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Incremental ontology-based integration for translational medical research

Translational medical research aims at transforming discoveries from basic sciences into diagnostic and therapeutic applications. This process is centered around the integration of heterogeneous biomedical data. Due to the increasing complexity and sensitivity of the data involved, structured domain knowledge and concepts for data privacy are highly relevant. In this thesis, a robust ontology-based approach for integrating primary data, domain knowledge and metadata is proposed. It advocates an incremental methodology that aims at carrying out semantic integration in a demand-oriented manner. A novel federation technique for graph-structured data allows researchers to maintain control over their local datasets. The system is able to access common biomedical data sources and supports various semantic integration and anonymization processes. A comprehensive graphical user interface allows data integrators to administer the data space.