1. Terminologies: An e-Science perspective Nicholas Gibbins Intelligence, Agents, Multimedia University of Southampton

2. What is e-Science? e-Science is science performed through distributed global collaborations Key characteristics: –Internet-based –Very large data collections –Terascale computing resources –High performance visualisations and services Central notion of Grid Computing (large-scale distributed computing)

3. UK e-Science projects GEODISE (design optimisation) Comb-e-chem (combinatorial chemistry) MyGrid (in silico biology) RealityGrid (condensed matter physics) AstroGrid (virtual observatory) GridPP (LHC grid) Climate Prediction …

4. The data deluge More data than we can cope with! The goal: the right information, to the right person, at the right time e-Science requires resource discovery –Find the relevant data for your experiments e-Science requires service discovery –Find the relevant services to help you conduct your experiments

5. The need for terminologies e-Science grids must be able to: –Manage experimental data –Manage metadata about data and services (for resource and service discovery) Need agreed languages, or terminologies, for expressing data and metadata Many types of terminology: –Controlled vocabularies –Taxonomies (hierarchical controlled vocabs) –Ontologies (taxonomies with relations, constraints)

6. e-Science Terminologies in use Some domain terminologies already exist: –IUPAC Gold Book, CML –Gene Ontology Consortium Some e-Science projects are writing their own: –Design process ontologies (GEODISE) –Bioinformatics ontologies (myGrid) –…–…

7. e-Science and the Semantic Web The Semantic Web is the next generation Web –The Web for machines –Machine-understandable information Several attractive features for e-Scientists –Ontology definition languages: RDF and OWL –Good integration with Web (and Grid) Services –Domain neutral –Growing tool support for SW technologies