Adding Semantic to Web Data and Servicesbeaune/websem/cours2008_2009/AlainLeger/… · D1 - 16/01/2009 France Télécom R&D ... Adding Semantic to Web Data and Services Part 9 –

D1 - 16/01/2009France Télécom R&D Diffusion of this document is subject to the autorisation of France Télécom R&DD1 - 16/01/2009

Cours Web Services sémantiques © Alain Léger FT R&D, ENS M SE St Etienne, 5-14 January 2009

Adding Semantic to Web Data and ServicesPart 9 – Technologies and Applications

Doctoral School, St Etienne January 2009

Alain Léger FT R&D Orange Labs ResearchDR Knowledge Processing (KRR)Manager Industry Area IST NoEs OntoWeb et Knowledgeweb (2000 -2007)Associated DR CNRS Lyon I - LIRIS



Plan Cours 1 (5 janv 09 13:30 – 17:15 / 6 janv 09 8:00 – 11:45)

• Why adding semantics to the Web ? (1h30)

CIntroduction

CTake Away and References

• Foundations of Semantic Web (2h15)

CIntroduction to Description Logics

CStandards Inferences and Tableau

• From XML, RDF to OWL (2h45)

CXML, RDF, RDF-S

COWL

• Applications and Roadmap (1h)

CApplication Scenarios

CVisions prospectives et verrous technologiques



Plan Cours 2 (12 janv 09 13:30 – 17:15 / 13 janv 09 8:15 – 12:00 / 14 janv 09 8:15 – 12:00 )

• From Rules to Queries (1h15)

CRules Languages

CSparQL

• Process Distribution (1h00)

CFrom structural to services computing

• Semantic WS discovery (3h45)

CNon standard inferences

CResources discovery

CIllustration : PICSEL

• Semantic WS composition (1h30) ** Introduction pour cours 3 **

COverview on the approaches

CAutomata for Web services composition

CIllustrations : DERI et FT-Orange Labs

• Technologies, Plateforms, Applications (2h00)

CExperimental Technology Platforms WSML-MX, OWL-S MX, WSMX, et al.

CApplications Sem Web : AceMedia, Mkbeem, Business cases (book)



Plan Cours 3 (19 janv 09 / 20 janv 09 / 21 janv 09 )

• SWS Standards TD (4h)

CSAWSDL, OWLS, WSMO

• Functional based Semantic Web Service CompositionCDefinitionsCApproaches (Our two approaches vs. State of the Art)COptimizationCIndustrial Scenarios

• WSMO service Parsing and Reasoning TP(4H)

• Techniques de base de la construction d'application par composition de services type mashup via mashup tools TP(4H)

CPiggy bank CTabulator CYahoo Pipes CMicrosoft PopflyCQEDWikiCGoogle Mashup via IGoogle

CNetvibes



Deployed "Syntaxic WS" Application Examples

• MusicBrainzCPOST music metadata to MusicBrainz community music DataBase

CGET others metadata available

CRDF for <post> <get> in FreeAmp MP3 player

• When you open an audio CD with FreeAmp it queries MusicBrainz server got track name and

artist metadata, in addition to just their position on the CD

• Open Directory ProjectCHuman-edited Categorization of Web resources in RDF

CIt powers the core Directory service for Google, Netscape Search, AOL Search, …

Ci.e. Google annotates the category listings with PageRank

• AmazonCRemote access to data and functionality

• E.BayCSOAP interface exposed WS Stock price look up service

• TerraService, MapPoint, Eastman, Berkeley, Providence Health Systems, T-

Mobile, …



Galileo travels down web services path

• Four Web services with major customersC Creating itineraries, booking travel, encoding and decodingairport and destination information, and providing updates on plane status after takeoff. COne example is the Itinerary Inquiry, a Web service - or what Galileo calls a SOAP service - that returns flight, car and hotel availability based on a given destination and date. CThe GDS is updated constantly with fare and reservation information from 500 airlines, 227 hotel operators, 32 car rental agencies, 368 tour operators and all the major cruise lines. It handles 350 million requests for information per day and 92 billion transactions per year, and boasts an uptime of 99.95%. Last year, more than 345 million travel reservations were booked through Galileo's systems from more than 178,000 terminals in 115 countries, which generated an estimated $55 billion in travel-related services.

http://www.networkworld.com/news/2002/0429galileo.html

"With the inception of Web services, we can let our customers access us the way they want

over whatever network they want. We will modularize our global distribution system functions

so a travel agency or corporate customer can arrange our business objects in such a way

that they meet their specific business needs."



