ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis

2025-01-27

AI Summary

ESG reports suffer from information redundancy, semantic ambiguity, and high subjectivity, undermining the reliability and interpretability of sustainability analysis. To address these challenges, we propose the first neuro-symbolic knowledge base framework tailored for corporate sustainability analysis—integrating domain-specific concept parsing, GPT-4o-based large language model reasoning, and semi-supervised label propagation, all operating in a zero-shot, plug-and-play manner. We construct the first action-oriented ESG hierarchical ontology knowledge base containing 44K triples. Experimental results demonstrate that our approach outperforms baseline methods by 26% on ESG relevance identification and by 31% on actionable recommendation generation. It enables high-precision, unsupervised disclosure text analysis and significantly enhances comprehension and generalization over long-tail ESG terminology.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Neuro-Symbolic Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy. However, this is hindered by the complexity and volume of corporate sustainability data (i.e. sustainability disclosures), not least by the effectiveness of the NLP tools used to analyse them. To this end, we identify three primary challenges - immateriality, complexity, and subjectivity, that exacerbate the difficulty of extracting insights from sustainability disclosures. To address these issues, we introduce ESGSenticNet, a publicly available knowledge base for sustainability analysis. ESGSenticNet is constructed from a neurosymbolic framework that integrates specialised concept parsing, GPT-4o inference, and semi-supervised label propagation, together with a hierarchical taxonomy. This approach culminates in a structured knowledge base of 44k knowledge triplets - ('halve carbon emission', supports, 'emissions control'), for effective sustainability analysis. Experiments indicate that ESGSenticNet, when deployed as a lexical method, more effectively captures relevant and actionable sustainability information from sustainability disclosures compared to state of the art baselines. Besides capturing a high number of unique ESG topic terms, ESGSenticNet outperforms baselines on the ESG relatedness and ESG action orientation of these terms by 26% and 31% respectively. These metrics describe the extent to which topic terms are related to ESG, and depict an action toward ESG. Moreover, when deployed as a lexical method, ESGSenticNet does not require any training, possessing a key advantage in its simplicity for non-technical stakeholders.

Problem

• Environmental, Social, and Governance (ESG) Evaluation
• Information Complexity
• Subjectivity in Reporting

Innovation

• ESGSenticNet
• GPT-4o integration
• Hierarchical taxonomy