- Physicians and medical decision-support applications, such as for diagnosis, therapy, monitor-ing, quality assessment, and clinical research, reason about patients in terms of abstract, clinically mean-ingful concepts, typically over significant time periods. Clinical databases, however, store only raw, time-stamped data. Thus, there is a need to bridge this gap. We introduce the Temporal Abstraction Language (TAR) which enables specification of abstract relations involving raw data and abstract concepts, and sup-ports query answering. We characterize TAR knowledge bases that guarantee finite answer sets and shortly explain why a complete bottom-up inference mechanism terminates. The TAR language was implemented as the inference component termed ALMA in the distributed mediation system IDAN, which integrates a set of clinical databases and medical knowledge bases. Initial experiments with ALMA and IDAN on a large oncology-patients dataset are highly encouraging.