Agibetov, AsanPatanè, GiuseppeSpagnuolo, MichelaAndrea Giachetti and Silvia Biasotti and Marco Tarini2015-10-142015-10-142015978-3-905674-97-2https://doi.org/10.2312/stag.20151293Biomedical ontologies helps discover hidden semantic links between heterogeneous and multi-scale biomedical datasets. Computational methods to ontology analysis may provide a semantic flavor to data analysis of biomedical mathematical models and help discover hidden links. In this paper we present Grontocrawler - a framework for visual ontology exploration applied to the biomedical domain. We define an OWL sublanguage - L and we present a methodology for transformation of L ontologies into directed labelled graphs. We then show how Social Network Analysis techniques (e.g., centrality measures, graph partitioning, community detection) can be used to i) filter the information presented to the user, and ii) provide a summary of knowledge encoded in the ontology. Finally, we show the application of ontology exploration in the biomedical domain to help discover hidden links between the biomedical datasets.H.3.3 [Information Search and Retrieval]Information filteringH.5.2 [Information Interfaces and Presentation]User interfacesGraphical user interfaces (GUI)I.2.4 [Computing Methodologies]Artificial IntelligenceKnowledge Representation Formalisms and MethodsJ.3 [Computer Applications]Life and Medical SciencesMedical information systems10.2312/stag.2015129367-76