[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124756-en":3,"doc-seo-124756-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124756,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","An Application Ontology for Reproducibility of Machine Learning Solutions - ACIS 2023 Proceedings","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.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ACIS 2023 Proceedings | Australasian (ACIS) |\n| --- | --- |\n| 12-2-2023\u003Cbr>An Application Ontology for Reproducibility of Machine Learning Solutions\u003Cbr>Madhushi Bandara\u003Cbr>University of Technology Sydney, Australia, [madhushi.bandara@uts.edu.au](madhushi.bandara@uts.edu.au)\u003Cbr>Yuchao Jiang\u003Cbr>University of New South Wales, Australia, [yuchao.jiang@unsw.edu.eu](yuchao.jiang@unsw.edu.eu)\u003Cbr>Asif Gill\u003Cbr>University of Technology Sydney, Australia, [asif.gill@uts.edu.au](asif.gill@uts.edu.au)\u003Cbr>[Fethi A. Rabhi](Fethi A. Rabhi)\u003Cbr>University of New South Wales, Australia, [f.rabhi@unsw.edu.au](f.rabhi@unsw.edu.au)\u003Cbr>Ghassan Beydon\u003Cbr>University of Technology Sydney, [Ghassan.Beydoun@uts.edu.au](Ghassan.Beydoun@uts.edu.au)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) |  |\n\nRecommended Citation  \nBandara, 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.  \n[https://aisel.aisnet.org/acis2023/57](https://aisel.aisnet.org/acis2023/57)  \nThis 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).  \nAustralasian Conference on Information Systems Bandara et al.  \n2023, Wellington ML-Reproduce Ontology  \nAn Application Ontology for Reproducibility of Machine Learning Solutions  \nFull research paper  \nMadhushi Bandara  \nSchool 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)  \nYuchao Jiang  \nSchool of Computer Science & Engineering University of New South Wales  \nSydney, 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)  \nAsif Gill  \nSchool of Computer Science University of Technology Sydney Sydney, Australia  \nEmail: [asif.gill@uts.edu.au](asif.gill@uts.edu.au)  \nFethi A. Rabhi  \nSchool of Computer Science & Engineering University of New South Wales  \nSydney, Australia  \nEmail: [f.rabhi@unsw.edu.au](f.rabhi@unsw.edu.au)  \nGhassan Beydoun  \nSchool of Computer Science University of Technology Sydney Sydney, Australia [Email: ghassan.beydoun@uts.edu.au](Email: ghassan.beydoun@uts.edu.au)  \nAbstract  \nWith 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","cbCaiu2q0GM8vvjD","https://ap.wps.com/l/cbCaiu2q0GM8vvjD","pdf",1746491,1,10,"English","en",105,"# Abstract\n# 1 Introduction\n# Keywords and Motivation","[{\"question\":\"What problem does the proposed ontology address?\",\"answer\":\"It addresses the difficulty of ensuring reproducibility of ML models and integrating ML knowledge into application environments across research and organisational contexts.\"},{\"question\":\"Why is ML solution reproducibility difficult for many organisations?\",\"answer\":\"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.\"},{\"question\":\"How does the approach support future reuse of ML systems?\",\"answer\":\"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.\"}]","An Application Ontology for Reproducibility of Machine Learning Solutions - 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