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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

The SSKG Hub platform is presented, a research prototype and interactive web platform that transforms standards into auditable knowledge graphs (KGs) through an LLM-centered, expert-guided pipeline and supports cross-KG fusion, KG-driven tasks, and dedicated modules for insights and curated resources.

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.

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

TL;DR

The SSKG Hub platform is presented, a research prototype and interactive web platform that transforms standards into auditable knowledge graphs (KGs) through an LLM-centered, expert-guided pipeline and supports cross-KG fusion, KG-driven tasks, and dedicated modules for insights and curated resources.

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.
Paper Structure (36 sections, 2 figures, 2 tables)

This paper contains 36 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLM-based Extraction and Structured Generation.
  4. KGs, Collaborative Tooling, and Visual Analytics.
  5. KG Fusion, Sustainability QA, and Human-in-the-Loop.
  6. System Overview
  7. From Standards to KGs
  8. LLM-empowered generation
  9. Ingestion & identification
  10. Configurable chunking & prompting
  11. LLM extraction
  12. Graph insertion & audit metadata
  13. Expert-guided curation
  14. Role-governed access
  15. Independent multi-expert review
  16. ...and 21 more sections

Figures (2)

  • Figure 1: System overview of SSKG Hub. Sustainability standards PDFs (e.g., GRI, SASB, IFRS S2, TCFD) are processed via text extraction (PyMuPDF), LLM-based identification and chunking, and standard-specific prompting. Qwen-Max extracts normalized (subject, predicate, object) triples with aligned provenance, forming a provenance-aware Draft KG stored in Neo4j with audit metadata. Through expert review and meta-expert certification, validated triples are promoted to a Certified KG. On top of this graph core, SSKG Hub supports catalog management, interactive graph exploration with evidence tracing, triple inspection, cross-KGs fusion, downstream tasks (e.g., KGQA, reasoning paths), analytics, and immutable audit logging under role-based governance.
  • Figure 2: SSKG Hub UI highlights.Left: (1) standard-specific extraction setup, where users upload a sustainability standards PDF, verify the detected standard family, and inspect tailored prompt templates; (2) real-time ingestion monitoring with chunk-level progress and model transparency; and (3) a structured ingestion summary reporting document metadata and graph statistics. Right: (1) provenance-backed verification that links each triple to its source page and evidence span, with editable CRUD operations on selected (subject, predicate, object) triples; (2) an overall graph overview with zoom controls; (3) an interactive KG canvas for navigation, filtering, and structural exploration; and (4) a side panel displaying the original PDF with search and export utilities for traceable validation.