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School of Electronics and Computer Science Web Intelligence Professor Nigel Shadbolt

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  • School of Electronicsand Computer Science

    Web IntelligenceProfessor Nigel Shadbolt

  • Why distributed and digital is good

  • The work of many people

    Harith AlaniSteve HarrisNick GibbinsYannis KalfoglouKieron OHaraDavid DupplawBo HuPaul LewisSrinandan DashamapatraDuncan Macrae-SpencerHugh Glaser

    Les CarrDavid de RoureWendy HallMike BradyDavid HawkesYorick WilksEnrico MottaCarole GobleSimon CoxAndy Keane:

  • Drivers

    Moores Law and Powers of 10The WWWMaking the Web SemanticIntelligence on the WebResearch on the Semantic WebFutures Challenges

  • Faster and Smaller

    Devices are getting smaller and faster all the timeMoores Law has held for 40 yearsThis leads to orders of magnitude

    Increase in powerIncrease in memoryDecrease in sizeDecrease in cost

    Constant migration and obsolescence

    Our processors will have very limited shelf lifeOur storage does tooOur physics does too

  • Alters our Perspectives

    In May 1997 Deep Blue beat world chess champion Kasparov

    It won the six-game rematch 3.5-2.5

    The system derives its playing strength mainly out of brute force computing power

    It is a massively parallel, 32-node, RS/6000, SP-based computer system enhanced with 256 special purpose VLSI chess processors

    Evaluating 100,000,000 positions per second.

  • Drivers

    Moores Law and Powers of 10The WWWMaking the Web SemanticIntelligence on the WebResearch on the Semantic WebFutures Challenges

  • The Magic of the Network Effect

    On the order of 108 users Used in every country on Earth

    A tiny percentage is trained in any way

    On the order of 1010 indexed web resources (text) in Google etc

    Massively distributed and open

    A set of protocols and languages driven by a strong standards approach

    The magic is the network effect

  • Exponential Development

    A new era 1990nxoc01.cern.ch the first web serverhttp://nxoc01.cern.ch/hypertext/WWW/TheProject.html - the first web pageIn 1992 26 web servers1993 200 web servers1994 Mosaic browser 1000 times more traffic than on the first web server1998 -329 million pagesThe International Telecommunications Union estimated 665 million users 2002

  • Drivers

    Moores Law and Powers of 10The WWW: Blessing and CurseMaking the Web SemanticIntelligence on the WebResearch on the Semantic WebFutures Challenges

  • Making the Web Semantic

  • Via meta content

    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

    That is machine readable.

  • Can Annotate Anything

    Publications

    Databases

    Metadata on scientific structures

    Web data set (XHTML)

  • The SW Community: Structured Spaces

    Linkage of heterogeneous information

    web contentdatabasesmeta-data repositorymultimedia

    Via ontologies as information mediation structuresUsing Semantic Web languages

    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).

    NCI Cancer Ontology (OWL)

  • Ontologies: Building Blocks for the SW

    A shared conceptualisation of a domain

    Provides the semantic backbone for applications

    Lightweight and is deployed using a W3C recommended standard language

  • Ontologies offer.

    CommunicationNormative modelsNetworks of relationshipsConsistent and unambiguousIntegrate multiple perspectives

    Inter-operability and Integration: Sharing & Reuse

    Inter-linguaSpecificationsReliability

    ControlControlled vocabularies Accurate data collection or retrievalClassificationFinding, sharing, discovering, navigation, indexing

  • Genetics: Gene Ontology

    One of the earliest examples of the benefits of ontologiesIntegration and interoperability were big winsSpecific tool supportConsiderable resources invested and continuing in maintenanceTranslation into DLsSpawned more generic biological ontology efforts

  • Manufacturing: Aerospace

    Considerable work on ontologies for products and componentsUsed in all stages of the life cycle, from design to in service maintenanceNeed for multiple perspectives e.g

    Whole engineHeat transferCost modelManufacturingAssembling/Maintenance

  • Ontologies: Observations

    In any domainUsually highly implicitPoorly documentedLikely to be ambiguous, vague, inconsistent

    When modellingInteraction Problem: tasks influence ontologiesIntegration Problem: integrating multiple ontologiesModularity Problem: how to modularise and what grain size?

