The development and application of high-throughput technologies in biology leads to a rapid increase of data and knowledge and enables the possibility for a paradigm shift towards the personalized treatment of disease based on an individual patient’s genetic markup. Major challenges that biology faces today are to integrate data across different databases, domains, levels of granularity and species, and to make the information resulting from high-throughput experiments amenable to scientific analyses and the discovery of mechanisms underlying disease. In my talk, I will demonstrate how formal ontologies combined with recent progress in automated reasoning can be used to represent, integrate and analyze data resulting from high-throughput phenotyping experiments. I will show how an expressive formal representation of phenotype ontologies can lead to interoperability with biomedical ontologies of other domains, illustrate an ontology modularization approach that enables the use of automated reasoning over these ontologies and show how to integrate phenotype data across multiple species. Finally, I will demonstrate how measures of semantic similarity can be applied to analyze high-throughput phenotype data and reveal novel gene-disease associations and discuss how an ontology-based approach to the semantic integration of data in biomedicine can facilitate translational research and personalized medicine.