towards a new generation of semantic web applications prof. enrico motta, phd knowledge media...
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Towards a new generation ofsemantic web applications
Prof. Enrico Motta, PhDKnowledge Media Institute
The Open UniversityMilton Keynes, UK
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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
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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
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<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>[email protected]</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
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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>
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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
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So, let's have a look at the semantic web as it is
today….
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Charting the web
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Charting the web (2)
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<?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>
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<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>
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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
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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
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Intelligence as a function of possessing domain knowledge
Large Bodyof Knowledge
Intelligent Behaviour
KA Bottleneck
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The Knowledge Acquisition Bottleneck
Large Bodyof Knowledge
Intelligent Behaviour
KA Bottleneck
Knowledge
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SW as Enabler of Intelligent Behaviour
Intelligent Behaviour
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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
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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)
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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
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So, how can we exploit this emerging, large scale semantic
resource?
Some examples….
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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
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New paradigm: use of background knowledge
A B
Background Knowledge(external source)
A’ B’R
R
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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.
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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,
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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
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How to combine online ontologies to derive
mappings?
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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 ⊥⇒⊥∧⊆
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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
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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.
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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)
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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“.
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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
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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
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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
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• 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)
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1895 2007
It is still early days..
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Current Gateway to the Semantic Web
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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
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A New Gateway to the Semantic Web
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Ontology Structuring Relations
extends
inconsistent-with
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Ontology Structuring Relations
extends
Inconsistent-with
inconsistent-with
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Formal Queries and relation discovery…
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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….
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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
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Next Generation Semantic Web Applications
PowerMagpie
PowerAqua
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Folksonomies
Tags are great to organize data!!!
But they don’t help much when searching…
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Finding tagged images
FlowerRose
Lilac
LilacFlower
TulipFlowersCutFlower
Tulip
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FlowerRose
Lilac
LilacFlower
TulipFlowersCutFlower
Tulip
Finding tagged images –
FLOWER
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What if …
Rose Tulip
Flower
Lilac
…folksonomies were semantically richer
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FlowerRose
Lilac
LilacFlower
TulipFlowersCutFlower
Tulip
Finding tagged images –FLOWER (II)
Rose Tulip
Flower
Lilac
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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.
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In More Detail…
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Examples
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Examples
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Examples
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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
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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
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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
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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.
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'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).
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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
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Current Gateway to the Semantic Web