The task of Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question.For the sake of expensive cost of large-scale question annotation, the methods of KBQG under low-resource scenarios urgently need to be developed.However, current methods heavily rely on annotated data for fine-tuning, which is not well-suited for few-shot question generation.The emergence of Large Language Models (LLMs) has shown their impressive generalization ability in few-shot tasks.Inspired by Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for reasoning, we formulate KBQG task as a reasoning problem, where the generation of a complete question is split into a series of sub-question generation.Our proposed prompting method KQG-CoT first selects supportive logical forms from the unlabeled data pool taking account of the characteristics of the logical form.Then, we construct a task-specific prompt to guide LLMs to generate complicated questions based on selective logic forms.To further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the logical forms by their complexity.We conduct extensive experiments over three public KBQG datasets.The results demonstrate that our prompting method consistently outperforms other prompting baselines on the evaluated datasets.Remarkably, our KQG-CoT+ method could surpass existing fewshot SoTA results of the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4, METEOR, and ROUGE-L, respectively.