Skeletons and Semantic Web Descriptions to Integrate Parallel Programming into Ontology Learning Frameworks

The current growth of biomedical knowledge is increasing the demand from the user community to automate the conversion of free text into a biomedical ontology. Thus ontology learning frameworks are gaining momentum as potential candidates to alleviate the current overload of biomedical information. Unfortunately the current problem at hand with these frameworks is scalability in terms of computing resources, processing power and the processing time required for biomedical experts and trained terminologists who use these frameworks. The current research study aims to tackle current difficulties in low-level parallel and distributed programming, e.g. the MPI standard, and probe the advantages for ontology learning frameworks in coupling high-level programming models together with formal semantic descriptions to enable a pay-back for the effort involved in skeleton-based parallel programming.