Towards a new generation ofsemantic web applications

Prof. Enrico Motta, PhDKnowledge Media Institute

The Open UniversityMilton Keynes, UK

The Semantic Web

A large scale, heterogenous collection of formal, machine processable, web accessible, ontology-based statements (semantic metadata) about web resources and other entities in the world, expressed in a XML-based syntax

The Semantic Web (pragmatic def.)

The collection of all statements expressed in one of the following formalisms:

{OWL, RDF, DAML, DAML+OIL, RDF-A…},

which can be accessed on the web

<akt:Person rdf:about="akt:EnricoMotta"> <rdfs:label>Enrico Motta</rdfs:label> <akt:hasAffiliation rdf:resource="akt:TheOpenUniversity"/> <akt:hasJobTitle>kmi director</akt:hasJobTitle> <akt:worksInOrgUnit rdf:resource="akt:KnowledgeMediaInstitute"/> <akt:hasGivenName>enrico</akt:hasGivenName> <akt:hasFamilyName>motta</akt:hasFamilyName> <akt:worksInProject rdf:resource="akt:Neon"/> <akt:worksInProject rdf:resource="akt:X-Media"/> <akt:hasPrettyName>Enrico Motta</akt:hasPrettyName> <akt:hasPostalAddress rdf:resource="akt:KmiPostalAddress"/> <akt:hasEmailAddress>e.motta@open.ac.uk</akt:hasEmailAddress> <akt:hasHomePage rdf:resource="http://kmi.open.ac.uk/people/motta/"/></akt:Person>

Person Organization

String Organization-Unit

partOf

hasAffiliation

worksInOrgUnithasJobTitle

Ontology

Metadata

Ontology

Metadata

UoD

<RDF triple><RDF triple><RDF triple><RDF triple><RDF triple><RDF triple>

<RDF triple><RDF triple><RDF triple><RDF triple><RDF triple><RDF triple>

<RDF triple><RDF triple><RDF triple><RDF triple><RDF triple><RDF triple>

<RDF triple><RDF triple><RDF triple><RDF triple><RDF triple><RDF triple>

Proposition #1

The SW today has already reached a level of scale good enough to make it a very useful source of knowledge to support intelligent applications

This is unprecedented in the history of AI

So, let's have a look at the semantic web as it is

today….

Charting the web

Charting the web (2)

<?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns="http://www.geonames.org/ontology#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:wgs84_pos="http://www.w3.org/2003/01/geo/wgs84_pos#"> <Feature rdf:about="http://sws.geonames.org/2642465/"> <name>Milton Keynes</name> <featureClass rdf:resource="http://www.geonames.org/ontology#P"/> <featureCode rdf:resource="http://www.geonames.org/ontology#P.PPLA"/> <inCountry rdf:resource="http://www.geonames.org/countries/#GB"/> <population>31967</population> <wgs84_pos:lat>52.0333333</wgs84_pos:lat> <wgs84_pos:long>-0.7</wgs84_pos:long> <parentFeature rdf:resource="http://sws.geonames.org/2635167/"/> <nearbyFeatures rdf:resource="http://sws.geonames.org/2642465/nearby.rdf"/> <locationMap>http://www.geonames.org/maps/geonameId=2642465</locationMap> </Feature> </rdf:RDF>

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"> <rdf:Description rdf:about="" xmlns:photoshop="http://ns.adobe.com/photoshop/1.0/"> <photoshop:City>Beijing</photoshop:City> <photoshop:Country>China</photoshop:Country> </rdf:Description> <rdf:Description rdf:about="" xmlns:Iptc4xmpCore="http://iptc.org/std/Iptc4xmpCore/1.0/xmlns/"> <Iptc4xmpCore:Location>great wall of china</Iptc4xmpCore:Location> </rdf:Description> <rdf:Description rdf:about="" xmlns:mediapro="http://ns.iview-multimedia.com/mediapro/1.0/"> <mediapro:People> <rdf:Bag> <rdf:li>Jim Hendler</rdf:li> <rdf:li>Enrico</rdf:li> <rdf:li>Freddie_the_camel</rdf:li> </rdf:Bag> </mediapro:People> </rdf:Description> </rdf:RDF>