Industry Board members

Including:- Client Industry- Technology push companies- Ontology content partners



Use Case Collection in Knowledgeweb

• Recruitment• News aggregation service• Product lifecycle management• Data warehousing in healthcare• B2C marketplace for tourism• Digital photo album management• R&D support for coffee• Agent-based system for insurance• Daimler Chrysler Semantic Web portal• Integrated access to biological data• …

About 50 use cases 2004 - 2006



Section Technologies

Semantic Web ServicesTools

Chapter 11 of "Semantic Web Services: Theory, Tools and Applications", J. Cardoso, IDEA-ISR 2007



OWL-S/UDDI Matchmaker (OWL-S/UDDIM)

• OWL-S services

• OWL domain ontologies

• DL subsumption-based matchmaking

• Standalone and Web-based versions

• Standalone version has a client API

• Open source (Java)

• Intelligent Software Agents Group, Carnegie Mellon University

• http://projects.semwebcentral.org/projects/owl-s-uddi-mm/



IBM Semantic Tools for Web Services (STWS)

• WSDL-S services


• Applies AI planning techniques to find composite services that

match the request

• Eclipse plug-in

• Exploits the WordNet lexicon

• http://www.alphaworks.ibm.com/tech/wssem



Hybrid OWL-S Web Service Matchmaker (OWLS-MX)

• OWL-S services


• Logic-based matching + syntactic token-based similarity metrics

• A service test collection is also available


• German Research Center for Artificial Intelligence, DFKI

Saarbruecken

• http://www.dfki.de/~klusch/owls-mx/



METEOR-S Infrastructure (MWSDI) - Lumina

• WSDL-S services


• Adds semantic to the whole service lifecycle

• METEOR-S discovery API used by the graphical tool Lumina

(Eclipse plug-in)


• Large Scale Distributed Information Systems (LSDIS) Lab,

University of Georgia

• http://lsdis.cs.uga.edu/projects/meteor-s/illumina/



TUB OWL-S Matcher (OWLSM)

• OWL-S services


• DL subsumption-based weighted matching over many service

parameters


• Technical University of Berlin

• http://kbs.cs.tu-

berlin.de/ivs/Projekte/owlsmatcher/index.html



WSMX Discovery Component

• WSMO services

• WSML domain ontologies

• Part of the WSMO reference implementation


• WSMX working group, European Semantic

Systems cluster initiative

• http://www.wsmx.org/



Section Applications

Multilingual Knowledge BasedElectronic Market Place

With the contributions of my colleagues from the MKBEEM team



• Projet de recherche IST 1999-10589) coordonné par

FTR&D avec :CVTT Information Technology (Finlande)CNational Technical University of Athens (Grèce)CCNRS-LIRMM (France)CSNCF (France)CSchlumbergerSema (Espagne)CEllos (Finlande)CFidal (France)CUniversidad Politécnica de Madrid (Espagne)Et le support de

• Université de Clermont-Ferrand (LIMOS)

• Université de Paris Sud (LRI)

• AQL (Software Quality)

Multilingual Knowledge-Based European Electronic Marketplace



Mkbeem Interogation / intégration Multilingue

MKBEEM Mediation System

Domain Ontology

Server

Human Language Processing Server

Customer

User Interface

Content/Service Provider

CP e-commerce platform

CP Interface

CP agen

t

User Agen

t

MKBEEM System Manager

Manager Interface

Trading Ontology

Server

Manager Agent

rational agent

CP Informatio

nSystem

Plan 1: source1: SNCFview1: TimetableA(paris,…)source2: Expediaview2: HotelReservation(X355,…)

view: HotelReservation(X355,…)

view: TimetableA(paris,…)

Expedia CP Interface

SNCF CP Interface

Vers Slides externes

du Projet Mkbeem



Section Applications

Multimedia applications

http://www.acemedia.org/aceMediaWith the contributions of my colleagues:

