KG4ESG: The ESG Knowledge Graph Atlas

Environmental, Social, and Governance (ESG) analytics increasingly uses knowledge graphs (KGs) to encode framework-grounded semantics, align overlapping standards, and attach provenance for auditable querying. Yet ESG evidence is mainly text-first (disclosures, regulations, policies, news, incident narratives), so quality depends on the KG--NLP interface. Research remains fragmented across topics, modalities, and pipelines, limiting reuse. To address this, we introduce the ESG Research Focus Map (ESG-RFM), a vendor-agnostic pillar–theme–focus taxonomy crosswalked to major ESG frameworks (MSCI, GRI, ESRS, and SASB), which serves as the organizing lens for KG4ESG, an atlas-style survey of 337 ESG KG papers (2015--2025). KG4ESG is curated via a query dictionary and PRISMA-style screening across four academic search engines, and provides a structured atlas and reusable resource that organizes the field into two stages: Data→KG and KG→App. For Data→KG, we summarize evidence sources and distill four construction paradigms: P1 ontology-first lifting/integration, P2 rule/supervised NLP extraction, P3 LLM-assisted structuring/alignment, and P4 agentic pipelines with iterative validation/repair. For KG→App, we group applications into reporting & compliance, monitoring & risk intelligence, and decision support. A corpus-level meta-analysis highlights gaps in evaluation, openness, and multimodal grounding motivating auditable benchmarks and reusable resources. We will open-source a repository containing all literature, the taxonomy, and related artifacts once permitted.