Proposition #2

The SW may well provide a solution to one of the classic AI challenges: how to construct and manage large volumes of knowledge to construct truly intelligent problem solvers and address the brittleness of traditional KBS

Knowledge Representation Hypothesis

Any mechanically embodied intelligent process will be comprised of structural ingredients that we as external observers naturally take to represent a propositional account of the knowledge that the overall process exhibits, and independent of such external semantic attribution, play a formal but causal and essential role in engendering the behaviour that manifests that knowledge

Brian Smith, 1982

Intelligence as a function of possessing domain knowledge

Large Bodyof Knowledge

Intelligent Behaviour

KA Bottleneck

The Knowledge Acquisition Bottleneck

Large Bodyof Knowledge

Intelligent Behaviour

KA Bottleneck

Knowledge

SW as Enabler of Intelligent Behaviour

Intelligent Behaviour

KBS vs SW Systems

Classic KBS SW Systems

Representation 'Clean' 'Dirty'

Size Small/Medium Extra Huge

Repr. Schema Homogeneous Heterogeneous

Quality High Very Variable

Degree of trust High Very Variable

Key Paradigm Shift

Classic KBS SW Systems

Intelligence

A function of sophisticated, task-centric problem solving

A side-effect of size and heterogeneity(Collective Intelligence)

Overall Goal

Our research programme is to contribute to the development of this large-scale web of data and develop a new generation of web applications able to exploit it to provide intelligent functionalities

So, how can we exploit this emerging, large scale semantic

resource?

Some examples….

1

0.9

0.9 0.91

–Label similarity methods •e.g., Full_Professor = FullProfessor

Ontology Matching

0.5

0.5

–Structure similarity methods•Using taxonomic/property related information

New paradigm: use of background knowledge

A B

Background Knowledge(external source)

A’ B’R

R

External Source = One Ontology

Aleksovski et al. EKAW’06• Map (anchor) terms into concepts from a richly axiomatized domain ontology • Derive a mapping based on the relation of the anchor terms

Assumes that a suitable (rich, large) domain ontology (DO) is available.

External Source = Web

van Hage et al. ISWC’05• rely on Google and an online dictionary in the food domain to extract semantic relations between candidate terms using IR techniques

A Brel

+ OnlineDictionary

IR Methods

Precision increases significantly if domain specific sources are used:50% - Web; 75% - domain texts.

Does not rely on a rich Domain Ont,

Proposal: • rely on online ontologies (Semantic Web) to derive mappings• ontologies are dynamically discovered and combined

A Brel

Semantic Web

Does not rely on any pre-selected knowledge sources.

M. Sabou, M. d’Aquin, E. Motta, “Using the Semantic Web as Background Knowledge inOntology Mapping", Ontology Mapping Workshop, ISWC’06. Best Paper Award

External Source = SW

How to combine online ontologies to derive

mappings?

Strategy 1 - Definition

Find ontologies that contain equivalent classes for A and B and use their relationship in the ontologies to derive the mapping.

A Brel

Sem

anti

c W

eb

A1’B1’

A2’B2’

An’Bn’

O1

O2 On

BABA

BABA

BABA

BABA

⊥⇒⊥⊇=>⊇⊆=>⊆≡⇒≡

''

''

''

''For each ontology use these rules:

…

These rules can be extended to take into account indirect relations between A’ and B’, e.g., between parents of A’ and B’:

'''' BABCCA ⊥⇒⊥∧⊆

Strategy 1- Examples

Beef Food

Sem

anti

c W

eb

Beef

RedMeat

Tap

Food

MeatOrPoultry

⊆

⊆

⊆

⊆

SR-16 FAO_Agrovoc

ka2.rdf

Researcher AcademicStaff

Sem

anti

c W

eb

Researcher

AcademicStaff

⊆

⊆

ISWC SWRC

Strategy 2 - Definition

BABCCAr

BABCCAr

BABCCAr

BABCCAr

BABCCAr

⊇⇒≡∧⊇⊇⇒⊇∧⊇⊥⇒⊥∧⊆≡⇒≡∧⊆⊆⇒⊆∧⊆

')5(')4(')3(')2(')1(

Principle: If no ontologies are found that contain the two terms then combine information from multiple ontologies to find a mapping.

