Abstract
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.
Abstract (translated)
传统的本体（Ontologies）擅长描述领域结构，但无法生成新的创意作品。大型语言模型则能够流畅地创作内容，但其产出往往缺乏结构性的有效性，会凭空创造出没有组成部分的机制或没有明确结束条件的目标。为此，我们引入了一种名为“生成式本体”(Generative Ontology) 的框架，该框架融合了这两者的优势：本体提供了语法和结构规则；大型语言模型则提供创造力和创新思维。 **生成式本体**通过将领域知识编码为可执行的Pydantic模式来实现这一点，这些模式使用DSPy签名约束大型语言模型的生成过程。一个基于多代理系统的管道能够分配不同的角色给不同领域的本体：例如，“力学架构师”设计游戏系统、“主题编织者”整合叙事元素、以及“平衡评论家”发现并解决潜在的问题或漏洞。每个代理都带有专业上的“焦虑感”，这确保了它们不会产生浅薄且迎合的输出，而是提供深刻和有价值的贡献。 **检索增强生成（Retrieval-augmented generation）**使新颖的设计基于现有范例进行锚定，并通过迭代验证确保机制与组件之间的连贯性。我们通过一个名为GameGrammar的游戏设计系统演示了该框架的应用：给出一个主题提示（例如“发光真菌在洞穴生态系统中的竞争”），该管道能够生成具有完整结构和可玩性的游戏规范，包括机制、组成部分、胜利条件以及设置说明等。 这些输出不仅满足本体的约束性要求，还保持了真实的创新性和创造力。这种模式的应用范围超出了游戏设计领域：任何具备专家词汇、有效性限制且积累了大量范例的领域（如音乐创作、软件架构或烹饪艺术）都可以采用生成式本体的方法来促进创意的结构性产出。 我们主张，这些约束不仅不会限制创造力，反而会使其成为可能。就像文法使诗歌得以实现一样，本体结构为有组织的设计创新提供了可能性。
URL
https://arxiv.org/abs/2602.05636
