Open-Source Transformer-Based Information Retrieval System for Energy Efficient Robotics Related Literature
Online veröffentlicht: 23. Mai 2025
Seitenbereich: 196 - 208
Eingereicht: 05. Jan. 2025
Akzeptiert: 12. Apr. 2025
DOI: https://doi.org/10.2478/orga-2025-0012
Schlüsselwörter
© 2025 Tine Bertoncel, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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.