@inproceedings{chang-etal-2026-esg,
title = "{ESG}-{KG}: A Multi-modal Knowledge Graph System for Automated Compliance Assessment",
author = "Chang, Li-Yang and
Chen, Chih-Ming and
Huang, Hen-Hsen and
Tsai, Ming-Feng and
Yen, An-Zi and
Wang, Chuan-Ju",
editor = "Croce, Danilo and
Leidner, Jochen and
Moosavi, Nafise Sadat",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-demo.43/",
pages = "602--608",
ISBN = "979-8-89176-382-1",
abstract = "Our system is built upon a multi-modal information extraction pipeline designed to process and interpret corporate sustainability reports. This integrated framework systematically handles diverse data formats{---}including text, tables, figures, and infographics{---}to extract, structure, and evaluate ESG-related content. The extracted multi-modal data is subsequently formalized into a structured knowledge graph (KG), which serves as both a semantic framework for representing entities, relationships, and metrics relevant to ESG domains, and as the foundational infrastructure for the automated compliance system. This KG enables high-precision retrieval of information across multiple source formats and reporting modalities. The trustworthy, context-rich representations provided by the knowledge graph establish a verifiable evidence base, creating a critical foundation for reliable retrieval-augmented generation (RAG) and subsequent LLM-based scoring and analysis of automatic ESG compliance system."
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<abstract>Our system is built upon a multi-modal information extraction pipeline designed to process and interpret corporate sustainability reports. This integrated framework systematically handles diverse data formats—including text, tables, figures, and infographics—to extract, structure, and evaluate ESG-related content. The extracted multi-modal data is subsequently formalized into a structured knowledge graph (KG), which serves as both a semantic framework for representing entities, relationships, and metrics relevant to ESG domains, and as the foundational infrastructure for the automated compliance system. This KG enables high-precision retrieval of information across multiple source formats and reporting modalities. The trustworthy, context-rich representations provided by the knowledge graph establish a verifiable evidence base, creating a critical foundation for reliable retrieval-augmented generation (RAG) and subsequent LLM-based scoring and analysis of automatic ESG compliance system.</abstract>
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%0 Conference Proceedings
%T ESG-KG: A Multi-modal Knowledge Graph System for Automated Compliance Assessment
%A Chang, Li-Yang
%A Chen, Chih-Ming
%A Huang, Hen-Hsen
%A Tsai, Ming-Feng
%A Yen, An-Zi
%A Wang, Chuan-Ju
%Y Croce, Danilo
%Y Leidner, Jochen
%Y Moosavi, Nafise Sadat
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-382-1
%F chang-etal-2026-esg
%X Our system is built upon a multi-modal information extraction pipeline designed to process and interpret corporate sustainability reports. This integrated framework systematically handles diverse data formats—including text, tables, figures, and infographics—to extract, structure, and evaluate ESG-related content. The extracted multi-modal data is subsequently formalized into a structured knowledge graph (KG), which serves as both a semantic framework for representing entities, relationships, and metrics relevant to ESG domains, and as the foundational infrastructure for the automated compliance system. This KG enables high-precision retrieval of information across multiple source formats and reporting modalities. The trustworthy, context-rich representations provided by the knowledge graph establish a verifiable evidence base, creating a critical foundation for reliable retrieval-augmented generation (RAG) and subsequent LLM-based scoring and analysis of automatic ESG compliance system.
%U https://aclanthology.org/2026.eacl-demo.43/
%P 602-608
Markdown (Informal)
[ESG-KG: A Multi-modal Knowledge Graph System for Automated Compliance Assessment](https://aclanthology.org/2026.eacl-demo.43/) (Chang et al., EACL 2026)
ACL
- Li-Yang Chang, Chih-Ming Chen, Hen-Hsen Huang, Ming-Feng Tsai, An-Zi Yen, and Chuan-Ju Wang. 2026. ESG-KG: A Multi-modal Knowledge Graph System for Automated Compliance Assessment. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 602–608, Rabat, Marocco. Association for Computational Linguistics.