Sem Web Applications Challenge the Research · – E-Commerce not deployed ... ¾From the POV of an...
Transcript of Sem Web Applications Challenge the Research · – E-Commerce not deployed ... ¾From the POV of an...
Sem Web ApplicationsChallenge the Research
IASW 2005, Jyväskylä, 25-27 August 2005
Alain Léger FT R&D ResearchCo-chair of Industry Area NoE KnowledgewebWith some of my colleagues:Lyndon Nixon, Free Univ BerlinPavel Shvaiko, Univ Trento
Slide 2© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Plan
Semantic Web : The Big pictureHype or Reality?
Accelerate the take-upRealize Business Use Cases!Make them profitable!
And next …Relax ☺
Slide 3© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Knowledge and Technology: What do great thinkers feel?
For a successful technology, reality must take precedenceover public relations, for nature cannot be fooled
Richard P Feynman, Nobel Prize Physicist 1965
Imagination is more important than knowledgeAlbert Einstein, Nobel Prize Physicist 1921
Slide 4© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Semantic Web Technology: What do great thinkers feel?
The fact that the programmer and the interpreter of the computer output use tehsymbols to stand for objects in the world is totally beyond the scope of thecomputere. The computer, to repeat, has a syntax but no semantics.
John Searle, Professor of Cognitive Science, Univ of California
Developing XML as a richer version of HTML was generally a good idea. But what botched the Semantic Web is that promoti,ng a unerversal syntax doesnothing to promote semantics. To avoid further confusion, it would be a goodidea to rename it the syntactic web. »
John Sowa
25 years ago, Ed Feigenbaum described Terry Winograd’s work on AI as a « breakthrough in enthusiasm ». I worry that web services and the SemanticWeb, in their reliance on the effective computational semantics are vulnerable to the same criticism.
Henry S Thomson, Univ Glasgow
Slide 5© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
The big pictureand
the tool box
Slide 6© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Semantic Web Tim berners Lee
It is not a separate Web, but an extension in which information is given well-defined meaning, better enabling computers and people to work incooperation – Realising the complete “vision” is too hard for now (probably)– But we can make a start by adding semantic annotation to web resources
Scientific American, May 2001:
From Machine Readable to
Machine understandable
Slide 7© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
The Services on the Web: Yesterday, today, tomorrow
1995– HTML, Netscape at Nasdaq, Web browsers – 8 million hosts – E-Commerce not deployed
2000 : “.com crash“2005
– Billion cybernauts, Trillions content-based sites, Thousands e-service sites, 40 million DN– US e-Tourism 20%+, e-Commerce 6%+, e-publicité 4%+, … Internet growth 20-40% each Year– Keyword-based search for web sites, some offering programmatic interfaces– US Top capitalizations: Google (30th), eBay (50th), Yahoo (65th), Amazon (145th)
– Broadband: 60%+ of Network capacity for music, video, games– Academia: Web-based registration, distance learning, homework submission, and research– Business: Web-based portals for online purchasing, electronic payment and DB interfaces – Consumers: rely on: e-commerce, e-mail, music downloads (Aple 500 millions), recreational surfing
2015– Proliferation of “converged” services; always-on connectivity– Broadband to the cell phone generalized– 100Ks or Ms of machine-machine e-commerce/e-service sites, – 80% of the planet on the WWW– The “semantic web”: Automated tasks based on – semantic - resource content
Slide 8© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Automated Mediation services Where the latestfilm from CleantEastwood visible
In the towntonight?
Critics and videoclips?
MediationsEnable an open and dynamic offerings of services
Reduce integration costs and time
Vorei il sommario in italiano di questa guida turistica. Grazie
Hi Dear Customer,Your problem …
and will beresolved in thenext 2 hours by
our technical team
Slide 9© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Ontology“Ontology is an explicit conceptualisation, formal and shared ” [Gruber 95] [Borst 97]
Aristotle ten categories•Substance. E.g., individual man.
•Quantity. E.g., two cubits.•Quality. E.g., white.
•Relation. E.g., double.•Location. E.g., in the market.
•Time. E.g., today.•Position. E.g., sitting.
•Possession. E.g., wearing shoes.•Doing. E.g., cutting.
•Undergoing. E.g., being cut.
Ontology first attempts remote to the Greecs (Parménide544-450, school of Eléates “L’être seul est immobile et éternel et il implique l’existence du non-être. First dogma on knowledge : logos”. Aristotle was the ontologist of common-sense reality
Slide 10© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Standardization is crucial!
