Paper Type
ERF
Abstract
Environmental, Social, and Governance (ESG) data are increasingly vital for strategic decision-making, yet remains fragmented and inconsistently reported across sources. Existing ESG analytics solutions typically address structuring, retrieval, or summarization in isolation, limiting their ability to capture complex, multi-entity relationships. We propose a unified ESG data processing framework that integrates knowledge graph construction, community detection, and Graph-based Retrieval-Augmented Generation (GraphRAG) to deliver structured, context-rich insights. Our system models interconnected ESG entities, applies network analysis to uncover hidden patterns, and generates traceable, decision-ready responses to complex queries. We evaluate an early implementation using a dataset of 8,350 ESG reports from 309 companies, demonstrating improvements in coverage, contextual relevance, and multi-perspective analysis. To address potential biases in ESG disclosures, we outline plans for cross-source validation and sentiment analysis. Our contributions establish a scalable and transparent foundation for ESG analytics, offering enhanced traceability, diversity, and decision support for analysts, regulators, and stakeholders.
Paper Number
1850
Recommended Citation
Olad, Milad; Khaksar, Ehsan; Ghanbari, Maryam; Tahmooresnejad, Leila; Ayanso, Anteneh; Lytle, Robert; and Hilimoniuk, Ryan, "Enhancing Environmental, Social, and Governance Insights with Structured Knowledge Graphs and Advanced Query Analysis" (2025). AMCIS 2025 Proceedings. 21.
https://aisel.aisnet.org/amcis2025/data_science/sig_dsa/21
Enhancing Environmental, Social, and Governance Insights with Structured Knowledge Graphs and Advanced Query Analysis
Environmental, Social, and Governance (ESG) data are increasingly vital for strategic decision-making, yet remains fragmented and inconsistently reported across sources. Existing ESG analytics solutions typically address structuring, retrieval, or summarization in isolation, limiting their ability to capture complex, multi-entity relationships. We propose a unified ESG data processing framework that integrates knowledge graph construction, community detection, and Graph-based Retrieval-Augmented Generation (GraphRAG) to deliver structured, context-rich insights. Our system models interconnected ESG entities, applies network analysis to uncover hidden patterns, and generates traceable, decision-ready responses to complex queries. We evaluate an early implementation using a dataset of 8,350 ESG reports from 309 companies, demonstrating improvements in coverage, contextual relevance, and multi-perspective analysis. To address potential biases in ESG disclosures, we outline plans for cross-source validation and sentiment analysis. Our contributions establish a scalable and transparent foundation for ESG analytics, offering enhanced traceability, diversity, and decision support for analysts, regulators, and stakeholders.
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