SSKG Hub: An Expert-Guided Platform for LLM-Empowered Sustainability Standards Knowledge Graphs
Chaoyue He, Xin Zhou, Xinjia Yu, Lei Zhang, Yan Zhang, Yi Wu, Lei Xiao, Liangyue Li, Di Wang, Hong Xu, Xiaoqiao Wang, Wei Liu, Chunyan Miao

TL;DR
SSKG Hub is an interactive platform that converts complex sustainability standards into structured, auditable knowledge graphs using an LLM-guided, expert-reviewed pipeline, enhancing analysis and traceability.
Contribution
It introduces a novel expert-guided pipeline for transforming sustainability standards into certified knowledge graphs with provenance and governance features.
Findings
Successful expert review and curation of knowledge graphs
Enhanced traceability and auditability of standards data
Public availability of the SSKG Hub platform
Abstract
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,…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Scientific Computing and Data Management · Research Data Management Practices