A Brel

Sem

anti

c W

eb

A’BC

C’B’rel

rel

Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’:

(a) if find relation between C and B.(b) if find relation between C and B.

CA ⊆'CA ⊇'

Details: (1) Select all ontologies containing A’ equiv. with A (2) For each ontology containing A’:

(a) if find relation between C and B.(b) if find relation between C and B.

Strategy 2 - Examples

PoultryChicken⊆FoodPoultry ⊆

Chicken Vs. Food(midlevel-onto)

(Tap)

Ex1:

FoodChicken⊆

Ham Vs. FoodEx2:

(r1)

MeatHam⊆FoodMeat ⊆

(pizza-to-go)

(SUMO) FoodHam⊆

(Same results for Duck, Goose, Turkey)

(r1)

Ham Vs. SeafoodEx3:

MeatHam⊆SeafoodMeat ⊥

(pizza-to-go)

(wine.owl) SeafoodHam ⊥(r3)

Evaluation: 1600 mappings, two teamsAverage precision: 70% (comparable to best in class)

(derived from 180 different ontologies)

Matching AGROVOC (16k terms) and NALT(41k terms)

Large Scale Evaluation

M. Sabou, M. d’Aquin, W.R. van Hage, E. Motta, “Improving Ontology Matching by Dynamically Exploring Online Knowledge“.

Chart 2

Ontologies (180) used to derive mappings.TAP

CPE

Mid-level-ontology.daml

SUMO.daml

Economy.daml

WMD.daml

a.com/ontology

http://139.91.183.30:9090/RDF/VRP/Examples

http://www.newOnto.org/1054569311671

http://sweet.jpl.nasa.gov/ontology/human_activities.owl

http://lonely.org/russia

http://calo.sri.com/core-plus-office

http://reliant.teknowledge.com/DAML/Geography.daml

http://edge.cs.drexel.edu/assemblies/ontologies/woolly/2004/06/flows.owl

http://mensa.sl.iupui.edu/ontology/BiologicalProcess.owl

http://travel.org/russia

http://www.mygrid.org.uk/ontology

http://sweet.jpl.nasa.gov/ontology/phenomena.owl

http://islab.hanyang.ac.kr/damls/Country.daml

http://sweet.jpl.nasa.gov/ontology/biosphere.owl

http://cohse.semanticweb.org/ontologies/people

http://cvs.sourceforge.net/viewcvs.py/meme/data

http://www.owl-ontologies.com/unnamed.owl

http://cvs.sourceforge.net/viewcvs.py/instancestore/instancestore/ontologies/Attic/wines.daml?rev=1.2

