ML Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies

Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar

Abstract: The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, 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. It can be easily extended and specialized and it is also mapped to other more domain-specific ontologies developed in the area of machine learning and data mining. In this paper we overview existing machine learning interchange formats and present the first release of ML-Schema, a canonical format resulted of more than seven years of experience among different research institutions. We argue that exposing semantics of machine learning algorithms, models, and experiments through a canonical format may pave the way to better interpretability and to realistically achieve the full interoperability of experiments.

TL;DR: The 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.

Keywords: Machine Learning, Linked Data, ML-Schema, MEX, Expose, OntoDM, DMOP, OpenML, WASOTA, METArchive