Industry and Business needsMust be one driving / converging
force
We are here
OWL, Rules, OWL-S, WSDL-S, …
Slide 11© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Mediation – prototypical architecture
HumanInterfaces
Inferencingalgorithms
Ontologies (OWL + Rules– W3C)
Web ServicesDirectory
(UDDI)
XML/RDF-S/OWL OWL-S, …..
DB 2
Plateforme de médiation
Connecters/Wrappers
Web
Appli
2
Appli
1
Inferencing algorithms:- OntoQuery/OntoClass- QueryRepair- OntoMedia- FL0 matcher, ALN matcher- OntoFusion- OntoWrappers- OntoSemMappings, etc …
Languages :- Non ambiguous syntax- Formal semantic- Support for inferencing- Expressivity vs complexity
Domain Modeling :- Conceptual- Formal- Shared
ModelTheory
MechanicalTheoremproving
Source
de
Données 2
Source
de
Données 2
Source
de
Données 2
* Semantic data integration is one key application
Slide 12© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Fast take up in industry is key !!
Showing the value to Business Units– Do not oversell the technology (AI syndrom …)– Convincing benefits on Not toy scenarios !– Fast ROI
Hiding the complexity of technology to all usersFocusing the research effort on key Industry RoadblocksMaking available tools and compliant FrameworksSharing the knowledge and theoretical skill with industryStandardizing on key elements
Do not realize the full picture at once !
Slide 13© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Industry permeates the Research
Slide 14© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Applications
Research
Technologies, Innovations
Deployed useful applications
&
Slide 15© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Main GoalPromote greater awareness and faster take-up
of SWS technology by industry – in close and permanent synergy with research area
How? create a virtuous feedback loop between industry and research, including education and training
Academia
Industry“bridge the gap” between industry and research(bi-directional process!)
NoE Knowledge Web Industry Area process
SpecificIndustry Needs
Use Cases &Knowledge components
Technology SuitabilityEvaluation
Interoperable Framework& Ontology content
Ontology ContentRecommendation
certifying, and serving validated ontologies
1
Kweb
Indu
stry
Allian
ce2 3
OOA Alliance
EducationResearch
Promotion, Deployment– Technology RoadMaps, Success stories, Technology show cases, Best Practices
4
Cross-Network cooperation – joint program of activities & Joint Education, competence centers5
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Kweb Portal6
Industry Support
Alain LégerRobert Meersman
Chairs of Industry Area Knowledgeweb
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Industry Board membersIncluding:- Client Industry- Technology push companies- Ontology content partners
Slide 19© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Industry members
Human Ressources– SAP.de– EDS.nl– NBC.nl– Kenteq.nl– Ordina.nl– BT.co.uk– City of Antwerp.be– Randstad.nl– Klett Verlag.de– Editis.fr– IMC.de– IDS-Scheer.de– VDAB.be– Le FOREM.be
Technology– TXT e-Solutions– Acklin– Distributed Thinking– Onto Text Lab– Isoco– Bitext– Risaris– IKV++– Semtation– HR-XML consortium– Amper– Grupo Miramon– Labein– Robotiker Infotech– Berlecon– Computas Technology– Synergetics– Net Dynamics– Green Cacti– Expert System Language
Publishing, Content– Merrall Ross International Ltd– Office Line Engineering– NIWA
Manufacturing– WTCM
Banking– Tecnologia, Informacion and Finanzas– Cidem
Telecom– France Telecom– British Telecom– Telefonica– Neofonie
Energy– IFP– EDF (not finalised)
Transport– SNCF– TrenItalia– EADS AirBus (not finalized)
Health care and Biomedecine– Biovista– L&C Languge and Computing– AstraZeneca.se– Synthematix.usa– Roche.sz– Novartis.sz– Entelos Inc.usa
Automotive– Daimler Chrysler
Foods– Illy Caffè
Space Defense– Thalès– DSTL– Ministero de Defensa– Deimos Space Focusing FIRST on
very promising areas
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Companies
Country
UKAustAllEsFrItGrBeBulDkIrlUSA
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Industry Partners facts
50 Industry partners on Board (Including OOA)50 prospects 15 nationalities14 economic sectors– Health, Telecom, Automotive, Energy, Food, Media, Transport, HR,
Space, Publishing, Banking, Manufacturing, Laws, and TechnologyFocused on areas– With rapid promising results
• Health Care and Life Sciences• Telecommunications• Human Resources management• Culture Museums
Give a « membership » statusVery demanding task
Core team partners very motivated
Slide 21© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Contract Alliance
Mutual obligations of results
Knowledge Web Early Access Policy– A panel of industrial corporations working as a “laboratorial” target market– where all can
• study industrial requirements, • test the industrial value of their ideas,• collect inspiration for new research opportunities.
