SSKG Hub: An Expert-Guided Platform for LLM-Empowered Sustainability Standards Knowledge Graphs

2026-02-28

AI Summary

Current sustainability disclosure standards—such as GRI and SASB—are often verbose, terminology-heavy, and densely cross-referenced, hindering structured analysis and practical application. This work proposes an innovative paradigm that integrates large language models with expert-led governance to construct an auditable and traceable knowledge graph. The approach employs customized prompt engineering and configurable text chunking to automatically extract RDF-style triples, which are stored in a Neo4j graph database alongside fine-grained provenance metadata. The system supports expert review, version certification, and cross-standard knowledge graph integration. An end-to-end implementation has been validated and deployed as a public platform, enabling interactive graph exploration, evidence tracing, and knowledge-driven analytical tasks.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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, 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.

Problem

• sustainability disclosure standards
• knowledge graph
• structured analysis
• cross-referential documents
• downstream use

Innovation

• LLM-centered knowledge graph
• expert-guided curation
• provenance-aware storage
• role-based governance
• sustainability standards