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Environmental, Social, and Governance (ESG) analytics increasingly uses knowledge graphs (KGs) to encode framework-grounded semantics, align partially 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 this end, we first introduce the ESG Research Focus Map (ESG-RFM), a vendor-agnostic pillar–theme–focus taxonomy crosswalked to major ESG frameworks and standards (MSCI, GRI, ESRS, and SASB), which serves as the organizing lens for KG4ESG, an atlas-style survey of 337 ESG knowledge graph (KG) papers (2015–2025). KG4ESG is curated via a query dictionary and PRISMA-style screening across 4 academic search engines, and provides a structured, evaluative atlas and reusable resource that organizes the field into two coupled stages: Data→KG and KG→App. For Data→KG, we summarize evidence sources and distill 4 construction paradigms: P1 ontology-first lifting/integration, P2 rule/supervised NLP/ML extraction, P3 LLM-assisted structuring/alignment, and P4 agentic/tool-using pipelines with iterative validation/repair. For KG→App, we group apps into reporting & compliance, monitoring & risk intelligence, and decision support, and synthesize recurring language interfaces. A corpus-level meta-analysis highlights gaps in evaluation, openness, and multimodal grounding motivating auditable benchmarks and reusable resources. We will release all the artifacts.