Early Knowledge transfer– "Early Releases“
• Results and deliverables from KW technical, are to be made available to Panellists at the respective owners' discretion;
– Early Releases have to be treated as confidential by the Panellist, – The Panelists agree
• To test the Early Releases diligently and to communicate to the Contractors sufficient progress and error reports,
• To provide inputs such as but not limited to, recommendations, or industrial requirements, feedback to the Contractors in relation to its own use of the Semantic Web services & technology,
Industry managers responsibility– ensuring timely achievement of the Panellists' tasks,– ensuring feedback and information for the benefit of all Contractors
Slide 22© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Ontology Outreach AuthorityFrom the POV of an enterprise, Semantic Web is an intrusive and a
disruptive technology. Need also to pay attention to roles of legacy systems,
methodology, and resulting research and education challenges
Ontologies are very difficult to standardize. Ontologies usually are
application-dependent; subjective; general evaluation criteria lacking
We suggest “standardization lite” by recommending ontologies:
Evaluate an ontology whether it is in concordance Evaluate an ontology whether it is in concordance with the claims of its developers.with the claims of its developers.
Goals, requirements, scope, reusability, usability, etc.
First sectors addressed:
• Human Resources and Employment • Healthcare and Life Sciences
Semantic Web Use Cases
KnowledgeWeb : Industry Area – Research Area
Alain Léger, Lyndon Nixon, Malgorzata Mochol, Roberta Cuel, François Paulus, Mustafa Jarrar. Heraklion, 3 June 2005
Slide 24© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use case collection process
We send a short questionnaire to eachindustry board member asking for input
Slide 25© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use case collection process
We follow up with a face-to-face meeting to collect furtherdetails and write up the use case in consultation with
industry
Slide 26© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Cases: Results so far
1. Recruitment2. Multimedia content analysis and annotation3. Peer-to-peer eScience portal4. News aggregration service5. Product lifecycle management6. Data warehousing in healthcare7. B2C marketplace for tourism8. Digital photo album management9. Geosciences project memory10. R&D support for coffee11. Co-ordination of Real Estate Management12. Hospital Information System13. Agent-based system for insurance14. Daimler Chrysler Semantic Web Portal15. Specialized Web Portals for Businesses16. Integrated access to Biological data
Slide 27© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Knowledge processing tasks
Extract research challenges from use caseDirect research to industrial requirements
To package and purchasea nice week-end
To plan a nice week-end
Customer Content and Service Providers (C/S Ps)
ManageContent-Aggregation
MappingContent ControlContent Transform-XmlStream
Access ServicerequestSchemas
Manage-Content
loadXmlStream
transformContent
returnResult
Task: Data Translation
Component: Wrapper
Requirements: Static mappings are not scalable. Correspondences between
content need to be determined dynamically.
Task: Results Reconciliation
Component: Reconciler
Requirements: Integrated data should be checked for duplications or overlap in the
collected information.
