Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly schema design and maintenance, whereas schema-free methods often produce fragmented graphs with weak global organization, especially in long technical documents with dense, context-dependent information. We propose \textbf{TRACE-KG} (\textbf{T}ext-d\textbf{R}iven schem\textbf{A} for \textbf{C}ontext-\textbf{E}nriched \textbf{K}nowledge \textbf{G}raphs), a framework that jointly constructs a context-enriched knowledge graph and an induced schema without assuming a predefined ontology. TRACE-KG captures conditional relations through structured qualifiers and organizes entities and relations using a data-driven schema that serves as a reusable semantic scaffold while preserving full traceability to the source evidence. Experiments show that TRACE-KG produces structurally coherent, traceable knowledge graphs and offers a practical alternative to both ontology-driven and schema-free construction pipelines.

翻译：知识图谱生成通常依赖于预定义本体或免模式抽取。基于本体的流程能确保一致的类型化，但需要高昂的模式设计与维护成本；而免模式方法常产生全局组织松散、碎片化的图谱，尤其在包含密集且依赖上下文信息的长篇技术文档中表现明显。我们提出**TRACE-KG**（**T**ext-d**R**iven schem**A** for **C**ontext-**E**nriched **K**nowledge **G**raphs，文本驱动的上下文增强知识图谱模式），该框架在不预设预定义本体的前提下，联合构建上下文增强的知识图谱及其诱导模式。TRACE-KG通过结构化限定符捕获条件关系，并利用数据驱动模式组织实体与关系——该模式既可作为可复用的语义骨架，又完整保留对源证据的可追溯性。实验表明，TRACE-KG能生成结构一致、可追溯的知识图谱，为基于本体与免模式的构建流程提供了实用替代方案。