Several approaches have been proposed for the generation of application ontologies from relational databases. Most of these approaches, propose a fully automatic process based on unrealistic assumption, where the input database is well designed, up-to the third normal form (3NF). Real-World databases may contain irrelevant, missing or erroneous information to the ontology learning process. Preparing databases before ontology learning is quite rare. We propose in this paper a methodology for Database Preparation (DBP), composed of three sub-processes: the extraction of a Business Database (BDB), the cleaning of the BDB, and the enrichment of the cleaned BDB. A proof-theoretical case study shows that the proposed methodology is feasible and useful.
We present in this paper, one important feature of our approach that draws on a semantic enrichment process of the RDB model for learning application ontologies from Relational Databases (RDB)s. In fact, our approach supports collaborative work on the enrichment process and considers the partitioning of the input RDB model into a set of business modules. The objectives behind the collaborative support are threefold: (i) to allow each user to work on the RDB part that fits his/her domain knowledge; (ii) to save time in manual semantic enrichment; and (iii) to make easiest human evaluation of the ontology. We present the principle behind our approach, its features and its benefits.
Renal transplantation is an ever-increasing procedure. Baseline DTPA renography is routinely performed in these patients in many centers to assess transplant perfusion and function. This report describes 2 cases of unusual appearance at renography resulting from the native polycystic kidneys.