Slide 28© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Analysis of use cases
Use Cases by Industry 6% 6%
6%
13%
18%13%
19%
13%6%
Automobile Energy
Food Industry Government & Public Sector
Media & Communications Pharmaceuticals & Health
Service Industry Technology Providers
Transport & Logistics
0
2
4
6
8
Solutions sought by industry
Matching Annotation Search
Navigation Integration of data Standardization of vocabulary
Data management Consistency checking Personalisation
Recruitment use case:
Solution – „matching between job offers and job seekers“
Why – „Employee recruitment is increasingly being carriedout online… Finding the best suited candidate in the fastest
time leads to cost cutting and resource sparing“
Data warehousing in heathcareuse case:
Solution – „introduce a commonterminology for healthcare dataand wrap all legacy data in this
terminology“
Why – „…allows for a dataintegration and consistency
checking solution… „
0
2
4
6
Technology locks
Sem antic query Ontology m atching Storage and retrievalKnowledge extraction Ontology-based reasoning Sem antic Web Services
Ontology m apping Sem i-autom ated annotation Ontology authoring tools
Ontology developm ent Support for rules Trus t
Ontology m aintenance
R&D Support for Coffee use case:
Summary – Semantic search engine for ontology basedqueries in a Knowledge Management system
Technology locks – Corporate domain ontologybuilding and maintenance
Integration of Biological Data Repositoriesuse case:
Summary – An unified point of access to different biological data repositories
accessible through the Internet
Technology locks – Generation and extraction of knowledge from biological
data…
Using standards.. providing a unified entrypoint to different biological data
repositories (ontology-based reasoning)
Slide 29© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Task typologyKnowledge processing tasks Components
Data Translation Wrapper
Ontology Management Ontology Manager
Matching Match Manager
Matching Results Analysis Match Manager
Content Annotation Annotation Manager
Reasoning Reasoner
Semantic Query Processing Query Processor
Composition of Web Services Planner
Results Reconciliation Results Reconciler
Schema/Ontology Merging Ontology Manager
Producing Explanations Match Manager
Personalization Profiler
Directory Management Directory Manager
Most tasks repeat over cases, suggesting a stable typology containing core tasks
stipulated by industry needs.
Drilling down into Use Cases
Slide 31© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
OverviewOverviewD1.1.4: Use Case Summaries– Use Case 1: Recruitment, Worldwidejobs– Use Case 2: B2C portal, FT– Use Case 3: News Aggregration, Neofonie– Use Case 4: Product Lifecycle Management, Semtation– Use Case 5: Managing Knowledge at Trenitalia– Use Case 6: Access to Biological data, Robotiker– Use Case 7: Needs in Petroleum industry, IFP– Use Case 8: Hospital information systems, L&C global– Use Case 9: Multimedia Processing
Conclusions
Selection of key use cases
Slide 32© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Slide 33© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 1: Recruitment
ApplicantEmployer
Job Posting annotated using controlled vocabularies
Semantic Matching of applicant’s profile with job postings
Recommended open positions
Job Application annotated using controlled vocabularies
Semantic Matching of applications with position’s requirements
Interview Recommendations
Automated Preselection
Job Portal
Slide 34© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 1: Recruitment
InformationIntegration +
MatchingInformationConsumers
SemanticMatchingEngine
Crawler
RDFRepository
Semantic Portal
InformationProviders
RDF-annotated Websites
RDFRepository
Non-RDFHR System
Wrapper
NetAPI
NetAPI
Slide 35© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 1: Recruitment
Semantic Matching
Match Manager
Improved matching: weighting, measures…
Storage& retrieval
Directory Manager
Scalability, retrieval reliability&performance
KnowledgeExtraction
Wrapper; annotater
Mapping non-RDF, semi-automation
Ontology managing
Ontology manager
Guidelines, change tracking (versioning)
Trust Match Manager
Reputation as ranking criteria
Slide 36© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 2: B2C portal
Customer Offers
Query plan generationDiscovery and composition
Request understanding
Semantic best Match of customer request and profile with current offers
Selection of matching combinations
Automated Knowledge fusion
Selected services and Data resources annotated using Ontologies
Knowledge fusion and summary of top best match
Travel Package thrustedRecommendations
Plane booking
Train booking
Car rentals
Books and Guides
Next two weeks I’m goingto Heraklion. Could you
propose me a personalizedleasure package
and a concise guide in spanish?
Slide 37© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 2: B2C portal
Semantic Matching
Match Manager
Adapted matchers,for DB and services
Local schemas mappings
Ontology Manager
Semi-automated ontology Merging and mappings
Knowledge Extraction
Automated Wrapper
Mapping non-RDF, semi-automation, ease of integration of new services / resources
Ontology managing
Ontology manager
Guidelines, change tracking (versioning), tool and methodology
Trust Match Manager
Reputation as ranking criteria
Slide 38© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 3: News Aggregration
Provision of an aggregated news service that is able to provide business clients with accurate search, thematic clustering, classification of news stories, and e-mail notification of stories of interest.http://www.newsexpress.de
The current solution is a semi-automatic approach consisting of two phases:• the manual creation by a source expert of a XSLT template for each news source,• the automatic processing of that news source through a thematicclustering algorithm (NLP) and classification with category mappings.
