The paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards, such as the ICD-10 and ATC classifications, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on the lightweight SKOS ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. The paper compares the current approach to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modelling and approach to the problem, while also pinpointing the advantages of Panacea. 1

Panacea, a semantic-enabled drug recommendations discovery framework