IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-031-72494-7_12.html
   My bibliography  Save this book chapter

Enhancing Technological Taxonomies by Large Language Models

Author

Listed:
  • Giuliana Barba

    (Università del Salento)

  • Mariangela Lazoi

    (Università del Salento)

  • Marianna Lezzi

    (Università del Salento)

Abstract

The evolution of Large Language Models (e.g. GPT-4) in the modern data-driven business contexts has opened up new perspectives in optimizing operations and managing information. This study introduces the Automated Semantic Taxonomy Enrichment Methodology (ASTEM), a novel framework utilizing GPT-4 to enhance the semantic richness of corporate taxonomies. ASTEM integrates advanced prompt engineering and iterative evaluation to generate contextually relevant taxonomy definitions. A case study carried out in a large company operating in the aerospace sector provides a practical perspective on the methodology effectiveness, demonstrating its crucial role in filling information gaps and establishing relevant semantic connections. This study demonstrates the potential of leveraging artificial intelligence to automate complex intellectual processes and suggests directions for future research in expanding its application across different industrial domains.

Suggested Citation

  • Giuliana Barba & Mariangela Lazoi & Marianna Lezzi, 2025. "Enhancing Technological Taxonomies by Large Language Models," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-72494-7_12
    DOI: 10.1007/978-3-031-72494-7_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-031-72494-7_12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.