MRM3 : machine readable ML model metadata

Čop, Andrej (author), Bertalanič, Blaž (author), Grobelnik, Marko (author), Fortuna, Carolina (author)

As the complexity and number of machine learning (ML) models grows, well-documented ML models are essential for developers and companies to use or adapt them to their specific use cases. Model metadata, already present in unstructured format as model cards in online repositories such as Hugging Face, could be more structured and machine readable while also incorporating environmental impact metrics such as energy consumption and carbon footprint. Our work extends the existing State of the Art by defining a structured schema for ML model metadata focusing on machine-readable format and support for integration into a knowledge graph (KG) for better organization and querying, enabling a wider set of use cases. Furthermore, we present an example wireless localization model metadata dataset consisting of 22 models trained on 4 datasets, integrated into a Neo4j-based KG with 113 nodes and 199 relations.

• Language: English
• Keywords: knowledge graphs, ontology, taxonomy, model metadata, neo4j, machine learning
• Typology: 1.08 - Published Scientific Conference Contribution
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: Str. 741-746
• PID: 20.500.12556/RUL-189415
• UDC: 004.8
• DOI: 10.1145/3711875.3736685
• COBISS.SI-ID: 251097347

Note

Podatek za številčenje strani ("Pages 741 - 746") je naveden na pristajalni strani prispevka (glej zgoraj izvorni URL). (Datum opombe: 6. 10. 2026)

• Publication date in RUL: 06.10.2026

Record is a part of a monograph

• Title: MobiSys '25 : proceedings of the 23rd ACM International Conference on Mobile Systems, Applications, and Services
• Place of publishing: New York
• Publisher: The Association for Computing Machinery
• Year: 2025
• ISBN: 979-8-4007-1453-5
• COBISS.SI-ID: 251089411

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: grafi znanja, ontologija, taksonomija, strojno učenje, metapodatki

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0016
• Name: Komunikacijska omrežja in storitve

• Funder: EC - European Commission
• Funding programme: HE
• Project number: 101096456
• Name: An Artificial Intelligent Aided Unified Network for Secure Beyond 5G Long Term Evolution
• Acronym: NANCY