Abstract
Knowledge graph construction 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 TRACE-KG (Text-dRiven schemA for Context-Enriched Knowledge Graphs), a multimodal 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.
Abstract (translated)
知识图谱构建通常依赖于预定义本体或无模式抽取。本体驱动的流程能保证类型一致性，但需要高昂的模式设计与维护成本；而无模式方法常产生碎片化图谱，全局组织性较弱，尤其在信息密集、依赖上下文的冗长技术文档中表现明显。本文提出TRACE-KG（面向上下文增强知识图谱的文本驱动模式），一种无需预定义本体的多模态框架，可联合构建上下文增强的知识图谱并自动归纳模式。该系统通过结构化限定词捕捉条件关系，并利用数据驱动模式组织实体与关系——该模式既作为可复用的语义支架，又保持对源证据的完全可追溯性。实验表明，TRACE-KG能生成结构连贯、可追溯的知识图谱，为传统本体驱动与无模式构建流程提供了实用替代方案。
URL
https://arxiv.org/abs/2604.03496
