Standard
Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology. /
Usmanova, Aida; Usbeck, Ricardo.
ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop. ed. / Dominik Stammbach; Jingwei Ni; Tobias Schimanski; Kalyan Dutia; Alok Singh; Julia Bingler; Christophe Christiaen; Neetu Kushwaha; Veruska Muccione; Saeid A. Vaghefi; Markus Leippold. Association for Computational Linguistics (ACL), 2024. p. 168-177 (ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Usmanova, A & Usbeck, R 2024,
Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology. in D Stammbach, J Ni, T Schimanski, K Dutia, A Singh, J Bingler, C Christiaen, N Kushwaha, V Muccione, SA Vaghefi & M Leippold (eds),
ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop. ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop, Association for Computational Linguistics (ACL), pp. 168-177, 1st Workshop on Natural Language Processing Meets Climate Change - ClimateNLP 2024, Bangkok, Thailand,
16.08.24. <
https://aclanthology.org/2024.climatenlp-1.13/>
APA
Usmanova, A., & Usbeck, R. (2024).
Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology. In D. Stammbach, J. Ni, T. Schimanski, K. Dutia, A. Singh, J. Bingler, C. Christiaen, N. Kushwaha, V. Muccione, S. A. Vaghefi, & M. Leippold (Eds.),
ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop (pp. 168-177). (ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop). Association for Computational Linguistics (ACL).
https://aclanthology.org/2024.climatenlp-1.13/
Vancouver
Usmanova A, Usbeck R.
Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology. In Stammbach D, Ni J, Schimanski T, Dutia K, Singh A, Bingler J, Christiaen C, Kushwaha N, Muccione V, Vaghefi SA, Leippold M, editors, ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop. Association for Computational Linguistics (ACL). 2024. p. 168-177. (ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop).
Bibtex
@inbook{bb2dc8c6f29c4401be1bae67a6cd4364,
title = "Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology",
abstract = "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{\textquoteright} 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.",
keywords = "Informatics",
author = "Aida Usmanova and Ricardo Usbeck",
note = "Publisher Copyright: {\textcopyright}2024 Association for Computational Linguistics.; 1st Workshop on Natural Language Processing Meets Climate Change - ClimateNLP 2024, ClimateNLP 2024 ; Conference date: 16-08-2024 Through 16-08-2024",
year = "2024",
language = "English",
series = "ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "168--177",
editor = "Dominik Stammbach and Jingwei Ni and Tobias Schimanski and Kalyan Dutia and Alok Singh and Julia Bingler and Christophe Christiaen and Neetu Kushwaha and Veruska Muccione and Vaghefi, {Saeid A.} and Markus Leippold",
booktitle = "ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop",
address = "United States",
url = "https://nlp4climate.github.io/",
}
RIS
TY - CHAP
T1 - Structuring Sustainability Reports for Environmental Standards with LLMs guided by Ontology
AU - Usmanova, Aida
AU - Usbeck, Ricardo
N1 - Conference code: 1
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Informatics
UR - http://www.scopus.com/inward/record.url?scp=85204423193&partnerID=8YFLogxK
M3 - Article in conference proceedings
AN - SCOPUS:85204423193
T3 - ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop
SP - 168
EP - 177
BT - ClimateNLP 2024 - 1st Workshop on Natural Language Processing Meets Climate Change, Proceedings of the Workshop
A2 - Stammbach, Dominik
A2 - Ni, Jingwei
A2 - Schimanski, Tobias
A2 - Dutia, Kalyan
A2 - Singh, Alok
A2 - Bingler, Julia
A2 - Christiaen, Christophe
A2 - Kushwaha, Neetu
A2 - Muccione, Veruska
A2 - Vaghefi, Saeid A.
A2 - Leippold, Markus
PB - Association for Computational Linguistics (ACL)
T2 - 1st Workshop on Natural Language Processing Meets Climate Change - ClimateNLP 2024
Y2 - 16 August 2024 through 16 August 2024
ER -
