Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
ERONTO: a tool for extracting ontologies from extended E/R diagrams
Proceedings of the 2005 ACM symposium on Applied computing
Relational.OWL: a data and schema representation format based on OWL
APCCM '05 Proceedings of the 2nd Asia-Pacific conference on Conceptual modelling - Volume 43
Context Integration for Mobile Data Tailoring
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Ontology Matching
A methodology for a Very Small Data Base design
Information Systems
A comparison of two modelling paradigms in the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
Managing the History of Metadata in Support for DB Archiving and Schema Evolution
ER '08 Proceedings of the ER 2008 Workshops (CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM) on Advances in Conceptual Modeling: Challenges and Opportunities
Semantics and complexity of SPARQL
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Discovering the semantics of relational tables through mappings
Journal on Data Semantics VII
The ESTEEM platform: enabling P2P semantic collaboration through emerging collective knowledge
Journal of Intelligent Information Systems
Bringing relational databases into the Semantic Web: A survey
Semantic Web - On real-time and ubiquitous social semantics
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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 the relational and ontological worlds, in order to leverage the efficiency and scalability of relational technologies and the high level view of data and metadata proper of ontologies. The system we designed and developed achieves: (i) automatic ontology extraction from relational data sources and (ii) automatic query translation from SPARQL to SQL. Among the others, we focus on two main applications of this novel technology: (i) ontological publishing of relational data, and (ii) 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.