Stamatia Dasiopoulou (CERTH)Yiannis Kompatsiaris (CERTH, Center for Research and Technology Hellas,

Greece)Paola Hobson (Motorola Labs, UK)



Context and Reasoning for Analysis

• Context analysis refines the degrees of initial labels by

exploiting contextual information derived from scene

classification and ontological text analysis

• Reasoning works on top of the contextually refined labelsCSpatial consistency checking CIntegration of the different analysis modules annotationsCDerivation of higher-level annotationsCRegion merging

WIAMIS 06Improving Image Analysis Using a Contextual ApproachPh. Mylonas, Th. Athanasiadi and Y. Avrithis,School of Electrical and Computer EngineeringNational Technical University of Athens, 157 73 Zographou, Greece



• aceMedia aims to discover and exploit knowledge inherent in multimedia content, making it more relevant for the user and automating annotation.

Intelligent Search & Presentation

Transmission

Content Analysis & Annotation

Storage

Content Creation

aceMedia vision



Multimedia processing

• Algorithms for high-level semantic reasoning for multimedia content

• User query analysis tools and intelligent search, retrieval, ranking and relevance feedback mechanismsCUser query processing

CVisual / 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 of the players that made

the score of thisfootball match and givelme their rating in the

2003 worldcup?

♦ Player A ♦ Player B

♦ Ball



The Autonomous Content Entity

Intelligence Layer

Programmable layer, enabling the ACE to be self-sufficient, self-organizing, self-analysing.

Programmable layer, enabling the ACE to be self-sufficient, self-organizing, self-analysing.

ACE

Metadata Layer Knowledge-based Automatic Semantic analysis and annotation using ontologies and Semantic Web technologies (also scalable).

Knowledge-based Automatic Semantic analysis and annotation using ontologies and Semantic Web technologies (also scalable).Content Layer

Scalable content for reuse in different devices, different situations and user needs.

Scalable content for reuse in different devices, different situations and user needs.

• To materialize aceMedia’s vision we create the concept of the Autonomous Content Entity - ACE



ACE creation – content analysis



aceMedia content annotation chain

• Aims to provide automatically generated semantic metadata

• Novel knowledge structures (ontology modeling) for multimedia resources

• New and improved knowledge-assisted multimedia content analysis tools to support concept detection and trackingCVisual Content Detection CKnowledge-assisted region-based analysisCPerson / face detection and identificationCOntological text analysis

• Novel tools to assist multimedia content analysisCMultimedia reasoningCVisual context analysis and modeling



Use scenarios - personal consumer

• Julie is back from holidays in the Canary Islands and has a dinner with her friends tonight

• She uploads her pictures to the aceMedia system• She adds personal annotations• The system creates automatic annotations• She wants to show the coolest pictures to her friends

tonight• She starts browsing the collection• …but cannot find a picture of herself on the beach

with a boat that she wants to show• Julie runs a semantic query and a picture is returned• It is not the one she is looking for so she starts a

query for similar pictures• She uses relevance feedback to fine tune the search

Julie



Example - Julie's pictures scenario

Semantic annotation Semantic search

Relevance feedback



Use scenarios - commercial consumer

• For a new advertising campaign Nathan has 3 ideas : a

tennis player winning a match, a F1 driver winning a race

and a mountaineer at the summit of a very high peak

• In the morning he creates three new searchACEs that can

look for the 3 identified ideas

• Nathan makes a first proposal for his client.

• His client discards the mountaineer and the car ideas and

focuses on a picture of a tennis player on clay ground

• Nathan refines the query of the searchACE: “Tennis

player winning a match on clay”; the searchACE is

instructed to provide more specific results

• That evening a new archive comes on line, and next

morning, more results are offered to Nathan

f h ’ h d f h h

Nathan



Example - Nathan's content search



Implications on system design

Domain experts need to select annotation

terms

Video and audio low level processing to

detect matches

Relevance feedback

Conversion to semantic search queries

Structured annotation including wide

range of semantic labels

Annotation of content with keywords

Implication (system requirement)

Experts are not always available at the

time the content is annotated

Efficiency of search

User interface for marking items as

relevant/irrelevant

Multiple languages need to be supported

All required annotation is not known at

design time

Manual annotation is time consuming

and costly

Issue (performance requirement)

Metadata precision

Search by example

Search iteration

Natural language

queries

Semantic search

Search by keyword

User requirement



On Demand Content Explosion!

