Verbalizing knowledge graphs for LLM is the task of representing knowledge graphs as text so that they can be written directly in the prompt, the main input source of LLM. Verbalization consists of finding textual representations for nodes, relationships between nodes, and their metadata. Verbalization can take place at different stages of the LLM lifecycle, during training (pre-training, instruction fine-tuning) or during inference (in-context learning), and consists in:

Mark boundaries of graph data using special tokens, like already for SQL-Queries: Improving Generalization in Language Model-Based Text-to-SQL
Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
Encoding strategies for nodes, relationship between nodes, nodes communities and metadata Talk like a graph: Encoding graphs for large language models (research.google)
What needs to be verbalized and where? System prompt

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for static information like KG-schema, user prompt for data instances

Integrating KGs by Fusion Modules

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Integrating KGs by Fusion Modules