Matching Biomedical Ontologies for Translational Medicine

large amount of data. To facilitate access to its desired parts, such a big
mass of data has been represented in structured forms, like biomedical
ontologies. On the other hand, constructing ontologies independently
from each other and storing them at different locations has brought
about challenges for automating high-level reasoning (e.g., answering
complex queries) about the knowledge represented in these ontologies.
One particular challenge, for instance, is ontology matching- to find
correspondences between semantically related concepts in different
ontologies. Ontology matching is required in particular for translational
medicine---to facilitate the exchange of clinical results and
experimental results for vital research like drug discovery. In this talk I
will describe briefly how ontologies, like SNOMED CT, that represent
knowledge about results of clinical research (e.g., trials of
investigational drugs on patients) can be matched with ontologies, like
LOINC, that represent knowledge about results of experimental
research (e.g., experiments to study structural properties of drugs).