知识图谱生成通常依赖于预定义本体或免模式提取。本体驱动的流水线强制执行一致的类型，但需要昂贵的模式设计和维护，而免模式方法通常产生碎片化的图谱，全局组织薄弱，尤其是在信息密集、依赖上下文的冗长技术文档中。我们提出 \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），一个无需假设预定义本体即可联合构建上下文增强的知识图谱和归纳模式的框架。TRACE-KG 通过结构化限定符捕获条件关系，并使用数据驱动模式组织实体和关系，该模式作为可重用的语义支架，同时保持对源证据的完全可追溯性。实验表明，TRACE-KG 生成结构连贯、可追溯的知识图谱，并为本体驱动和免模式构建流水线提供了实用的替代方案。
英文摘要：
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.
OntoMetric: An Ontology-Driven LLM-Assisted Framework for Automated ESG Metric Knowledge Graph Generation
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Faculty of Sciences, Engineering and Technology, The University of Adelaide（阿德莱德大学科学、工程与技术学院）；School of Information Systems and Technology Management, University of New South Wales（新南威尔士大学信息系统与技术管理学院）
The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 6 million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID-19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations inherent to scientific literature. Moreover, because these engines mainly rely upon semantic information, their ability to capture complex global relationships across documents is limited, which reduc…
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning
Department of Industrial \& Systems Engineering, KAIST Daejeon South Korea；Graduate School of Data Science, KAIST Daejeon South Korea；School of Computer Engineering, Hansung University Seoul South Korea；Department of Industrial \& Systems Engineering, KAIST；Graduate School of Data Science, KAIST；School of Computer Engineering, Hansung University
Retrieval-Augmented Code Generation for Universal Information Extraction
CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所网络数据科学与技术重点实验室）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Platform and Content Group, Tencent（腾讯平台与内容事业群）
RWTH Aachen University（亚琛工业大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；ZBMED - Information Center for Life Sciences（ZB MED——生命科学信息中心）；University of Cologne（科隆大学）
Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A domain-specific knowledge graph~(KG) is an explicit conceptualization of a specific subject-matter domain represented w.r.t semantically interrelated entities and relations. A KG can be constructed by integrating such facts and data and be used for data integration, exploration, and…
Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全重点实验室）；Shandong University（山东大学）；Shandong University-Weihai Research Institute of Industrial Technology（山东大学威海工业技术研究院）
The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China)（知识工程与大数据关键实验室（中华人民共和国教育部））；School of Computer Science and Information Engineering（计算机科学与信息工程学院）；Anhui Zhongke Guojin Intelligent Technology Co., Ltd.（安徽中科创金智能科技有限公司）；Nanjing University of Science and Technology（南京理工大学）
