ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis

TL;DR

ESGSenticNet is a neurosymbolic knowledge base designed to improve the analysis of corporate sustainability disclosures by capturing ESG-related concepts and actions more effectively than existing methods, without requiring training.

Contribution

This paper introduces ESGSenticNet, a novel neurosymbolic knowledge base that integrates concept parsing, GPT-4o inference, and semi-supervised learning for enhanced sustainability analysis.

Findings

Outperforms state-of-the-art baselines in capturing ESG-related terms.

Achieves 26% and 31% improvements in relatedness and action orientation metrics.

Contains 44,000 knowledge triplets supporting sustainability insights.

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.