    MaintenanceOngoing maintenance overheadOntologies evolve and changeDesign rationale is important

    UpsideThey do facilitate interoperabilityThey do enhance reuseThey are becoming part of the infrastructure

  • Standards are fundamental

    HTML XML + Name Space + XML SchemaTopic Maps SMIL

    RDF(S)XOL

    OWL

    RDF

    Unicode URI

  • Drivers

    Moores Law and Powers of 10The WWW: Blessing and CurseMaking the Web SemanticIntelligence on the WebResearch on the Semantic WebFutures Challenges

  • Advanced Knowledge Technologies IRC

    AKT started Sept 00, 6 years, 8.8 Meg, EPSRC

    www.aktors.org

    Around 65 investigators and research staff

  • Infrastructures and Components

    Built core infrastructuresConstructed component technologies that cover the knowledge life cycle in a number of applications

  • Exemplar Technology: ClassAKT

  • Component Technologies: Modelling

  • Component Technologies: Modelling

  • Integrating Semantic Spaces

    Exploit core infrastructureIntegrate component technologies from the knowledge life cycle for an application

  • The CS AKTive Space:International Semantic Web Challenge Winner

    24/7 update of contentContent continually harvested and acquired against community agreed ontologyEasy access to information gestalts - who, what, whereHot spots Institutions Individuals TopicsImpact of research citation services etc funding levels Changes and deltasDynamic Communities of Practice

  • CS AKTiveSpace

  • Extending the model

  • datasources

    ?

    Mediate and Aggregate: UK Research Councils

  • EPSRC: Knowing what they know

    datasources

    gatherersand

    mediators

    ontology knowledge repository

    (triplestore)

    applications

  • Raw CSV dataHeterogeneous tables

    Processed RDF informationUniform format for files

    Mediate and Aggregate: Ontologies

  • Visualising Interaction

  • Visualising Interaction: Programmes

  • Integrating Semantic Spaces

    Exploit core infrastructureIntegrate component technologies from the knowledge life cycle for an application

  • Collaborative Medical Decision Making MIAKT:Integrating Semantic Spaces

    Diverse and heterogeneous contentClinical examination

    Notes

    ImagingX-ray, UltrasoundMRI

    MicroscopyHistopathology

    TreatmentProtocol RecordsRe-assessment

    Medical RecordsCase setsIndividual patient records

    Published backgroundEpidemiologyMedical Abstracts

  • Why This Domain?

    The user and patient perspective 10% of women develop breast cancer during their lives3 million screening cases in UK per year: 8~25% cancers are missed by radiologists; 70~80% biopsies turn out to be benign; inter- and intra-radiologist variability is typically 30%.

    AKT perspectiveLarge amounts of data, information and knowledgeCollaboration among different domain experts but little technical support for the collaborative elements of the problem and little technical support for the semantics of the domain

    MIAS perspectiveDeployment of methods as web and grid servicesNeed for information management

  • Multi-disciplinary Assessment: The Medics

    Different domains of expertise overlaidBreast imaging X-ray, ultrasound, MRIClinical examinationNon-aggressive methods Fine Needle AspirationHistopathology SlidesMicroscopy cells and tissues (also, hormone receptors)Prognosis, staging and statistical windows into the future

    http://www.thebreastclinic.com/BREAST/IMAGES/mmgs/20_800_348.gifhttp://www.ecs.soton.ac.uk/~dpd/projects/miakt/gfx/miakt-mri-ss-08-03.jpg

  • MIAKT Services

    Image Analysis ServicesOxfords XRay Mammogram AnalyserKCL MRI Mammogram Analyser/Classifier

