@inproceedings{konan-etal-2024-automating,
    title = "Automating the Generation of a Functional Semantic Types Ontology with Foundational Models",
    author = "Konan, Sachin  and
      Rudolph, Larry  and
      Affens, Scott",
    editor = "Yang, Yi  and
      Davani, Aida  and
      Sil, Avi  and
      Kumar, Anoop",
    booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.naacl-industry.21/",
    doi = "10.18653/v1/2024.naacl-industry.21",
    pages = "248--265",
    abstract = "The rise of data science, the inherent dirtiness of data, and the proliferation of vast data providers have increased the value proposition of Semantic Types. Semantic Types are a way of encoding contextual information onto a data schema that informs the user about the definitional meaning of data, its broader context, and relationships to other types. We increasingly see a world where providing structure to this information, attached directly to data, will enable both people and systems to better understand the content of a dataset and the ability to efficiently automate data tasks such as validation, mapping/joins, and eventually machine learning. While ontological systems exist, they have not had widespread adoption due to challenges in mapping to operational datasets and lack of specificity of entity-types. Additionally, the validation checks associated with data are stored in code bases separate from the datasets that are distributed. In this paper, we address both challenges holistically by proposing a system that efficiently maps and encodes functional meaning on Semantic Types."
}