An Introduction to Machine Learning Lifecycle Ontology and its Applications

Published

April 18, 2024

Author(s)

Milos Drobnjakovic, Perawit Charoenwut, Ana Nikolov, Hakju Oh, Boonserm Kulvatunyou

Abstract

Machine Learning (ML) adoption is on the rapid rise, with a nearly 40% compound annual growth rate over the next decade. In other words, companies will be flooded with ML models developed with different datasets and software. The ability to have information at one's fingertips about how these ML models were developed, what they were used for, what their performances and uncertainties are, what their internal structure looks like, and what datasets were used can have several benefits. These pieces of ML metadata are what we collectively call ML lifecycle information. This paper explains our current research into developing an ML Lifecycle Ontology (MLLO) to capture such information in a knowledge graph. The main objective of this paper is to describe the motivation through use cases and show that future research is warranted. To that end, basic and advanced use scenarios are described. MLLO is then introduced at a high level and validated with the basic use case to show its value. We then describe future work we are undergoing to demonstrate the hypotheses in the advanced use case in which MLLO not only serves as a standard queryable representation of ML metadata across different ML software but also as a connector to domain knowledge to assist in the ML model development and reuse.

Proceedings Title

IFIP Advances in Information and Communication Technology

Conference Dates

September 8-12, 2024

Conference Location

Chemnitz, DE

Pub Type

Conferences

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Keywords

Machine Learning, Ontology, Knowledge Graph, Machine Learning Lifecycle Management, Biomanufacturing

Citation

Drobnjakovic, M. , Charoenwut, P. , Nikolov, A. , Oh, H. and Kulvatunyou, B. (2024), An Introduction to Machine Learning Lifecycle Ontology and its Applications, IFIP Advances in Information and Communication Technology, Chemnitz, DE, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957977 (Accessed May 13, 2026)

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