  Biomedical ontology refers to a shared conceptualization for a biomedical
domain of interest that has vastly improved data management and data sharing
through the open data movement. The rapid growth and availability of biomedical
data make it impractical and computationally expensive to perform manual
analysis and query processing with the large scale ontologies. The lack of
ability in analyzing ontologies from such a variety of sources, and supporting
knowledge discovery for clinical practice and biomedical research should be
overcome with new technologies. In this study, we developed a Medical Topic
discovery and Query generation framework (MedTQ), which was composed by a
series of approaches and algorithms. A predicate neighborhood pattern-based
approach introduced has the ability to compute the similarity of predicates
(relations) in ontologies. Given a predicate similarity metric, machine
learning algorithms have been developed for automatic topic discovery and query
generation. The topic discovery algorithm, called the hierarchical K-Means
algorithm was designed by extending an existing supervised algorithm (K-means
clustering) for the construction of a topic hierarchy. In the hierarchical
K-Means algorithm, a level-by-level optimization strategy was selected for
consistent with the strongly association between elements within a topic.
Automatic query generation was facilitated for discovered topic that could be
guided users for interactive query design and processing. Evaluation was
conducted to generate topic hierarchy for DrugBank ontology as a case study.
Results demonstrated that the MedTQ framework can enhance knowledge discovery
by capturing underlying structures from domain specific data and ontologies.
