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Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge, which is difficult to support within database sys- tems. As a result, there is a need for principled methods to overcome the disconnect between the database representation of time-oriented research data and corresponding knowledge of domain-relevant concepts. In this paper, we present a set of methodologies for undertaking knowledge-level querying of temporal patterns, and discuss its application to the verifica- tion of temporal constraints in clinical-trial applications. Our approach allows knowledge generated from query results to be tied to the data and, if necessary, used for further infer- ence. We show how the Semantic Web ontology and rule lan- guages, OWL and SWRL, respectively, can support the tempo- ral knowledge model needed to integrate low-level represen- tations of relational data with high-level domain concepts used in research data management. We present a scalable bridge-based software architecture that uses this knowledge model to enable dynamic querying of time-oriented research data.

Knowledge-Level Querying of Temporal Patterns in Clinical Research Systems.