http://mirrors.webthing.com/view=Medium/www.w3.org/2000/10/swap/test

http://sweet.jpl.nasa.gov/ontology/process.owl

http://www.daml.org/experiment/ontology/beta/infrastructure-elements-ont

http://mensa.sl.iupui.edu/ontology/BiologicalOntology.owl

http://www.mondeca.com/owl/moses/ita.owl

http://139.91.183.30:9090/RDF/VRP/Examples/mgedontology.rdfs

http://example.com/ontology/food

http://wow.sfsu.edu/ontology/rich/EcologicalConcepts.owl

http://www.daml.org/experiment/ontology/infrastructure-elements-ont

http://www.larflast.bas.bg/ontology

http://www.lri.jur.uva.nl/~rinke/aargh

http://www.pizza-to-go.org/ontology

http://onto.cs.yale.edu:8080/umls/UMLSinDAML/NET/SRDEF

http://www.csd.abdn.ac.uk/research/AgentCities/ontologies/beer

http://www.aktors.org/ontology/portal

http://www.daml.org/experiment/ontology/information-elements-ont

http://www.w3.org/2002/03owlt/I5.21/premises002

http://hoersaal.kbs.uni-hannover.de/rdf/java_ontology.rdf

http://mensa.sl.iupui.edu/ontology/OMIM_input_details.owl

http://purl.org/atom/ns

http://www.csd.abdn.ac.uk/~yzhang/test.rdfs

http://www.wines.org/winevocabulary.xml

urn:pizza-demo

http://cvs.sourceforge.net/viewcvs.py/biopax/biopax/xml/owl/Attic/biopax-level1.owl?rev=1.2

http://homepages.inf.ed.ac.uk/stephenp/OWL/BACKUPS/ForecastOntology.owl

http://loki.cae.drexel.edu/~wbs/ontology/2004/04/model

http://mirrors.webthing.com/view=Asis/www.w3.org/TR/2003/WD-owl-guide-20030210/food.owl

