Semantic Web and the Grid Brian Matthews

1. Semantic Web and the Grid Brian Matthews

2. Contents • A Changing Environment for Research • The Semantic Web • The Grid • The Semantic Grid • What does that mean for CRIS and OA? • Conclusion

3. A Future Environment for Research • OA and CRIS as drivers for the management and access to information • Need for shared metadata and exchange mechanisms • Central control impossible/undesirable • a loosely coupled federated approach • based on common interchange and access standards • W3C, GGF, IETF, OASIS, EuroCRIS, WfMC etc • Changes in technology • resource discovery • enables access • Two leading technology opportunities • Semantic Web and the GRID

4. The Semantic Web Adding machine readable information about the web, to the web. • The Web is chaotic - why are resources are linked? • Imagine a library where all the books have the same text on the cover, and the only catalogues are compiled by photocopying the books, cutting up the copies, and arranging the words in the order of frequency. Johan Hjelm • Google is great at returning all the pages on the web that mention "Tim Berners-Lee“ • But what about returning those pages written by Tim Berners-Lee? • The Semantic Web adds well-defined meaning to describe the Web (Metadata). The Semantic Web is an extension of the current web in which the information is given well-defined meaning, better enabling computers and people to work in cooperation • Tim Berners-Lee, James Hendler and Ora Lassila The Semantic Web, Scientific American, May 2001

5. Add Meaning to Resources

6. “The Web of Trust” Reasoning over statements about resources Formalism for defining and sharing vocabularies Language of triples for describing resources Basic Syntax of the Web Semantic Web:A Layered Architecture

7. Machine Readable Meaning • Meaning becomes machine readable - so software agents can use it for: • Improving searches (indexing, cataloguing) • Convey information on the usage of the resource (access control, IPR). • Convey information on the actors involved (user preferences, device profiles, privacy preferences) • Give third party opinions on the content of another site (rating services, brokering). • Essentially, Metadata of all kinds

8. Progress so far • A lot more than you might think! • Base standards are now mature: • RDF, RDF Schema, OWL • many others reaching maturity: • Many shared vocabularies emerging • DC, DMoz, Prism, FOAF, VCard, SKOS, RSS…. • Lots of RDF out there! • Mozilla, Adobe, RSS, • Still a lot of work to do • reasoning, trust, provenance, tools, • But we are getting there!

9. Users CRIS portal Query distributor and collator  Thesaurus Service   CRIS 1 CRIS 2 Example: SKOS • Community effort led by CCLRC/W3C • A vocabulary to represent Thesauruses • Heavily used in the library community • but traditionally locked up in institutional databases • Allow people to share controlled vocabularies for cataloguing resources • Examples • GEMET – environmental data • GCL – e-Government • English Heritage • W3C glossary

10. Example: Simile • Project of MIT + HP Labs + W3C • Publishing digital library information onto the semantic web. • Make semantic interoperability of metadata a reality for digital libraries by: • providing reusable software for browsing, searching and mapping heterogeneous metadata • using semantic web technologies • identifying issues, gaps and best practices • allow libraries to share information • Provide semantic web browser, and RDF based datasets • for art history information • combined from different sources • Using SKOS as the thesaurus format. • OA within the Semantic Web

11. Semantic Web and OA • Semantic web provides an underlying mechanism to support OA: • common metadata • data exchange mechanism • searching and browsing across web • query language and logic • interoperability • lose coupling. • Can also support CRIS this way too. • CERIF in OWL (Lopatenko) • And also Data Sets • CCLRC Metadata format – also in RDF Schema • But that is not the only main technology change

12. The Grid The Grid provides an environment that enable software applications to integrate instruments, displays, computational and information resources that are managed by diverse organisations in widespread locations. • Provide access to a global distributed computing environment • via authentication, authorisation, negotiation, security • Identify and allocate appropriate resources • interrogate information services -> resource discovery • enquire current status/loading via monitoring tools • decide strategy - eg move data or move application • (co-)allocate resources -> process flow • Schedule tasks and analyse results • ensure required application code is available on remote machine • transfer or replicate data and update catalogues • monitor execution and resolve problems as they occur • retrieve and analyse results - eg using local visualization • So far typically in large-scale science and engineering.

