Learning Description Logic Ontologies by Associate Professor Ana Ozaki, Department of Informatics, University of Bergen
Description logic ontologies have been used to represent the relevant knowledge of a domain of interest in a formal and machine-processable format. Such knowledge can be applied to constrain the space of hypotheses in learning tasks, to integrate data coming from multiple sources, to support query answering, among others. Understanding how to describe domain knowledge in a concise and interpretable way is a fundamental challenge in artificial intelligence. In this presentation, we will look into some examples of expressions and see which ones we can and cannot represent within classical description logic. We will also highlight some approaches in machine learning and data mining that have been applied to (semi)-automate the process of building description logic ontologies.