OntoMetric: An Ontology-Driven LLM-Assisted Framework for Automated ESG Metric Knowledge Graph Generation

TL;DR

OntoMetric is a framework that uses ontologies and LLMs to automatically generate and govern ESG knowledge graphs from regulatory documents, improving accuracy and efficiency.

Contribution

It introduces an ontology-guided, scalable method for extracting and validating ESG metrics from regulatory texts using LLMs with embedded constraints.

Findings

Semantic accuracy of 65-90% with ontology guidance

Over 80% schema compliance achieved

48 times efficiency improvement over baseline

Abstract

Environmental, Social, and Governance (ESG) metric knowledge is inherently structured, connecting industries, reporting frameworks, metric categories, metrics, and calculation models through compositional dependencies, yet in practice this structure remains embedded implicitly in regulatory documents such as SASB, TCFD, and IFRS S2 and rarely exists as an explicit, governed, or machine-actionable artefact. Existing ESG ontologies define formal schemas but do not address scalable population and governance from authoritative regulatory sources, while unconstrained large language model (LLM) extraction frequently produces semantically incorrect entities, hallucinated relationships, and structurally invalid graphs. OntoMetric is an ontology-guided framework for the automated construction and governance of ESG metric knowledge graphs from regulatory documents that operationalises the ESG Metric Knowledge Graph (ESGMKG) ontology as a first-class constraint embedded directly into the extraction and population process. The framework integrates structure-aware segmentation, ontology-constrained LLM extraction enriched with semantic fields and deterministic identifiers, and two-phase validation combining semantic type verification with rule-based schema checking, while preserving segment-level and page-level provenance to ensure traceability to regulatory source text. Evaluation on five ESG regulatory standards shows that ontology-guided extraction achieves 65-90 percent semantic accuracy and over 80 percent schema compliance, compared with 3-10 percent for unconstrained baseline extraction, and yields stable cost efficiency with a cost per validated entity of 0.01-0.02 USD and a 48 times efficiency improvement over baseline.