KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering
Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, particularly knowledge graph question answering (KGQA). Existing approaches primarily combine LLMs with KGs through retrieval-augmented generation (RAG)-based, agent-based, and SPARQL-based methods. Although these methods have achieved notable success, they still suffer from several limitations, including structural information loss, unfaithful reasoning, and limited flexibility and generalization. To address these challenges, this paper proposes KG2Code, a novel approach that transforms knowledge graphs into a code-...
| Authors | Yike Wu, Nan Hu, Guilin Qi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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