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.
AKSW, Universität Leipzig
www.aksw.org
Diego Esteves is currently a PhD student at the AKSW - Smart Data Analytics Research Group. Before starting at the AKSW group in Leipzig he worked for over 10 years in large companies such as Accenture, B2W Inc., Wilson Sons and BTG Pactual Investment Bank.
Software Engineer
I am a research engineer that currently is working on large-scale semantic information management and integration to further the efforts of the DBpedia Association. Additionally, I also carry a strong interest in Natual Language Processing (lately in particular in Computational Morphology and Named Entity Recognition).