Use Case 3: News Aggregration
Annotation Annotation manager Tool integration, automation
OntologyMapping
Wrapper Dynamicism, performance
OntologyDevelopment
Ontology manager Extraction from text, fuzziness
Search Query,reasoner, results reconcile
Rich, efficient query. Matching
Security, Trust Annotation, wrapper, results
Usage rights, assesstrust
Slide 39© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Slide 40© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 4: Product Lifecycle
There is a high cost associated with the development and maintenance of product catalogues throughout the product lifecycle. Expensive and complex tools are used due to non standardized terminology of the structure of product catalogue data, causes difficulties in development and maintenance of that data.
A tool supports the representation of product configurations as Visio diagrams, in which the diagram components are tied to descriptions using a common terminology.The current tool could be extended to support Semantic Web standards such as OWL in the future.
Use Case 4: Product Lifecycle
Ontology development Ontologymanager
Good tool, visualisation, change tracking
Personalisation Profiler User profiles, access rights, collaboration
Logic / rules Reasoner Extensions (rules, measures), Efficiency (minimal subsets)
Slide 41© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Slide 42© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 5: Managing Knowledge at TrenitaliaInstitutional
K
KBase
Portale
Unique access
A unique Workflow of K contribution
A central Repository of
Content Management
A common taxonomy
content management
system
Intranet Site
Tools of personal
productivityP
P
P
Local Contribution, codification and storing
Distributed KM: Research and Development Peer to peer document sharing and reviewing systems; semantic information
retrieval tools; Expert maps; Social technologies that allow communities exchanging processes; Documents upload on official repositories
Slide 43© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 5: Managing Knowledge at Trenitalia
Semantic Matching Match Manager Improved matching: weighting, measures…
Storage& retrieval Directory Manager
Scalability, retrieval reliability&performance
Queries Wrapper; annotater
Mapping non-RDF, semi-automation
Ontology managing Ontology manager
Guidelines, change tracking (versioning)
Trust Match Manager Reputation as ranking criteriaCollaborative and social creation and reviewing of documents
Collaborative writing systems, and evaluation ranking
Contribution and position within community as ranking criteria
Use Case 6: Integrated access to biological data
Biological Data Repositories
Existing Ontologies
Semi-Automated merging and mapping
Annotations and wrappers generation
Slide 44© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Selected Data resources
Knowledge mining and fusion
Automated Knowledge extraction
Ontology merging and mapping
RDF-annotated Websites
RDFRepository
Non-RDFBD System
(Nucleotide Sequences, amino acid sequences,…), corporate databases, results of experiments (DNA-chips),
health cards, medical literature sites…
a researcher wants to compare the
result of a experiment with the genome annotation
database
Non-RDFBD System
Non-RDFBD System
Inte
rnet
allow, combining and associating existing ontologies in the biological field, an integral modelling of the biological data sources
(genomics, proteomics, metabolomics and systems biology)
BiologistExpert
Query plan generation
Selection of matching combinations
Semantic best Match
Requestunderstanding
New knowledgegeneration
Slide 45© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 6: Integrated access to biological data
InformationIntegration + Matching
Knowledge fusion Biological Data
RepositoriesBiology experts
Semantic Query Matching Engine
Automated Ontology Merging and Mapping
(Nucleotide Sequences, amino acid sequences,…), corporate databases, results of experiments (DNA-chips),
health cards, medical literature sites…
RDF-annotated Websites
RDFRepository
Non-RDFBD System
Semantic Mediations
Non-RDFBD System
Non-RDFBD System
Inte
rnet
Automated wrapper generation
Slide 46© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 6 : Integrated access to biological data
Semantic Matching
Match Manager
Adapted matchersfor very heterogeneous DB
Local schemas mappings
Ontology Manager
Semi-automated ontology Merging and mappings
Knowledge Extraction
Automated Wrapper
Mapping non-RDF, semi-automation, ease of integration of new services / resources
Ontology managing
Ontology manager
Guidelines, change tracking (versioning), tool and methodology
Knowledge Generation
Knowledge miner
Knowledge mining over ontology mediated sources
Use Case 7: Geoscience semantic memory
In a KM context to give semantic access to multi projects documents and data (software, subsurface models) to practitioners Complex
MultimediaDocuments
From Energy industry partners
GeoscienceExperts
Slide 47© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
I am in charge of this new project with Total
company for energyexploration in Indian sea ,
could you find me therecent subsurface
documents of south-eastIndia?
