Using Ontology-Based Similarity Measures to Find Training Data for Problems with Sparse Data

In this article we present three similarity measures based on ontologies that are used to allow data mining in case of very sparse data. With the use of semantic knowledge associated to data sources we can aggregate data from similar sources and use that aggregated data to train a machine learning model. This article focuses on the ontology-based similarity measures that are required to compare data sources. We evaluate our similarity measures using a custom ontology created for fashion specific data and show that it is possible to identify similar data sources and gradually assign them similarity values.