http://newOnto.org/f8ab84c7e1

http://sweet.jpl.nasa.gov/ontology/material_thing.owl

http://sweet.jpl.nasa.gov/ontology/property.owl

http://www.daml.org/experiment/ontology/beta/economic-elements-ont

http://139.91.183.30:9090/RDF/VRP/Examples/metanet.rdf

http://139.91.183.30:9090/RDF/VRP/Examples/Phenomenon

http://annotation.semanticweb.org/iswc/iswc.daml

http://frot.org/space/0.1

http://ontology.ihmc.us/CoSAR-TS/Medical.owl

http://www.coe.neu.edu/~rfeng/MIM3153/ont/pj_o.daml

http://www.cwi.nl/~media/ns/IWA/VideoGen.rdfs

http://www710.univ-lyon1.fr/~s-suwa02/MSch/sc

http://139.91.183.30:9090/RDF/VRP/Examples/imdb.rdf.old

http://139.91.183.30:9090/RDF/VRP/Examples/LOM.rdf

http://dickinson-i-4/daml/tests/test-cases.daml

http://harth.org/2002/course

http://mensa.sl.iupui.edu/ontology/GeneInfo.owl

http://sweet.jpl.nasa.gov/ontology/earthrealm.owl

http://www.iwi-iuk.org/material/RDF/1.1/Schema/Class/mn

http://www.mindswap.org/2003/owl/pet

http://www.ontoprise.de/bizon/product

http://139.91.183.30:9090/RDF/VRP/Examples/CIDOC.rdf

http://ebiquity.umbc.edu/v2.1/ontology/person.owl

http://mensa.sl.iupui.edu/ontology/EnzymeClassification.owl

http://protege.stanford.edu/plugins/owl/owl-library/tambis-full.owl

http://reliant.teknowledge.com/DAML/SUMO.owl

http://www.biopax.org/release/biopax-level1.owl

http://www.cs.vu.nl/~karamkhe/wbkr/ontology.owl

http://www.ida.liu.se/~daele/Ontubi.owl

http://www.mindswap.org/2003/owl/swint/terrorism

http://www.mondeca.com/owl/document1_1.xml

http://www.ontotext.com/kim/2004/04kimo

http://www.public.asu.edu/~pnivargi/ppm/ontologies/med1/MedicalOnt.rdfs

http://www.w3.org/2001/vcard-rdf/3.0

rdfs:Resource

http://139.91.183.30:9090/RDF/VRP/Examples/earthrealm.rdf

http://139.91.183.30:9090/RDF/VRP/Examples/LOM_Educational.rdfs

http://139.91.183.30:9090/RDF/VRP/Examples/Mathematics_International.rdf

http://139.91.183.30:9090/RDF/VRP/Examples/Property.rdf

http://139.91.183.30:9090/RDF/VRP/Examples/substance.rdf

http://a.com/TimeAndSpace

http://daml.umbc.edu/ontologies/cobra/0.4/academia

http://media.iti.gr/iswc2004

http://mensa.sl.iupui.edu/ontology/ProteinMutationInfo.owl

http://ontologies.isx.com/onts/isx_basic_onts/isxlocusont.daml

http://potato.cs.man.ac.uk/ontologies/booze

http://protege.stanford.edu/swbp/books2.owl

http://tap.stanford.edu/data

http://www.co-ode.org/ontologies/pizza/pizza_20041007.owl

http://www.daml.org/2002/04/geonames/featureTypes.daml

http://www.w3.org/TR/2003/PR-owl-guide-20031209/food

http://coli.lili.uni-bielefeld.de/~felix/lehre/ws04_05/ontologischeRessourcen/beispiele/Government.owl http://edge.cs.drexel.edu/assemblies/ontologies/woolly/2003/10/core.owl

