Traditional ontologies excel at describing domain structure but cannot generate novel artifacts. Large language models generate fluently but produce outputs that lack structural validity, hallucinating mechanisms without components, goals without end conditions. We introduce Generative Ontology, a framework that synthesizes these complementary strengths: ontology provides the grammar; the LLM provides the creativity. Generative Ontology encodes domain knowledge as executable Pydantic schemas that constrain LLM generation via DSPy signatures. A multi-agent pipeline assigns specialized roles to different ontology domains: a Mechanics Architect designs game systems, a Theme Weaver integrates narrative, a Balance Critic identifies exploits. Each agent carrying a professional "anxiety" that prevents shallow, agreeable outputs. Retrieval-augmented generation grounds novel designs in precedents from existing exemplars, while iterative validation ensures coherence between mechanisms and components. We demonstrate the framework through GameGrammar, a system for generating complete tabletop game designs. Given a thematic prompt ("bioluminescent fungi competing in a cave ecosystem"), the pipeline produces structurally complete, playable game specifications with mechanisms, components, victory conditions, and setup instructions. These outputs satisfy ontological constraints while remaining genuinely creative. The pattern generalizes beyond games. Any domain with expert vocabulary, validity constraints, and accumulated exemplars (music composition, software architecture, culinary arts) is a candidate for Generative Ontology. We argue that constraints do not limit creativity but enable it: just as grammar makes poetry possible, ontology makes structured generation possible.

翻译：传统本体论擅长描述领域结构，但无法生成新颖的产物。大语言模型能够流畅生成内容，但其输出缺乏结构有效性，常产生无组件的机制、无终止条件的目标等幻觉内容。我们提出生成式本体论框架，旨在综合这两者的互补优势：本体论提供语法规则，大语言模型提供创造力。该框架将领域知识编码为可执行的Pydantic模式，通过DSPy签名约束大语言模型的生成过程。我们设计的多智能体流程为不同本体论领域分配专门角色：机制架构师负责设计游戏系统，主题编织者整合叙事元素，平衡性评审员识别设计漏洞。每个智能体携带专业"焦虑"机制，以避免产生浅薄迎合的输出。检索增强生成技术将新颖设计锚定在现有范例的先例中，而迭代验证则确保机制与组件间的协调性。我们通过GameGrammar系统展示了该框架的实际应用——这是一个能够生成完整桌面游戏设计的系统。给定主题提示（如"洞穴生态系统中发光真菌的生存竞争"），该流程可生成结构完整、可直接游玩的游戏规范，包含机制设计、组件配置、胜利条件和设置说明。这些输出既满足本体论约束，又保持真正的创造性。该模式可推广至游戏领域之外。任何具有专业术语体系、有效性约束和积累范例的领域（如音乐创作、软件架构、烹饪艺术）都是生成式本体论的潜在应用场景。我们认为约束不会限制创造力，反而能使其成为可能：正如语法规则使诗歌创作成为可能，本体论使结构化生成成为可能。