@inproceedings{sadia-etal-2025-squid,
    title = "{SQU}i{D}: Synthesizing Relational Databases from Unstructured Text",
    author = "Sadia, Mushtari  and
      Yang, Zhenning  and
      Xiao, Yunming  and
      Chen, Ang  and
      Roy Chowdhury, Amrita",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.emnlp-main.1629/",
    doi = "10.18653/v1/2025.emnlp-main.1629",
    pages = "31975--32000",
    ISBN = "979-8-89176-332-6",
    abstract = "Relational databases are central to modern data management, yet most data exists in unstructured forms like text documents. To bridge this gap, we leverage large language models (LLMs) to automatically synthesize a relational database by generating its schema and populating its tables from raw text. We introduce SQUiD, a novel neurosymbolic framework that decomposes this task into four stages, each with specialized techniques. Our experiments show that SQUiD consistently outperforms baselines across diverse datasets. Our code and datasets are publicly available at: https://github.com/Mushtari-Sadia/SQUiD."
}