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.

翻译：可持续发展披露标准（例如GRI、SASB、TCFD、IFRS S2）内容全面但篇幅冗长、术语密集且高度交叉引用，阻碍了结构化分析与下游应用。本文提出SSKG Hub（可持续发展标准知识图谱中心），这是一个研究原型与交互式网络平台，通过以LLM为核心、专家引导的流程将标准转化为可审计的知识图谱。该系统集成了自动标准识别、可配置分块、标准特定提示、鲁棒三元组解析以及支持细粒度审计元数据的可溯源Neo4j存储。LLM提取生成具有溯源关联的草稿知识图谱，经专家评审、整理后，通过元专家裁定正式升级为认证知识图谱。基于角色的治理框架涵盖只读访客访问、专家评审与增删改查操作、元专家认证及管理监督，确保草稿与认证状态间的可追溯性与权责明晰。除图谱探索与三元组级证据追溯外，SSKG Hub还支持跨图谱融合、图谱驱动任务以及专用于洞察分析与精选资源的模块。我们通过一项全面的专家主导知识图谱评审案例研究验证该平台，展示了端到端的整理流程与质量保证机制。该网络应用程序已在www.sskg-hub.com公开访问。