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School of Electronics and Computer Science

Explore the driving forces for the next 10 years in electronics and computer science through key topics like Open Access debate, Moore’s Law, Semantic Web, and Research Publications. Discover how faster and smaller devices are shaping the future, with a focus on semantic web technologies and structured data sets. Dive into ontologies, gene ontology, and core technologies like RDF and XML that underpin the Semantic Web. Get insights on the AKT project, knowledge management infrastructures, and the impact on dynamic communities of practice. Unveil the role of e-Science in scientific discovery and the importance of data publication and dissemination in driving change.

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School of Electronics and Computer Science

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  1. School of Electronicsand Computer Science Knowledge Repositories: The Next 10 Years Professor Nigel Shadbolt

  2. Drivers for Change • The Open Access debate and the Open Archive Initiative • Moore’s Law • The Semantic Web • The Nature of Research Publications

  3. Drivers for Change • The Open Access debate and the Open Archive Initiative • Moore’s Law • The Semantic Web • The Nature of Research Publications

  4. Faster and Smaller • Devices are getting smaller and faster all the time • Moore’s Law has held for 40 years • This leads to orders of magnitude • Increase in power • Increase in memory • Decrease in size • Decrease in cost • Constant migration and obsolescence • Our processors will have very limited shelf life • Our storage does too • Our physics does too

  5. Drivers for Change • The Open Access debate and the Open Archive Initiative • Moore’s Law • The Semantic Web • The Nature of Research Publications

  6. Making the Web Semantic…

  7. Via meta content… That is machine readable…. This is a type of object event and this is its title This is the URL of the web page for the event This is a type of object photograph and the photograph is of Tim Berners-Lee Tim Berners-Lee is an invited speaker at the event

  8. Web data set (XHTML) Can Annotate Anything • Publications… • Databases… • Metadata on scientific structures

  9. Oncogene(MYC): Found_In_Organism(Human). Gene_Has_Function(Transcriptional_Regulation). Gene_Has_Function(Gene_Transcription). In_Chromosomal_Location(8q24). Gene_Associated_With_Disease(Burkitts_Lymphoma). Vocabulary (RDFS) NCI Cancer Ontology (OWL) <meta> <classifications> <classification type="MYC” subtype="old_arx_id">bcr-2-1-059</classification> </classifications> </meta> BioMedCentral Metadata (XML) Web data set (XHTML) The SW Community: Structured Spaces • Linkage of heterogeneous information • web content • databases • meta-data repository • multimedia • Via ontologies as information mediation structures • Using Semantic Web languages

  10. Ontologies: Fundamental Building Blocks of the Semantic Web

  11. The Ontology • A shared conceptualisation of a domain • Provides the semantic backbone • Lightweight and is deployed using a W3C recommended standard language

  12. Genetics: Gene Ontology • One of the earliest examples of the benefits of ontologies • Integration and interoperability were big wins • Specific tool support • Considerable resources invested and continuing in maintenance • Spawned more generic biological ontology efforts

  13. OWL RDF(S) RDF XOL Topic Maps SMIL HTML XML + Name Space + XML Schema Unicode URI Standards are fundamental

  14. AKT started Sept 00, 6 years, £8.8 Meg, EPSRC www.aktors.org Around 65 investigators and research staff Advanced Knowledge Technologies IRC

  15. Infrastructures and Components • Built core infrastructures • Constructed component technologies that cover the knowledge life cycle in a number of applications

  16. Exemplar Technology: ClassAKT

  17. Semantic Spaces: Integrating Knowledge Technologies

  18. 24/7 update of content Content continually harvested and acquired against community agreed ontology Easy access to information gestalts - who, what, where Hot spots Institutions Individuals Topics Impact of research citation services etc funding levels Changes and deltas Dynamic Communities of Practice… The CS AKTive Space:International Semantic Web Challenge Winner

  19. Components of a Solution • Information sets • Ontology to mediate information sets • Semantic Storage Capability • Query Capability on Storage • Network and graph analysis tools • Browsing and Visualisation tools

  20. CS AKTiveSpace

  21. Extending the model

  22. EPSRC: Knowing what they know datasources gatherers and mediators ontology knowledge repository(triplestore) applications

  23. Visualising Interaction

  24. Visualising Interaction: Programmes

  25. Drivers for Change • The Open Access debate and the Open Archive Initiative • Moore’s Law • The Semantic Web • The Nature of Research and Publication • Knowledge Mapping

  26. New ways of discovery: e-Science • A large part of scientific discovery is now a joint human machine endeavour • Without considerable compute power no hope of progress • Examples from physics, astronomy, biology, chemistry and engineering

  27. Virtual Learning Environment Reprints Peer-Reviewed Journal & Conference Papers Technical Reports LocalWeb Preprints & Metadata Institutional Archive Publisher Holdings Certified Experimental Results & Analyses Data, Metadata & Ontologies Undergraduate Students Digital Library Graduate Students E-Scientists E-Scientists E-Scientists Grid 5 E-Experimentation Entire E-Science CycleEncompassing experimentation, analysis, publication, research, learning

  28. The need for xtl-Prints Combechem DATA PUBLICATION DISSEMINATION Combichem

  29. Structural Eprints

  30. Drivers for Change • The Open Access debate and the Open Archive Initiative • Moore’s Law • The Semantic Web • The Nature of Research and Publication • Knowledge Mapping

  31. Increasing Use of Value Added Services

  32. Communities of Authors • An example of a small coauthorship network depicting collaborations among scientists at a private research institution. Newman, M. E. J. (2004) • Web services to run over archives at varying grainsize

  33. Evolving Domains: Impact Analysis • Three time periods in the PNAS high-impact map show the progression from the basic gene and protein work and techniques that dominated the 1980s to more diverse applications in the 1990s (Boyack, Kevin W. 2004)

  34. Bursting onto the scene: New Topics Fig. 2. • Co-word space of the top 50 highly frequent and bursty words used in the top 10% most highly cited PNAS publications in 1982-2001

  35. Self Organising Maps: Topic Landscapes • Use of k-means clustering in combination with a term dominance landscape to support semantic zooming. Skupin et al 2004

  36. Detecting Key Moments: Pathfinder • A 624-node merged network with global pruning by using Pathfinder Chen (2004)

  37. A future… • With institutional OAI at its heart… • A semantic web of knowledge • Knowledge repositories as key holdings • Knowledge mapping services increasing in range and capability • Beyond bibliometrics…

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