  Despite their competitive performance on knowledge-intensive tasks, large
language models (LLMs) still have limitations in memorizing all world knowledge
especially long tail knowledge. In this paper, we study the KG-augmented
language model approach for solving the knowledge graph question answering
(KGQA) task that requires rich world knowledge. Existing work has shown that
retrieving KG knowledge to enhance LLMs prompting can significantly improve
LLMs performance in KGQA. However, their approaches lack a well-formed
verbalization of KG knowledge, i.e., they ignore the gap between KG
representations and textual representations. To this end, we propose an
answer-sensitive KG-to-Text approach that can transform KG knowledge into
well-textualized statements most informative for KGQA. Based on this approach,
we propose a KG-to-Text enhanced LLMs framework for solving the KGQA task.
Experiments on several KGQA benchmarks show that the proposed KG-to-Text
augmented LLMs approach outperforms previous KG-augmented LLMs approaches
regarding answer accuracy and usefulness of knowledge statements.
