Schema Evolution publication categorizer

Relational databases have been designed to store high volumes of data and to provide an efficient query interface. Ontologies are geared towards capturing domain knowledge, annotations, and to offer high-level, machine-processable views of data and metadata. The complementary strengths and weaknesses of these data models motivate the research effort we present in this paper. The goal of this work is to bridge relational and ontological worlds, in order to leverage the efficiency and scalability of relational technologies to support an ontological, high level view of data and metadata. The system we designed and developed achieves: (i) automatic ontology extraction from relational data sources, (ii) automatic query translation from SPARQL to SQL, and (iii) a highly efficient scheme for storing data-intensive ontologies. Among the others, we focus on three main applications of this novel technology: (i) ontological publishing of relational data, and (ii) relational storage for data-intensive ontologies, and (iii) automatic relational schema annotation and documentation. The system has been designed and tested against real life scenarios from Big Science projects, which are used as running examples throughout the paper.