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 light-weight 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.
%0 Conference Paper
%1 estevesMEX2015
%A Esteves, Diego
%A Moussallem, Diego
%A Neto, Ciro Baron
%A Soru, Tommaso
%A Usbeck, Ricardo
%A Ackermann, Markus
%A Lehmann, Jens
%B 11th International Conference on Semantic Systems (SEMANTiCS 2015), 15-17 September 2015, Vienna, Austria
%D 2015
%K 2015 MOLE ackermann aligned aligned-project baron esteves group_aksw lehmann mack mex mole moussallem neto simba soru usbeck
%T MEX Vocabulary: A Lightweight Interchange Format for Machine Learning Experiments
%U http://svn.aksw.org/papers/2015/SEMANTICS_MEX/public.pdf
%X 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 light-weight 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.
@inproceedings{estevesMEX2015,
abstract = {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 light-weight 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.},
added-at = {2024-06-18T09:44:39.000+0200},
author = {Esteves, Diego and Moussallem, Diego and Neto, Ciro Baron and Soru, Tommaso and Usbeck, Ricardo and Ackermann, Markus and Lehmann, Jens},
bdsk-url-1 = {http://svn.aksw.org/papers/2015/SEMANTICS_MEX/public.pdf},
biburl = {https://www.bibsonomy.org/bibtex/293300cea64d28b12317255ac586a3569/aksw},
booktitle = {11th International Conference on Semantic Systems (SEMANTiCS 2015), 15-17 September 2015, Vienna, Austria},
interhash = {4f2cc17e9898f457f46810378ec36ea7},
intrahash = {93300cea64d28b12317255ac586a3569},
keywords = {2015 MOLE ackermann aligned aligned-project baron esteves group_aksw lehmann mack mex mole moussallem neto simba soru usbeck},
timestamp = {2024-06-18T09:44:39.000+0200},
title = {MEX Vocabulary: A Lightweight Interchange Format for Machine Learning Experiments},
url = {http://svn.aksw.org/papers/2015/SEMANTICS_MEX/public.pdf},
year = 2015
}