered by code large language models

Traditional knowledge graph technologies still face significant challenges in converting human knowledge, expressed in natural language, into a formal language-based knowledge graph and utilizing it effectively. In recent years, code large language models (LLMs) have demonstrated remarkable capabilities in understanding both natural and formal languages, as well as in translating between them. These advancements are expected to drive significant breakthroughs in developing next-generation knowledge graph technologies. This paper reviews the application of code LLMs in KGs. Firstly, this paper systematically analyzes the role of code LLMs in enhancing key knowledge graph technologies across three critical areas: construction, reasoning, and question-answering. Secondly, a relatively detailed introduction to the existing methodologies in these areas is provided. Finally, this paper summarizes the current state in the field and offers insights into the future of knowledge graph technologies empowered by code LLMs. In the future, knowledge representation based on programming languages is expected to enable more efficient, automated, and complex operations on knowledge graphs, realizing knowledge programming.