Deepvoc: a linked open vocabulary for reproducible and reliable deep learning experiments
Document Type
Article
Source of Publication
International Journal of Machine Learning and Cybernetics
Publication Date
6-13-2025
Abstract
Several Deep Learning (DL) algorithms and techniques have been developed and published in recent years to address problems across various domains. To ensure accurate result comparisons, DL experiments must be conducted in a consistent computing environment using the same algorithm configurations and datasets. Developing DL algorithms requires programmers to manage numerous parameter settings and datasets, making it challenging to test and document results accurately without proper metadata provenance. However, the connected data community lacks a lightweight metadata exchange framework for DL across different environments, limiting high-level interoperability. This article bridges that gap by introducing DeepVoc (Deep Learning Vocabulary) a structured vocabulary designed to enhance data provenance and improve the usability of DL experiments. Moreover, DeepVoc adheres to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, ensuring better metadata standardization while enhancing the reproducibility, reusability, and interoperability of DL experiments.
DOI Link
ISSN
Publisher
Springer Science and Business Media LLC
Disciplines
Computer Sciences
Keywords
Deep learning, Experiments, FAIR, Interoperability, Metadata, Reusability
Scopus ID
Recommended Citation
Shaheen, Laiba; Tubaishat, Abdallah; Shah, Babar; Mussiraliyeva, Shynar; Maqbool, Fahad; Razzaq, Saad; and Anwar, Sajid, "Deepvoc: a linked open vocabulary for reproducible and reliable deep learning experiments" (2025). All Works. 7385.
https://zuscholars.zu.ac.ae/works/7385
Indexed in Scopus
yes
Open Access
no