ComplexMultimediaProject documents
90% Automated deep annotation with available taxonomy/ontology
Deeply AnnotatedProjects documents
Automated annotation
Intra
/Ext
rane
t
C HC
C
=> CCO²
CO
Query plan generation
Requestunderstanding
Selection of matching combinations
Semantic best Match
Non-RDFBD System
Non-RDFBD System
Automated Knowledge summary
Best availableInformation / knowledge
Selected Doc / Data
Extraction
Knowledge extraction and summary
Slide 48© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 7: Geoscience semantic memory
ComplexMultimedia
Documents Data Repositories
InformationIntegration + MatchingKnowledge summary
Geoscienceexperts
Semantic Mediations
Semantic Query Matching Engine
Automated Ontology Annotation
Non-RDFBD System
Non-RDFBD System
Automated summarization
Intra
/Ext
rane
t
C HC
C
=> CCO²
CO
Slide 49© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 7: Geoscience semantic memory
Semantic Matching
Match Manager
Selection of the best (part-of) documents and data
Automatedannotation
Annotator Automated on-the-fly of complex documents / DB / SW
Knowledge Summary
Summarizer Summarizing complex documents and data table
Ontology managing
Ontology manager
Large domain Ontology building and maintenance
Navigation Ontologynavigator
Ontology-driven navigation
Slide 50© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 8: Hospital Information System
Dealing with the issues of database integration in the domain of healthcare
Semantic Mediator
Medical Ontology
Mappin
g/Ann
otatin
g
Database 1 Database n
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
…
Query
Free text reports
I want to access all diagnoses and treatment
information about my patients, from all of these heterogeneous resource
easily and quickly
Slide 51© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 8: Hospital Information System
Identified Technology needs
Mediator
Ontology
Mappin
g/Ann
otatin
g
Database 1 Database n
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
Xyz XyzXyz Xyz Xyz Xyz
Xyz Xyz Xyz Xyz Xyz Xyz Xy
z Xyz Xyz Xyz XyzXyz Xyz
…
Query How to build a good ontologyFoundational challenges in ontology modeling
How to deal with (free text) medical reports: - Extraction,- Indexing, - Annotating …
How to agregate with other patient data:-Echograph-Cardiograms-….
Slide 52© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 8 : Hospital Information System
Automatedannotation
Annotator To automatically annotate medical reports and DB.
Automatedmapping
Merger To automatically merge medical facts about a patient.
Knowledge Extraction Extractor Knowledge extraction from
medical reports
Ontology Engineering Methodology
Modeling methodology that guide ontology builders to have a high quality (and reusable) ontology content, i.e. facing foundational modeling challenges…
Slide 53© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 9: Multimedia processing (1)
Knowledge structures (ontology modeling) for multimedia resources– Tools for enriching ontologies with multimedia information– Construction of domain ontologies for knowledge-assisted analysis
Knowledge-assisted multimedia content analysis tools to support concept detection and tracking
– Ontology learning and knowledge-assisted analysis– Person / face detection and recognition, mood detection – Ontological text analysis
Pre - ProcessingSegmentation
Descriptor ExtractionMultimedia Documents
Ontology Framework
ShotsPartitions
DescriptorsDescriptor Matching
ProcessingReasoning
Ontology Access & Querying
DetectedObjects/EventsACE Metadata
MergingSplittingFusion
TrackingConsistency
Checking
Indexing
DIRECTOR
SCENETAKE
TITLE
Pre - ProcessingSegmentation
Descriptor ExtractionMultimedia Documents
Ontology Framework
ShotsPartitions
DescriptorsDescriptor Matching
ProcessingReasoning
Ontology Access & Querying
DetectedObjects/EventsACE Metadata
MergingSplittingFusion
TrackingConsistency
Checking
Indexing
DIRECTOR
SCENETAKE
TITLE
KnowledgeBase
ContentAnnotation
Slide 54© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Use Case 9: Multimedia processing (2)
Algorithms for high-level semantic reasoning for multimedia contentUser query analysis tools and intelligent search, retrieval, ranking and relevance feedback mechanisms
– User query processing– Visual / conceptual hybrid search; relevance feedback
Context analysis
Contentanalysis
- object recognition (inference, reasoning)- querying- SW Services
Colours (LL features)
Sports
Football Basketball …
Player
Ball
Field
…
…
Give me the names ofthe players that made
the score of thisfootball match and
givel me their rating in the 2003 worldcup?
