MEX vocabulary: A lightweight interchange format for machine learning experiments

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

  • Diego Esteves
  • Diego Moussallem
  • Ciro Baron Neto
  • Tommaso Soru
  • Ricardo Usbeck
  • Markus Ackermann
  • Jens Lehmann

Over the last decades many machine learning experiments have been published, giving benefit to the scientific progress. In order to compare machine-learning experiment results with each other and collaborate positively, they need to be performed thoroughly on the same computing environment, using the same sample datasets and algorithm configurations. Besides this, practical experience shows that scientists and engineers tend to have large output data in their experiments, which is both difficult to analyze and archive properly without provenance metadata. However, the Linked Data community still misses a lightweight specification for interchanging machine-learning metadata over different architectures to achieve a higher level of interoperability. In this paper, we address this gap by presenting a novel vocabulary dubbed MEX. We show that MEX provides a prompt method to describe experiments with a special focus on data provenance and fulfills the requirements for a long-term maintenance.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Semantic Systems, SEMANTiCS 2015
EditorsAxel Polleres, Sebastian Hellmann, Josiane Xavier Parreira
Number of pages8
PublisherAssociation for Computing Machinery, Inc
Publication date16.09.2015
Pages169-176
ISBN (electronic)9781450334624
DOIs
Publication statusPublished - 16.09.2015
Externally publishedYes
Event11th International Conference on Semantic Systems, SEMANTiCS 2015 - Vienna University of Economics and Business (WU), Vienna, Austria
Duration: 16.09.201517.09.2015
https://2015.semantics.cc/

Bibliographical note

Horizon 2020 Framework Programme
Funding number: 644055

DOI