An Application Ontology for Reproducibility of Machine Learning Solutions - ACIS 2023 Proceedings

Aug 5, 2026

With Artificial Intelligence and Machine Learning (ML) on the rise, organisations of different scales seek to operationalise ML systems for day-to-day activities. Many enterprises struggle to adapt existing ML solutions due to gaps in understanding, customisation, infrastructure enablement, and training. External service providers can also misalign solutions with organisational goals and cause loss of expert knowledge. The paper proposes an ontology to ensure reproducibility of ML models and to support their integration into application environments.

LanguageEnglish

CategoryResearch & Report

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Pages10

Reading Time25 min

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Abstract

1 Introduction

Keywords and Motivation

Association for Information Systems AIS Electronic Library (AISeL) | ACIS 2023 Proceedings | Australasian (ACIS) | | --- | --- | | 12-2-2023
An Application Ontology for Reproducibility of Machine Learning Solutions
Madhushi Bandara
University of Technology Sydney, Australia, [madhushi.bandara@uts.edu.au](madhushi.bandara@uts.edu.au)
Yuchao Jiang
University of New South Wales, Australia, [yuchao.jiang@unsw.edu.eu](yuchao.jiang@unsw.edu.eu)
Asif Gill
University of Technology Sydney, Australia, [asif.gill@uts.edu.au](asif.gill@uts.edu.au)
[Fethi A. Rabhi](Fethi A. Rabhi)
University of New South Wales, Australia, [f.rabhi@unsw.edu.au](f.rabhi@unsw.edu.au)
Ghassan Beydon
University of Technology Sydney, [Ghassan.Beydoun@uts.edu.au](Ghassan.Beydoun@uts.edu.au)
Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) | | Recommended Citation Bandara, Madhushi; Jiang, Yuchao; Gill, Asif; Rabhi, Fethi A.; and Beydon, Ghassan, "An Application Ontology for Reproducibility of Machine Learning Solutions" (2023) . ACIS 2023 Proceedings. 57. [https://aisel.aisnet.org/acis2023/57](https://aisel.aisnet.org/acis2023/57) This material is brought to you by the Australasian (ACIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ACIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org). Australasian Conference on Information Systems Bandara et al. 2023, Wellington ML-Reproduce Ontology An Application Ontology for Reproducibility of Machine Learning Solutions Full research paper Madhushi Bandara School of Computer Science University of Technology Sydney Sydney, Australia [Email:](Email: madhushi.bandara@uts.edu.au)[ ](Email: madhushi.bandara@uts.edu.au)[madhushi.bandara@uts.edu.au](Email: madhushi.bandara@uts.edu.au) Yuchao Jiang School of Computer Science & Engineering University of New South Wales Sydney, Australia [Email:](Email: yuchao.jiang@unsw.edu.au)[ ](Email: yuchao.jiang@unsw.edu.au)[yuchao.jiang@unsw.edu.au](Email: yuchao.jiang@unsw.edu.au) Asif Gill School of Computer Science University of Technology Sydney Sydney, Australia Email: [asif.gill@uts.edu.au](asif.gill@uts.edu.au) Fethi A. Rabhi School of Computer Science & Engineering University of New South Wales Sydney, Australia Email: [f.rabhi@unsw.edu.au](f.rabhi@unsw.edu.au) Ghassan Beydoun School of Computer Science University of Technology Sydney Sydney, Australia [Email: ghassan.beydoun@uts.edu.au](Email: ghassan.beydoun@uts.edu.au) Abstract With Artificial Intelligence and Machine Learning (ML) on the rise, organisations of different scales and nature are looking to utilise ML systems to support their day-to-day operations. Many enterprises find it difficult to adapt existing ML solutions to their organisations without huge investments in solution understanding, customisation, infrastructure enablement and workforce training. Some organisations utilise external service providers to provision their standard analytics services, and this often leads to solutions that either do not fit well with their organisation goals or may lead to the loss of expert knowledge behind the establishment of the AI system. This paper aims to address some of these challenges by proposing an ontology for ensuring the reproducibility of ML models in research as well as their integration within application environments. Our work will ensure that the knowledge about a developed ML system or process is accumulated and recorded within an organisation and can be used in the future, either by new employees or other teams within the organisation. This approach can also be utilised by researchers and developers of ML systems to record and publish metadata of their studies, ensuring that future researcher

FAQs

What problem does the proposed ontology address?

It addresses the difficulty of ensuring reproducibility of ML models and integrating ML knowledge into application environments across research and organisational contexts.

Why is ML solution reproducibility difficult for many organisations?

Enterprises often lack the investment and expertise needed for solution understanding, customisation, infrastructure enablement, and workforce training, and knowledge can be lost when projects end.

How does the approach support future reuse of ML systems?

It accumulates and records knowledge about developed ML systems and processes so that new employees or other teams, as well as researchers and developers, can reuse and publish study metadata with minimal effort.

Aug 5, 2026

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