    Classification ServicesAbnormality Nave Bayes Classifier (Soton)MRI Lesion Classifier (KCL)

    Patient Data Retrieval Services (OU)For example, Find Patients With Same Age

    Image Registration (KCL)GRID service invoked via web-service

    Natural Language Report Generation (Sheffield)Generate a patient report from RDF description

    UMLS Lookup (Sheffield)Lookup term definitions in the UMLS

    Patient Records also accessed through web-service (Soton)Web-service enabled AKT 3store

  • Patient Cases in RDF

    01.01.1923

    invasive carcinoma

    03.05.1992

  • The MIAKT Framework

  • Demonstration

  • Drivers

    Moores Law and Powers of 10The WWW: Blessing and CurseMaking the Web SemanticIntelligence on the WebResearch on the Semantic WebFutures Challenges

  • 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

  • Grid

    E-Scientists

    Entire E-Science CycleEncompassing experimentation, analysis, publication, research, learning

    5

    Institutional Archive

    LocalWebPublisher

    Holdings

    Digital Library

    E-Scientists Graduate Students

    Undergraduate Students

    Virtual Learning Environment

    E-Experimentation

    E-Scientists

    Technical Reports

    Reprints

    Peer-Reviewed Journal &

    Conference Papers

    Preprints & Metadata

    Certified Experimental

    Results & Analyses

    Data, Metadata & Ontologies

  • Combichem

    Combechem

    The need for xtl-Prints

    DATA PUBLICATION

    DISSEMINATION

  • Structural Eprints

  • Increasing Use of Value Added Services

  • 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

  • Hubs and Authorities

    Begin with existing measures: document count and citation count.Apply Kleinberg (1998) hubs/authorities analysis to data.Note that higher citation count may not mean higher authority rating: quality citations are what count.

  • Turning Points and Centrality

    Allows us to find turning points in scientific development: Kuhns paradigm shiftmoment.Centrality measure to be applied to same Citeseer data.

  • Fig. 2. Bursting onto the scene: New Topics

    New topics in research literatures

  • Detecting Key Moments

    Cause and effect chains in content sets

  • Future Challenges

    Developing, Deploying, Managing and Reusing OntologiesCo reference resolution and referential integrity on the SWInference on the webThe annotation bottleneckAnnotating Multimedia ContentComposing Knowledge ServicesTrust

    Representing provenanceMechanisms for trust

  • More Information

    www.aktors.org

    School of Electronicsand Computer ScienceWhy distributed and digital is goodThe work of many peopleDriversFaster and SmallerAlters our PerspectivesDriversThe Magic of the Network Effect Exponential DevelopmentDriversMaking the Web SemanticVia meta contentCan Annotate AnythingThe SW Community: Structured SpacesOntologies: Building Blocks for the SWOntologies offer.Genetics: Gene OntologyManufacturing: AerospaceOntologies: ObservationsStandards are fundamentalDriversAdvanced Knowledge Technologies IRCInfrastructures and ComponentsExemplar Technology: ClassAKTComponent Technologies: ModellingComponent Technologies: ModellingIntegrating Semantic SpacesThe CS AKTive Space:International Semantic Web Challenge WinnerCS AKTiveSpaceExtending the modelMediate and Aggregate: UK Research CouncilsEPSRC: Knowing what they knowMediate and Aggregate: OntologiesVisualising InteractionVisualising Interaction: ProgrammesIntegrating Semantic SpacesCollaborative Medical Decision Making MIAKT:Integrating Semantic SpacesWhy This Domain?Multi-disciplinary Assessment: The MedicsMIAKT ServicesPatient Cases in RDFThe MIAKT FrameworkDemonstrationDriversNew ways of discovery: e-Science Structural EprintsIncreasing Use of Value Added ServicesCommunities of AuthorsHubs and AuthoritiesTurning Points and CentralityBursting onto the scene: New TopicsDetecting Key MomentsFuture ChallengesMore Information

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