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

© 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 ☺


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


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


The big pictureand

the tool box


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


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


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


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


Standardization is crucial!

Industry and Business needsMust be one driving / converging

force

We are here

OWL, Rules, OWL-S, WSDL-S, …


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


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 !


Industry permeates the Research


Applications

Research

Technologies, Innovations

Deployed useful applications

&


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

Slide 16© KnowledgeWeb Alain Léger – IASW2005, Jyväskylä, Finland, 26 August 2005

Kweb Portal6

Industry Support

Alain LégerRobert Meersman

Chairs of Industry Area Knowledgeweb


Industry Board membersIncluding:- Client Industry- Technology push companies- Ontology content partners


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

0

1

2

3

4

5

6

7

8

9

10

Companies

Country

UKAustAllEsFrItGrBeBulDkIrlUSA


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


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


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


Use case collection process

We send a short questionnaire to eachindustry board member asking for input


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


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

http://images.google.de/imgres?imgurl=http://sig4.ago.fr/img/SEMTATION.jpg&imgrefurl=http://sig4.ago.fr/partners.html&h=76&w=155&sz=4&tbnid=B7J7iYLTSmwJ:&tbnh=44&tbnw=90&hl=de&start=8&prev=/images%3Fq%3Dsemtation%26hl%3Dde%26lr%3D


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.


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)


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


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



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



InformationIntegration +

MatchingInformationConsumers

SemanticMatchingEngine

Crawler

RDFRepository

Semantic Portal

InformationProviders

RDF-annotated Websites

RDFRepository

Non-RDFHR System

Wrapper

NetAPI

NetAPI



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


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?


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


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.

http://www.newsexpress.de/

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



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)




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


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


Selected Data resources

Knowledge mining and fusion

Automated Knowledge extraction

Ontology merging and mapping


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


Semantic best Match

Requestunderstanding

New knowledgegeneration


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…


RDFRepository

Non-RDFBD System

Semantic Mediations

Non-RDFBD System

Non-RDFBD System

Inte

rnet

Automated wrapper generation


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


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


Semantic best Match

Non-RDFBD System

Non-RDFBD System

Automated Knowledge summary

Best availableInformation / knowledge

Selected Doc / Data

Extraction

Knowledge extraction and summary



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



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


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










…

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


Use Case 8: Hospital Information System

Identified Technology needs

Mediator

Ontology

Mappin

g/Ann

otatin

g

Database 1 Database n













…

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-….


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…


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


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


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

http://bibster.semanticweb.org/

http://cs.aktivespace.org/

http://www.cs.vu.nl/~pmika

http://museosuomi.cs.helsinki.fi/

http://sciencedesk.arc.nasa.gov/

http://esw.w3.org/mt/esw/archives/cat_applications_and_demos.html

http://www.w3.org/


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, …


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


Who have real Business today ?

Early Adopters !!

Key Technology announced 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…


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

http://ubp.l3s.uni-hannover.de/ubp

http://knowledgeweb.semanticweb.org/o2i/


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

http://semantic-web-days.net/

http://iswc2005.semanticweb.org/

http://www.eswc2005.org/


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

http://www.w3.org/2001/sw/

http://knowledgeweb.semanticweb.org/

http://www.w3.org/

http://ubp.l3s.uni-hannover.de/ubp

http://knowledgeweb.semanticweb.org/o2i/

Sem Web Applications Challenge the Research · – E-Commerce not deployed ... ¾From the POV of an...

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