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Biomedical ontology refers to a shared conceptualization for a biomedicaldomain of interest that has vastly improved data management and data sharingthrough the open data movement. The rapid growth and availability of biomedicaldata make it impractical and computationally expensive to perform manualanalysis and query processing with the large scale ontologies. The lack ofability in analyzing ontologies from such a variety of sources, and supportingknowledge discovery for clinical practice and biomedical research should beovercome with new technologies. In this study, we developed a Medical Topicdiscovery and Query generation framework (MedTQ), which was composed by aseries of approaches and algorithms. A predicate neighborhood pattern-basedapproach introduced has the ability to compute the similarity of predicates(relations) in ontologies. Given a predicate similarity metric, machinelearning algorithms have been developed for automatic topic discovery and querygeneration. The topic discovery algorithm, called the hierarchical K-Meansalgorithm was designed by extending an existing supervised algorithm (K-meansclustering) for the construction of a topic hierarchy. In the hierarchicalK-Means algorithm, a level-by-level optimization strategy was selected forconsistent with the strongly association between elements within a topic.Automatic query generation was facilitated for discovered topic that could beguided users for interactive query design and processing. Evaluation wasconducted to generate topic hierarchy for DrugBank ontology as a case study.Results demonstrated that the MedTQ framework can enhance knowledge discoveryby capturing underlying structures from domain specific data and ontologies.

MedTQ: Dynamic Topic Discovery and Query Generation for Medical Ontologies