♦ Player A
♦ Player B
♦ Ball
Slide 55© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Other prominent applications !
Bibster – A semantics-based Bibliographic P2P– http://bibster.semanticweb.org
CS AKTive space – Semantic data integration– http://cs.aktivespace.org (Winner 2003 SemWeb challenge)
Flink: SemWeb for analysis of Social Networks– http://www.cs.vu.nl/~pmika (Winner 2004 SemWeb challenge)
Museum Finland: Sem Web for cultural portal– http://museosuomi.cs.helsinki.fi (2nd prize 2004 SemWeb challenge)
ScienceDesk collaborative knowledge management system in NASA– http://sciencedesk.arc.nasa.gov/ (3rd prize 2004 SemWeb challenge)
Also see Applications and Demos at W3C SWG BPD– http://esw.w3.org/mt/esw/archives/cat_applications_and_demos.html
Slide 56© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Industry-Research (some) Challenges Key areas for semantic solutions are search and data integrationIndustry wants to better find and use the (legacy) databases (migration!)
Key technology locks are: – development of ontologies i.e. modelling of business domains, authoring,
best practices and guidelines, re-use of existing ontologies and simple tools!– knowledge extraction i.e. the population of ontologies by finding knowledge
within legacy data– mapping i.e. overcoming heterogeneity (use of different ontologies) by
determining how one ontology can be expressed in terms of another– Scalability: approximation, modularization, distribution– Matching: exact vs. fuzzy matching– Web services: where are they really needed? – Language extensions: what aspects are missing
• E.g. data types, expressiveness of rules, context, …
Slide 57© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Transfer to industry in actionBe pragmatic
Realize the semantic web step by step
European Semantic Web research is working hard– to meet industrial requirements underway -
Industry members provide a concrete testbed for testing and evaluating research results– tools, ontologies, components and methodologies underway -
It is also important to support technology migration & industrial training – adapted courses to practitioners underway -
First phase of results– concrete technology transferance to industry planned early 2006
Slide 58© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Who have real Business today ?
Early Adopters !!
Key Technology announced August 2005!
Slide 59© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Slide 60© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Joining Knowledge Web
All Knowledge Web resources(use cases, success stories, technological recommendations,
industrial events, tutorials, learning resources…) will be found at:
http://knowledgeweb.semanticweb.org/o2i
Note that access is for Industrial Board members, of course you are welcome to join…
Slide 61© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
NoE Knowledge Web : 3 Pillars
Industry Research Education
Ontologyrecommendation
body OOA
Virtual researchcentre
Virtual educationPlatform
Horizontal integration
KnowledgeWeb is an EU FP6 Network of Excellence– Runs from 2004 to 2007 (4 years)– Budget of approx €7 million– 18 academic partners and research centers from 11 countries
Slide 62© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Join us at the next Industry events
Semantic Web Days (6-7 Oct 2005, Munich)– Business solutions for tomorrow– http://semantic-web-days.net
ISWC 2005 (6-10 Nov 2005, Galway)– 4th International Semantic Web Conference– http://iswc2005.semanticweb.org
ESWS 2005 (June 2005)– Industry forum day (slides and papers)– http://www.eswc2005.org
Slide 63© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005
Selected referencesThe Semantic Web: research and Applications
– LNCS 2342 (ISWC 2002)– LNCS 2870 (ISWC 2003)– LNCS 3053 (ESWS 2004)– LNCS 3298 (ISWC 2004)– LNCS 3532 (ESWS 2005)
Journal of Web semantics (Elsevier)Thematic portal
– http://www.semanticweb.org
Semantic Technology Conference 2005– http://www.semantic-conference.com
WWW conferences– http://www2004.org Proceedings
Yearly semantic web applications challenge– http://challenge.semanticweb.org
SIGsemis semantic web information systems journal– http://www.sigsemis.org
http://www.w3.org/2001/sw/– All the subgroups !!– http://www.w3.org/2001/sw/BestPractices/ (Sem Web Best Practices)– http://www.w3.org/2004/12/rules-ws/report/ (Rules working draft)– http://www.w3.org/2004/swls-ws.html (Sem Web in Life sciences)– http://www.w3.org/2001/sw/Europe/ (Transfering W3C standards to Practioners)
http://knowledgeweb.semanticweb.org/– Education portal– Industry portal