http://edge.cs.drexel.edu/assemblies/ontologies/woolly/2003/10/functions.owl

http://etri.re.kr/2003/10/HyundaiCar.owl

http://iswc2004.semanticweb.org/ns

http://meh/tourism1

http://monet.nag.co.uk/algorithm

http://nbi.inf.fu-berlin.de/research/swpatho/owldata/immunhistology.owl

http://nbi.inf.fu-berlin.de/research/swpatho/owldata/swpatho_space.owl

http://orl01.drc.com/daml/Ontology/Genealogy/2.X/Gentology-ont-2.0v.daml

http://protege.stanford.edu/plugins/owl/owl-library/ka.owl

http://protege.stanford.edu/plugins/owl/owl-library/koala.owl

http://reliant.teknowledge.com/DAML/Communications.daml

http://reliant.teknowledge.com/DAML/Economy.owl

http://reliant.teknowledge.com/DAML/Government.daml

http://reliant.teknowledge.com/DAML/Transportation.daml

http://site.uottawa.ca/~mkhedr/FuzzyOnto

http://sweet.jpl.nasa.gov/ontology/time.owl

http://www.atl.lmco.com/projects/ontology/ontologies/network/networkA.owl

http://www.chimera39.essex.ac.uk/delta.owl

http://www.cs.helsinki.fi/arkistin/tktl

http://www.cs.man.ac.uk/~robertsa/daml-oil-workshop/upper-ontology.daml

http://www.daml.org/experiment/ontology/economic-elements-ont

http://www.ksl.stanford.edu/projects/Aquaint/AquaSumo.daml

http://www.simondfraser.co.uk/geo_ont.daml

rdfs:Place

dctype:Text

file:/home/sofia/Ptyxiakh/files/ELS_Final/LOM.rdfs

http://139.91.183.30:9090/RDF/VRP/Examples/cld.rdf

http://139.91.183.30:9090/RDF/VRP/Examples/imdb.rdf

http://139.91.183.30:9090/RDF/VRP/Examples/ka2.rdf

http://139.91.183.30:9090/RDF/VRP/Examples/SWPG.rdfs

http://a.com/EcologicalConcepts

http://a.com/TaxonomicID

http://co4.inrialpes.fr/align/Contest/223/onto.rdf

http://cobra.umbc.edu/ont/2004/05/ebiquity-geo

http://daml.umbc.edu/ontologies/cobra/0.4/space-basic

http://daml.umbc.edu/ontologies/webofbelief/Wob.owl

http://dmag.upf.es/ontologies/2001/12/ipronto.daml

http://ebiquity.umbc.edu/v2.1/ontology/research.owl

http://edge.cs.drexel.edu/assemblies/ontologies/woolly/2004/06/functions.owl

http://lsdis.cs.uga.edu/proj/semdis/testbed/

http://mensa.sl.iupui.edu/ontology/BiologicalProcess_beta.owl

http://moguntia.ucd.ie/owl/Datatypes.owl

http://newOnto.org/f8ab9091da

http://newOnto.org/f8b166084d

http://opencyc.sourceforge.net/daml/cyc.daml

http://reliant.teknowledge.com/DAML/Geography.owl

http://reliant.teknowledge.com/DAML/Mid-level-ontology.owl

http://semweb.mcdonaldbradley.com/OWL/TaxonomyMapping/COI/NII/Taxonomy.owl

http://studioddtonline.web.infoseek.co.jp/ns/aniota

http://sweet.jpl.nasa.gov/ontology/data.owl

http://sweet.jpl.nasa.gov/ontology/numerics.owl

http://sweet.jpl.nasa.gov/ontology/space.owl

http://wow.sfsu.edu/ontology/rich/EcologicalNetworks.owl

http://www.agent-net.com/wsmm/data/artifact.owl

http://www.aktors.org/ontology/support

http://www.atl.external.lmco.com/projects/ontology/ontologies/basketball_soccer/basketball.daml

http://www.cbil.upenn.edu/Ontology/MGEDontology.rdfs

http://www.combechem.org/ontology/process

http://www.csd.abdn.ac.uk/~cmckenzi/playpen/rdf/locations.owl

http://www.daml.org/services/owl-s/1.0/Concepts.owl

http://www.dayf.de/2004/owl/beer.owl

http://www.infres.enst.fr/~bdtest/astres/astres.rdfs

http://www.iwi-iuk.org/material/RDF/1.1/Schema/Class/scf

http://www.kanzaki.com/ns/music

http://www.mindswap.org/2004/SSSW04/akt-support-ontology-latest.owl

http://www.public.asu.edu/~pnivargi/ppm/ontologies/Roles.owl

http://www.srdc.metu.edu.tr/~yildiray/HW3.OWL

http://www.w3.org/1999/02/22-rdf-syntax-ns

http://www.w3.org/2002/07/owl

http://www.w3.org/TR/2003/PR-owl-guide-20031209/wine

http://www.wam.umd.edu/~katyn/CMSC828y/hw1/hw1.daml

http://www.wam.umd.edu/~katyn/SemanticWebThesauri/perry2.owl

http://xmlns.com/foaf/0.1

owl:Thing

rdfs:Bag

rdfs:Container

Proposition #3

Using the SW to provide dynamically background knowledge to tackle the Agrovoc/NALT mapping problem provides the first ever test case in which the SW, viewed as a large scale heterogeneus resource, has been successfully used to address a real-world problem

Next Generation Semantic Web Applications

NG SW Application

• Able to exploit the SW at large – Dynamically retrieving the relevant

semantic resources – Combining several, heterogeneous

Ontologies

• Typically use a single ontology – Usually providing a homogeneous view over heterogeneous data sources.

– Limited use of existing SW data

• Typically closed to semantic resources

Contrast with 1st generation SW Applications

1st generation SW applications are far more similar to traditional KBS (closed semantic systems) than to 'real' SW applications (open semantic systems)

1895 2007

It is still early days..

Current Gateway to the Semantic Web

Limitations of Swoogle

• Very limited quality control mechanisms– Many ontologies are duplicated– No quality information provided

• Limited Query/Search mechanisms– Only keyword search; no distinction between types of elements

– need for more powerful query methods (e.g., ability to pose formal queries; ability to distinguish between classes and instances, etc…)

• Limited range of ontology ranking mechanisms– Swoogle only uses a 'popularity-based' one

• No support for ontology modularization

A New Gateway to the Semantic Web

Ontology Structuring Relations

extends

inconsistent-with

Ontology Structuring Relations

extends

Inconsistent-with

inconsistent-with

Formal Queries and relation discovery…

Current state of Watson

• Initial version implemented• Demo version available online

– See http://watson.kmi.open.ac.uk/

• However still rather unstable…..– Stable version to be available within 4-6 weeks

• Initial crawl of the SW has already produced interesting results….

Some initial figures…

• Lots of ontologies are in OWL FULL (3x the number of OWL Lite)

• … but most of the ontologies use only a very restricted sub-part of the expressivity of OWL and DAML, e.g.,– only 147 go beyond ALC– role transitivity is used in only 11 ontologies……..

