IDEAS home Printed from https://ideas.repec.org/a/vrs/organi/v58y2025i2p196-208n1006.html
   My bibliography  Save this article

Open-Source Transformer-Based Information Retrieval System for Energy Efficient Robotics Related Literature

Author

Listed:
  • Bertoncel Tine

    (University of Primorska, Faculty of Management, Koper, Slovenia)

Abstract

Background and Purpose This article employs the Hugging Face keyphrase-extraction-kbir-inspec machine learning model to analyze 654 abstracts on the topic of energy efficiency in systems and control, computer science and robotics. Methods This study targeted specific arXiv categories related to energy efficiency, scraping and processing abstracts with a state-of-the-art Transformer-based Hugging Face AI model to extract keyphrases, thereby enabling the creation of related keyphrase networks and the retrieval of relevant scientific preprints. Results The results demonstrate that state-of-the-art open-source machine learning models can extract valuable information from unstructured data, revealing prominent topics in the evolving field of energy-efficiency. Conclusion: This showcases the current landscape and highlights the capability of such information systems to pinpoint both well researched and less researched areas, potentially serving as an information retrieval system or early warning system for emerging technologies that promote environmental sustainability and cost efficiency.

Suggested Citation

  • Bertoncel Tine, 2025. "Open-Source Transformer-Based Information Retrieval System for Energy Efficient Robotics Related Literature," Organizacija, Sciendo, vol. 58(2), pages 196-208.
    • Handle: RePEc:vrs:organi:v:58:y:2025:i:2:p:196-208:n:1006
    DOI: 10.2478/orga-2025-0012
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/orga-2025-0012
    Download Restriction: no
    File URL: https://libkey.io/10.2478/orga-2025-0012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:vrs:organi:v:58:y:2025:i:2:p:196-208:n:1006. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.