Knowledge-level querying of temporal patterns in clinical research systems.
Managing time-stamped data is essential to clinical research activities and often requires the use of considerable domain knowledge. Adequately representing this domain knowledge is difficult in relational database systems. 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 verification 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 inference. We show how the Semantic Web ontology and rule languages, OWL and SWRL, respectively, can support the temporal knowledge model needed to integrate low-level representations 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.
O'Connor
Martin J
MJ
Stanford Medical Informatics, Stanford University, Stanford, CA 94305, USA. martin.oconnor@stanford.edu
Shankar
Ravi D
RD
Parrish
David B
DB
Das
Amar K
AK
D035843:Biomedical Research; D019992:Databases as Topic; D016247:Information Storage and Retrieval; D051188:Knowledge Bases; D012660:Semantics; D012984:Software; D013995:Time; D018875:Vocabulary, Controlled