Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The biological integration system
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Knowledge representation for information integration
Information Systems - Special issue on web data integration
Formal Concept Analysis: Foundations and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Combining classifiers to identify online databases
Proceedings of the 16th international conference on World Wide Web
Many-Valued Concept Lattices for Conceptual Clustering and Information Retrieval
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Feta: a light-weight architecture for user oriented semantic service discovery
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
BioRegistry: a structured metadata repository for bioinformatic databases
CompLife'05 Proceedings of the First international conference on Computational Life Sciences
Using Domain Knowledge to Guide Lattice-based Complex Data Exploration
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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Biological databases are blooming today at an increasing rate to deal with the huge amount of data produced by genomic and post-genomic research. The need for a well-maintained searchable directory is therefore an important issue for a good exploitation of these databases. The BioRegistry repository is automatically generated from a publicly available list of biological databases (The Molecular Biology Database Collection published in Nucleic Acids Research) and aims at associating content metadata with each database in view of database retrieval and/or discovery. Such content metadata are either simple keywords or terms belonging to a medical thesaurus. Querying modalities including a search by semantic similarity are described. The use of conceptual clustering methods is proposed to build a semantic classification of biological databases enabling browsing through the BioRegistry repository and discovering previously unknown databases.