@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://preview.aclanthology.org/ingest-eacl/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."
}