• Almost 20% of semantic resources appear to be duplicates

Next Generation Semantic Web Applications

PowerMagpie

PowerAqua

Folksonomies

Tags are great to organize data!!!

But they don’t help much when searching…

Finding tagged images

FlowerRose

Lilac

LilacFlower

TulipFlowersCutFlower

Tulip

FlowerRose

Lilac

LilacFlower

TulipFlowersCutFlower

Tulip

Finding tagged images –

FLOWER

What if …

Rose Tulip

Flower

Lilac

…folksonomies were semantically richer

FlowerRose

Lilac

LilacFlower

TulipFlowersCutFlower

Tulip

Finding tagged images –FLOWER (II)

Rose Tulip

Flower

Lilac

Learning Relations Between Tags

Tags

{camera, digital, photograph} {damage, flooding, hurricane, katrina, Louisiana} Clusters

digital

cameraphotographtakenWith

Ontology

NLP/Clustering

Find and combine Online ontologies

L.Specia, E. Motta, "Integrating Folksonomies with the Semantic Web", ESWC 2007.

In More Detail…

Examples

Examples

Examples

Key Research Tasks

• Overall Infrastructure – crawling, storing, structuring, querying the SW

• Ontology Selection– In the context of dynamically identifying the sources of knowledge relevant to the needs of a system

• Ontology Mapping– When integrating information from different ontologies

– When mapping query/specs to ontologies

• Ontology Modularization– Find the sub-modules relevant to a system's query.

• Semantic Markup Generation– From various types of sources

New task context

• Key point is that NG-SW applications require solutions in a new dynamic context (run-time rather than design-time)– Example: Ontology Mapping

• Much current work focuses on design-time mapping of complete ontologies

– Example: Ontology Selection• Current work focuses on user-mediated ontology selection

– Example: Ontology Modularization• Current work by and large assumes that the user is in the loop

References

• Ontology Selection– Sabou, M., Lopez, V., Motta, E. (2006). "Ontology

Selection for the Real Semantic Web: How to Cover the Queen’s Birthday Dinner?". Proceedings of EKAW 2006

• Ontology Modularization– D'Aquin, M., Sabou, M., Motta, E. (2006).

"Modularization: A key for the dynamic selection of relevant knowledge components". ISWC 2006 Workshop on Ontology Modularization

• Watson– d’Aquin, M., Sabou, M., Dzbor, M., Baldassarre,

C., Gridinoc, L., Angeletou, S. and Motta, E.: "WATSON: A Gateway for the Semantic Web". Poster Session at ESWC 2007

References (2)

• Ontology Mapping– Lopez, V., Sabou, M., Motta, E. (2006). "Mapping

the real semantic web on the fly". ISWC 2006– Sabou, M., D'Aquin, M., Motta, E. (2006). "Using

the semantic web as background knowledge for ontology mapping". ISWC 2006 Workshop on Ontology Mapping.

• Intg. of folksonomies and SW– L.Specia, E. Motta, "Integrating Folksonomies

with the Semantic Web", ESWC 2007.

'Vision' Papers

• Motta, E., Sabou, M. (2006). "Next Generation Semantic Web Applications". 1st Asian Semantic Web Conference, Beijing.

• Motta, E., Sabou, M. (2006). "Language Technologies and the Evolution of the Semantic Web". LREC 2006, Genoa, Italy.

• Motta, E. (2006). "Knowledge Publishing and Access on the Semantic Web: A Socio-Technological Analysis". IEEE Intelligent Systems, Vol.21, 3, (88-90).

Conclusions

• SW provides an unprecedented opportunity to build a new generation of intelligent systems, able to exploit large scale, heterogeneous KBs

• This new class of systems is fundamentally different in many respects both from traditional KBS and even from early SW applications

• The size of the SW is increasing steadily and the infrastructure is getting more and more robust. These developments should enable more and more new generation SW applications to emerge within 2-3 years

Current Gateway to the Semantic Web