Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

Following the introduction of the European Sustainability Reporting Standard (ESRS), companies will have to adapt to a new policy and provide mandatory sustainability reports. However, implementing such reports entails a challenge, such as the comprehension of a large number of textual information from various sources. This task can be accelerated by employing Large Language Models (LLMs) and ontologies to effectively model the domain knowledge. In this study, we extended an existing ontology to model ESRS Topical Standard for disclosure. The developed ontology would enable automated reasoning over the data and assist in constructing Knowledge Graphs (KGs). Moreover, the proposed ontology extension would also help to identify gaps in companies’ sustainability reports with regard to the ESRS requirements. Additionally, we extracted knowledge from corporate sustainability reports via LLMs guided with a proposed ontology and developed their KG representation.

Original languageEnglish
Title of host publicationClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop
EditorsDominik Stammbach, Jingwei Ni, Tobias Schimanski, Kalyan Dutia, Alok Singh, Julia Bingler, Christophe Christiaen, Neetu Kushwaha, Veruska Muccione, Saeid A. Vaghefi, Markus Leippold
Number of pages10
PublisherAssociation for Computational Linguistics (ACL)
Publication date2024
Pages168-177
ISBN (electronic)979-8-89176-159-9
DOIs
Publication statusPublished - 2024
Event1st Workshop on Natural Language Processing Meets Climate Change - ClimateNLP 2024 - Bangkok, Thailand
Duration: 16.08.202416.08.2024
Conference number: 1
https://nlp4climate.github.io/

Bibliographical note

Publisher Copyright:
©2024 Association for Computational Linguistics.

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