13. To make this happen you need . . . • agreed protocols (cf WWW -> W3C) • defined application programming interfaces (APIs) • existence of directories for both system and application • distributed data management • availability of current status of resources • monitoring tools • accepted authentication procedures and policies • network traffic management provided by Grid-based toolkits and services

14. GRID History • mid 90s – Globus • The GRID Bible • Based on “traditional” protocols (IETF) • Taken up by e-Science • Standardised via GGF • Now converging with Web • Web Services - WSRF

15. Example: NASA IPG Unitary Plan Wind Tunnel Multi-source Data Analysis desktop & VR clients with shared controls real-time collection archival storage Computer simulations

16. Example: DataGrid • LHC will produce several PBs of data per year for at least 10 years from 2005 . • Data analysis will be carried out by farms of 1000’s of commodity processors (the “computing fabric”) in each of about 10 regional Tier1 centres - RAL is UK Tier1 • Each Tier1 centre will need to hold several PBs of raw data and results of physics analysis • Strong focus on middleware and testbeds - open source

17. Semantic Grid What Next? The Semantic Grid Semantic Web machine readable semantics GRID WEB distributed computation thanks to Dave de Roure

18. Semantic Grid What Next? The Semantic Grid • Current GRID is “hand-crafted” • users have to know a lot about the available resources • users have to “write scripts” to use the GRID • Add machine readable semantics (metadata) • The Semantic GRID Semantic Web machine readable semantics GRID WEB distributed computation “the GRID is an application of the Semantic Web” de Roure, Goble thanks to Dave de Roure

19. But what does that mean? • more automation • more negotiation • more autonomy • more self-monitoring and control • use of autonomous agents • Will make the Grid much more like the electricity Grid • You don’t need to know where the stuff comes from.

20. Semantic Grid Example • Major UK e-Science project • Bio-informatics • In-silico experimentation • www.mygrid.org.uk • Based on a GRID architecture • Uses Semantic Web Tools for • Workflow and service discovery • Prior to and during enactment • Semantic registration • Workflow assembly • Semantic service typing of inputs and outputs • Provenance of workflows and other entities • Experimental metadata glue • Use of RDF, RDFS, DAML+OIL/OWL • Instance store, ontology server, reasoner • Materialised vs at point of delivery reasoning. • myGrid Information Model • About to join them to work on workflow

21. What does this mean for CRIS & OA? Ambient, Pervasive Access Portal with knowledge-assisted user interface SCIENTIFIC DATASETS metadata CRIS metadata PUBLICATIONS metadata publish validate Digital Curation Facility GRIDs The Semantic Grid is what makes this work!

22. Local metadata Local metadata Local metadata Local metadata Local data Local data Local data Local data DA 1 IR 2 IR 1 DA 2 Example: Validation • Validate results from paper • need to access paper (OA) • need to link to data (and metadata) • need to access analysis and visualisation tools • need common metadata and access to resources across Grid. Data Portal Pub Portal Grid middleware

23. Submit proposal Prepare experiment Generate results Analyse results Write report Provenance metadata access conditions data description data location Related material + + + + Example: Science as a process • Within a Grid environment DA IR CRIS Collecting the metadata can then become part of the experimental support environment

24. Example: the Nature of a Publication • Traditional publication as continuous text, with static graphs and images • Change the notion of the content of the publication • hypertext • include active components – links to simulations, visualisations • a much more dynamic document • a multimedia presentation • How will publishers cope? • How will publication archives cope?

25. Resource discovery good metadata common formats standards Resource negotiation for data and services Quality of service guarantees Policies and contracts Security and trust Provenance Monitoring and payment Work flow Reasoning tools Autonomous agents Autonomic systems Links to legacy especially database systems querying systems Collaborative working environments Design methods So how to achieve this?

26. Progress • Moving quite fast on this from many different directions • e-Science • Next Generation Grid Report • FP6/7 • Semantic Grid at GGF • OA initiatives • Digital Curation a major concern • Real exciting opportunity to pull it all together

27. Conclusions • Semantic Grid and Open Access • enables • enabling • CRIS as an information coordinator • Archiving and curation • need to archive much more • data, programs, visualisation and analysis tools, formats, calibrations, versions, OS …… • Workflow a key component • Metadata collection and maintenance is a big problem. B.M.Matthews@rl.ac.uk