The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder-decoder architectures have advanced traditional KGQG approaches. However, these approaches encounter challenges in achieving question diversity and grammatical accuracy. They often suffer from a disconnect between the phrasing of the question and the type of the answer entity, a phenomenon known as semantic drift. To address these challenges, we introduce LEMON, a knowledge-enhanced, type-constrained, and grammar-guided model for KGQG. LEMON enhances the input by integrating entity-related knowledge using heuristic rules, which fosters diversity in question generation. It employs a hierarchical global relation embedding with translation loss to align questions with entity types. In addition, it utilizes a graph-based module to aggregate type information from neighboring nodes. The LEMON model incorporates a type-constrained decoder to generate diverse expressions and improves grammatical accuracy through a syntactic and semantic reward function via reinforcement learning. Evaluations on benchmark datasets demonstrate LEMON's strong competitiveness. The study also examines the impact of question generation quality on question-answering systems, providing guidance for future research endeavors in this domain.