  Community annotation of biological entities with concepts from multiple
bio-ontologies has created large and growing repositories of ontology-based
annotation data with embedded implicit relationships among orthogonal
ontologies. Development of efficient data mining methods and metrics to mine
and assess the quality of the mined relationships has not kept pace with the
growth of annotation data. In this study, we present a data mining method that
uses ontology-guided generalization to discover relationships across ontologies
along with a new interestingness metric based on information theory. We apply
our data mining algorithm and interestingness measures to datasets from the
Gene Expression Database at the Mouse Genome Informatics as a preliminary proof
of concept to mine relationships between developmental stages in the mouse
anatomy ontology and Gene Ontology concepts (biological process, molecular
function and cellular component). In addition, we present a comparison of our
interestingness metric to four existing metrics. Ontology-based annotation
datasets provide a valuable resource for discovery of relationships across
ontologies. The use of efficient data mining methods and appropriate
interestingness metrics enables the identification of high quality
relationships.
