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Machine learning with OWL ontologies

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The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing ontologies and knowledge bases in machine learning methods can add domain-specific background knowledge and enforce consistent predictions with respect to domain knowledge. I will demonstrate how to use ontologies in machine learning, both to incorporate background knowledge and to enforce consistent predictions, focusing on three main approaches: graph-based learning which treats ontologies as graphs; syntactic approaches which treat axioms similar to natural language statements; and model-based approaches which construct models with a geometric interpretation. All the approaches combine distributional and symbolic representations in different ways, and I will discuss their advantages, disadvantages, and limitations.

Bio: Robert Hoehndorf is an Associate Professor in Computer Science at King Abdullah University of Science and Technology (KAUST) in Thuwal. Prior to joining KAUST , Robert had research positions at Aberystwyth University, the University of Cambridge, the European Bioinformatics Institute, and the Max Planck Institute for Evolutionary Anthropology. His research focuses on the development and application of knowledge-based algorithms with applications in biology and biomedicine.

This talk is part of the Data Science and Computational Statistics Seminar series.

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