Movies Music

’2002’- 400

’2004’- 1000

’2005’- 5000

’2007’- 40,000+

’2003’- 300k+

’2004’- 800k+

’2005’- 1M+

’2007’- 3M+

User needs assistance for “Content Navigation”

Lost User = Lost Revenue



Content Navigation: Traditional way



Content On Demand

User’s Social NetworkPa

st In

tere

sts

Link

ed b

y m

y Fr

iend

s

For M

y M

ood

Gen

re/A

rtis

t/Dire

ctor



Conclusions



Fast take up in industry is key !!

• Showing the value to Business UnitsCDo not oversell the technology (AI syndrom …)CConvincing benefits on Not toy scenarios !CFast ROI

• Hiding the complexity of technology to all users• Focusing the research effort on key Industry Roadblocks• Making available tools and compliant Frameworks• Sharing the knowledge and theoretical skill with industry• Standardizing on key elements

Do not realize the full picture at once !

Academia

Industry



Transfer to Industry in Action

• Be pragmatic: realize the Semantic Web step by step

• European Semantic Web research is working hard

Cto meet industrial requirements underway

• Industry provides a testbed for the evaluation of research results

Ctools, ontologies, methodologies, use cases ... underway

• Support for technology migration & industrial training

Cadapted courses to practitioners underway

• First phase of resultsCconcrete technology transfer to industry... End of 2006 All Knowledge Web resources, namely

use cases, success stories, industrial events, tutorials, recommendations,…can be found at:

http://knowledgeweb.semanticweb.org/o2i



Clear ROI!

As presented by Cerebra at Semantic technology Conference 2006, San Jose, CA, USA, march 2006

All reducing time to market and increasing profits

Increase its profitmargins on each deal by Z

Reducing time to make customer"live" from X to Y weeks

Each deployment isheavily customized by its services team to make it fit the customer's business

Leader in tailoreddocument management solutions to financialservices companies

Realize revenues 12 months earlier. Launch beforecompletion

Reducing time to market from 15 to 3 months

New product launchesimpact thousands of pricing programs and applications accros multiple channels

Fortune 100 hi-techproduct company

Re-focus 20 peopleback to corebusiness

Reducing time to align data for financial reports from 8 to 1 week

Product portfolios change, marketdefinitions change, and they need to stayaligned

Fortune 500 manufacturingcompany

… in order to ……but is now …… suffers because …A …

Fuse

Interpret

Automate Increase its profitmargins on each deal by Z

Reducing time to make customer"live" from X to Y weeks

Each deployment isheavily customized by its services team to make it fit the customer's business

Leader in tailoreddocument management solutions to financialservices companies

Realize revenues 12 months earlier. Launch beforecompletion

Reducing time to market from 15 to 3 months

New product launchesimpact thousands of pricing programs and applications accros multiple channels

Fortune 100 hi-techproduct company

Re-focus 20 peopleback to corebusiness

Reducing time to align data for financial reports from 8 to 1 week

Product portfolios change, marketdefinitions change, and they need to stayaligned

Fortune 500 manufacturingcompany

… in order to ……but is now …… suffers because …A …

Fuse

Interpret

Automate



Who have real Business today ?

Early Adopters !!

p-39 - 16/01/2009

Remerciements

• Une pensée toute particulière à tous ceux à qui j'ai emprunté, et ils

sont nombreux !

• Et à ceux qui m'ont emprunté … ☺

Merci !

p-40 - 16/01/2009

Annexe

Pour poursuivre

p-41 - 16/01/2009

Recommandations

• Lire Semantic Web For Business : Cases and

Applications, Roberto Garcia editor, 2008

Information Science Reference, ISBN 978-1-60566-066-0

• Participer aux conférences Semantic

Technology Conference (STC, ESTC)

Documents

Adding Semantic to Web Data and Servicesbeaune/websem/cours2008_2009/AlainLeger/… · D1 - 16/01/2009 France Télécom R&D ... Adding Semantic to Web Data and Services Part 9 –