@inproceedings{sahay-etal-2025-autokb,
    title = "{A}uto{KB}: Automated Creation of Structured Knowledge Bases for Domain-Specific Support",
    author = "Sahay, Rishav  and
      Jain, Arihant  and
      Aggarwal, Purav  and
      Saladi, Anoop",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.58/",
    pages = "708--723",
    ISBN = "979-8-89176-194-0",
    abstract = "Effective customer support requires domain-specific solutions tailored to users' issues. However, LLMs like ChatGPT, while excelling in open-domain tasks, often face challenges such as hallucinations, lack of domain compliance, and imprecise solutions when applied to specialized contexts. RAG-based systems, designed to combine domain context from unstructured knowledge bases (KBs) with LLMs, often struggle with noisy retrievals, further limiting their effectiveness in addressing user issues. Consequently, a sanitized KB is essential to ensure solution accuracy, precision, and domain compliance. To address this, we propose AutoKB, an automated pipeline for building a domain-specific KB with a hierarchical tree structure that maps user issues to precise and domain-compliant solutions. This structure facilitates granular issue resolution by improving real-time retrieval of user-specific solutions. Experiments in troubleshooting and medical domains demonstrate that our approach significantly enhances solution correctness, preciseness, and domain compliance, outperforming LLMs and unstructured KB baselines. Moreover, AutoKB is 75 times more cost-effective than manual methods."
}