Graph Query Language

Graph Query Languages (GQLs) aim to efficiently retrieve information from graph databases, a crucial task across diverse fields. Current research focuses on improving the translation of natural language queries into GQLs (NL2GQL), often leveraging large language models (LLMs) and hybrid model architectures that combine the strengths of smaller and larger models for improved accuracy and reduced hallucinations. These advancements are driven by the need for more robust and user-friendly interfaces to graph databases, impacting various applications from software engineering to knowledge graph querying. Furthermore, research is exploring the integration of more complex query patterns into existing GQLs to handle increasingly heterogeneous graph data and user needs.