@inproceedings{chakraborty-etal-2025-group,
    title = "Group, Embed and Reason: A Hybrid {LLM} and Embedding Framework for Semantic Attribute Alignment",
    author = "Chakraborty, Shramona  and
      Mujumdar, Shashank  and
      Gupta, Nitin  and
      Mehta, Sameep  and
      Kat, Ronen  and
      Etelis, Itay  and
      Mahameed, Mohamed  and
      Guez, Itai  and
      Tzoref-Brill, Rachel",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.120/",
    pages = "1703--1710",
    ISBN = "979-8-89176-333-3",
    abstract = "In enterprise systems, tasks like API integration, ETL pipeline creation, customer record merging, and data consolidation rely on accurately aligning attributes that refer to the same real-world concept but differ across schemas. This semantic attribute alignment is critical for enabling schema unification, reporting, and analytics. The challenge is amplified in schema only settings where no instance data is available due to ambiguous names, inconsistent descriptions, and varied naming conventions.We propose a hybrid, unsupervised framework that combines the contextual reasoning of Large Language Models (LLMs) with the stability of embedding-based similarity and schema grouping to address token limitations and hallucinations. Our method operates solely on metadata and scales to large schemas by grouping attributes and refining LLM outputs through embedding-based enhancement, justification filtering, and ranking. Experiments on real-world healthcare schemas show strong performance, highlighting the effectiveness of the framework in privacy-constrained scenarios."
}