An ontology-driven mediator for querying time-oriented biomedical data

Most biomedical research databases contain considerable amounts of time-oriented data. However, temporal knowledge about the contextual meaning of such data is not usually represented in a principled fashion. As a result, investigators often develop custom techniques for temporal data analysis that are difficult to reuse. We addressed this problem by developing a set of knowledge-driven methods and tools for temporally representing and querying biomedical data, and have integrated them using a mediator approach. A central issue driving our work is a need to integrate temporal representations of data in relational databases with the domain-specific semantics of temporal patterns used in querying. This paper presents a formal temporal knowledge model using the Semantic Web ontology and rule languages, OWL and SWRL, respectively. The model informs the mediator of the temporal semantics used for data analysis. We show that our approach provides the computational foundation for much-needed software to make sense of complex temporal patterns in two biomedical research domains.