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MicroRNAs (miRNAs) play important roles in various biological processes by regulating their target genes. Therefore, miRNAs are closely associated with development, diagnosis, and prognosis for many diseases. The prediction of miRNA targets remains a challenging task for biologists because it involves an extremely large amount of data sources to be explored: to manually integrate information of identified targets and related information from various sources is time-consuming and error-prone; most of all, it is subject to biologists' limited prior knowledge. In this paper we investigated an ontology-based knowledge sharing framework to assist biologists in unraveling important roles of miRNAs in human disease in an automated and more efficient manner, (i) We developed the very first domain-specific ontologies in the miRNA field, Ontology for MicroRNA Target (OMIT), (ii) According to the global metadata model defined in ontologies, heterogeneous data sources were annotated and seamlessly integrated and stored into a central Resource Description Framework (RDF) data repository, (iii) We then enabled ontology-based queries, instead of traditional SQL queries, by inferring new statements from RDF data triples. Consequently we were able to acquire hidden knowledge originally implicit and unclear, yet critical, to biologists.