An OWL Ontology Representation for Machine-Learned Functions Using Linked Data
Jingyuan Xu, Hao Wang, Henry Trimbach · 2016
This paper proposes a method to represent classifiers or learned regression functions using an OWL ontology. Also proposed are methods for finding an appropriate learned function to answer a simple query. The ontology standardizes variable names and dependence properties, so that feature values can be given by users or found on the semantic web.