How to structure and access XML documents with ontologies
Data & Knowledge Engineering - Special issue on heterogeneous information resources need semantic access
A Discovery-Based Approach to Database Ontology Design
Distributed and Parallel Databases
Data modelling versus ontology engineering
ACM SIGMOD Record
Schema Evolution and Versioning: A Logical and Computational Characterisation
FoMLaDO/DEMM 2000 Selected papers from the 9th International Workshop on Foundations of Models and Languages for Data and Objects, Database Schema Evolution and Meta-Modeling
Ontological Engineering
Information Sharing on the Semantic Web
Information Sharing on the Semantic Web
Ontology Evolution: Not the Same as Schema Evolution
Knowledge and Information Systems
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
Database Processing: Fundamentals, Design, and Implementation (10th Edition)
Database Processing: Fundamentals, Design, and Implementation (10th Edition)
Query processing using ontologies
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
An ontology-based support for product conceptual design
Robotics and Computer-Integrated Manufacturing
Semi-automatic Generation of a Patient Preoperative Knowledge-Base from a Legacy Clinical Database
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part II
Ontologies versus relational databases: are they so different? A comparison
Artificial Intelligence Review
Bringing relational databases into the Semantic Web: A survey
Semantic Web - On real-time and ubiquitous social semantics
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Currently, knowledge from biological research is stored in hundreds of databases, counting only public accessible ones. Finding specific data in these is a challenging task which can be supported by ontologies describing them. The maintenance of a corresponding ontology is time consuming manual work, because research database schemas change rapidly. Our project will reduce the work by automating tasks, like a generation process and applying schema changes to the corresponding ontology. We call the proposed method coevolution, because database schema and ontology are allowed to evolve independently without ever losing their connection to each other. Our method consists of initial ontology generation, manual annotation and change propagation steps.