To pursue a systematic approach to the discovery of novel and inferable relationships between drugs and diseases based on mechanistic knowledge, we have sought to apply Semantic Web-based technologies to integrate heterogeneous data from pharmacological and biological domains. We have devised a knowledge framework, Disease-Drug Correlation Ontology (DDCO), constructed for semantic representation of the key entities and relationships. A collection of prior knowledge sets including pharmacological substance, drug target, pathway, disease and clinical features, and all interlinking properties were integrated using an RDF (Resource Description Framework) model derived from the semantic elements defined in the DDCO framework. Using the resulting RDF graph network, ontology-based mining and queries could identify embedded associations in this genome-phenome-pharmacome network. Several use-cases demonstrated that potentially powerful rewards could be obtained through semantic integration based on principles of drug action modeling.
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