ML-Schema: An interchangeable format for description of machine learning experiments

Tracking #: 2134-3347

This paper is currently under review
Authors: 
Gustavo Publio
Agnieszka Lawrynowicz
Larisa Soldatova
Pance Panov
Diego Esteves1
Joaquin Vanschoren
Tommaso Soru1

Responsible editor: 
Guest Editors Semantic E-Science 2018

Submission type: 
Ontology Description
Abstract: 
In this paper, we present the ML-Schema, proposed by the W3C Machine Learning Schema Community Group. ML-Schema is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. ML-Schema, a canonical format, resulted of more than seven years of experience of different research institutions. We discuss the main challenge in the development of ML-Schema, which have been to align existing machine learning ontologies and other relevant representations designed for a range of particular purposes following sometimes incompatible design principles, resulting in different not easily interoperable structures. The resulting ML-Schema can now be easily extended and specialized allowing to map other more domain-specific ontologies developed in the area of machine learning and data mining.
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Under Review