A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning

Developments The author spropose prompt base KG foundation model using ind-context learning, called KG_ICL. The result yield suniversal reasoning with query-related examples and facts. They use a tokenizer to map entities and relations in prompt graphs to predefined tokens. They show that the method outperforms baselines and it enables generalization and knowledge transfer across diverse KGs.