Terminologies: An e-Science perspective

Nicholas GibbinsIntelligence, Agents, Multimedia

University of Southampton

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)

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• …

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

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)

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)– …

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