SSKG Hub: 一个基于大语言模型的可持续性标准知识图谱专家指导平台
SSKG Hub: An Expert-Guided Platform for LLM-Empowered Sustainability Standards Knowledge Graphs
AI总结：
SSKG Hub通过大语言模型和专家指导构建可持续性标准知识图谱，实现标准到可审计图谱的转化，并提供治理框架和跨图谱融合功能。
摘要：
可持续性披露标准（如GRI、SASB、TCFD、IFRS S2）具有全面性但内容冗长，术语密集且高度交叉引用，阻碍了结构化分析和下游应用。我们提出了SSKG Hub（可持续性标准知识图谱枢纽），一个研究原型和交互式网页平台，通过以大语言模型为中心、专家指导的流程将标准转化为可审计的知识图谱（KG）。系统集成了自动标准识别、可配置分块、标准特定提示、强大的三元组解析和具有溯源意识的Neo4j存储，配合细粒度的审计元数据。大语言模型提取生成一个具有溯源链接的草案KG，通过元专家裁定进行审核、整理并正式推广为认证KG。一个基于角色的治理框架涵盖只读访客访问、专家审核和CRUD操作、元专家认证以及行政监督，确保在草案和认证状态之间具有可追溯性和问责性。除了图谱探索和三元组级证据追溯外，SSKG Hub还支持跨KG融合、KG驱动任务以及专门模块用于见解和整理资源。我们通过一个全面的专家主导的KG审查案例研究验证了该平台，展示了端到端的整理和质量保证。该网页应用在www.sskg-hub.com上公开可用。
英文摘要：
Sustainability disclosure standards (e.g., GRI, SASB, TCFD, IFRS S2) are comprehensive yet lengthy, terminology-dense, and highly cross-referential, hindering structured analysis and downstream use. We present SSKG Hub (Sustainability Standards Knowledge Graph Hub), a research prototype and interactive web platform that transforms standards into auditable knowledge graphs (KGs) through an LLM-centered, expert-guided pipeline. The system integrates automatic standard identification, configurable chunking, standard-specific prompting, robust triple parsing, and provenance-aware Neo4j storage with fine-grained audit metadata. LLM extraction produces a provenance-linked Draft KG, which is reviewed, curated, and formally promoted to a Certified KG through meta-expert adjudication. A role-based governance framework covering read-only guest access, expert review and CRUD operations, meta-expert certification, and administrative oversight ensures traceability and accountability across draft and certified states. Beyond graph exploration and triple-level evidence tracing, SSKG Hub supports cross-KG fusion, KG-driven tasks, and dedicated modules for insights and curated resources. We validate the platform through a comprehensive expert-led KG review case study that demonstrates end-to-end curation and quality assurance. The web application is publicly available at www.sskg-hub.com.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
