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<p>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.</p>

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Deepvoc: a linked open vocabulary for reproducible and reliable deep learning experiments

  • 期刊论文

  • 发表日期：

    2025年6月13日

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  • Laiba Shaheen,
  • Abdallah Tubaishat,
  • Babar Shah,
  • Shynar Mussiraliyeva,
  • Fahad Maqbool,
  • Saad Razzaq,
  • Sajid Anwar

摘要

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.