TY - CHAP

T1 - MEX vocabulary

T2 - 11th International Conference on Semantic Systems, SEMANTiCS 2015

AU - Esteves, Diego

AU - Moussallem, Diego

AU - Neto, Ciro Baron

AU - Soru, Tommaso

AU - Usbeck, Ricardo

AU - Ackermann, Markus

AU - Lehmann, Jens

N1 - Horizon 2020 Framework Programme Funding number: 644055

PY - 2015/9/16

Y1 - 2015/9/16

N2 - 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.

AB - 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.

KW - Data Provenance

KW - Interchange Format

KW - Machine Learning Experiments

KW - Vocabulary

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84962485425&partnerID=8YFLogxK

U2 - 10.1145/2814864.2814883

DO - 10.1145/2814864.2814883

M3 - Article in conference proceedings

AN - SCOPUS:84962485425

T3 - ACM International Conference Proceeding Series

SP - 169

EP - 176

BT - Proceedings of the 11th International Conference on Semantic Systems, SEMANTiCS 2015

A2 - Polleres, Axel

A2 - Hellmann, Sebastian

A2 - Parreira, Josiane Xavier

PB - Association for Computing Machinery, Inc

Y2 - 16 September 2015 through 17 September 2015

ER -