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Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics & Istituto di Linguistica Computazionale - CNR

Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

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Page 1: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Computational Lexicons and the Semantic WebTraining Session

Alessandro Lenci

Università di Pisa – Department of Linguistics

&

Istituto di Linguistica Computazionale - CNR

Page 2: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Case Studies

1. Formalize in RDF the EuroWordNet Synsets and Top Ontology

2. Writing semantic frames in RDF/S as basis for interlingua representations

Page 3: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

EuroWordNetTop Ontology

Top Concepts (TC) classify synsets the subset of 1024 Basic Concepts synsets

TC are hierarchically ordered by means of a subsumption relation multiple inheritance between TC is not allowed

BC can be cross-classified in terms of multiple TC

“It is important to realize that the TC are more like semantic features than common conceptual classes”

Vossen (2001)

Page 4: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

EuroWordNet

Page 5: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

EuroWordNetTop Ontology

skinhairbody-covering

Top

1stOrderEntity 2ndOrderEntity

SituationType SituationComponent

Living

Location ExperiencePhysicalStatic DynamicNaturalCovering Part Group

Composition OriginFunction Form

Etc….Etc.

bodypartcellmuscleorgan

Object

Human

Mental

Directiondistancespatial propertyspatial relationcoursepath

change of positiondividelocomotionmotion

feeldesiredisturbanceemotionfeelinghumorpleasance

churchcompanyinstituteorganizationpartyunion

humanadultadult femaleadult malechildnativeoffspring

Page 6: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Task 1

1. Formalize in RDF/S the EWN TC

2. Creating RDF/S entries for the following synsets and link them to the relevant TC

{fruit} #0001TC: Comestible Function; ObjectForm; PartComposition; PlantLivingNaturalOrigin

{cell} #0002TC: PartComposition; LivingNaturalOrigin

{car} #0003TC: ArtifactOrigin; ObjectForm; VehicleFunction

Page 7: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Lexical ObjectsTop Level

mlm:Entry

mlm:SynUmlm:SemU

mlm:Synset

mlm:LexObjectrdfs:subClassOf

rdfs:subClassOf

rdfs:subClassOf

mlm:hasSynUmlm:correspondsToSynset

mlm:hasSemU

rdfs:literal

mlm:language

Page 8: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

MLC in RDF/S features

mlm:LexObject mlm:Valuesmlm:feature

mlm:SemValues

mlm:SynValues

rdfs:subClassOfmlm:semFeature

rdfs:subClassOf

mlm:synFeature

rdfs:subPropertyOf

features are properties of lexical objects

Page 9: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Synsets in RDF/S

mlm:Synset rdfs:literalmlm:word

mlm:Synset

mlm:synsetRelation

mlm:Values

rdfs:literalmlm:gloss

mlm:feature

cf. also http://www.semanticweb.org/library/wordnet/wordnet-20000620.rdfs

Page 10: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

EWN Top Ontology in RDF/S

TC as semantic features

mlm:SemValuesmlm:Synset

EwnTopOntology

rdfs:subClassOf

mlm:semFeature

topOntology

rdfs:subPropertyOf

http://…/EWNFunction

http://…/EWNVehicle

rdf:type

rdf:typeisa

Page 11: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Task 2

1. Formalize in RDF/S the following type of semantic frame

2. share semantic frames among entries in different languages

feature

semRole

n

feature

semRole

predicate

arg_

...

1arg_

Page 12: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Task 2semantic frames

PREDemploy#1

Arg#1<AGENT - HUMAN>

Arg#2<PATIENT - HUMAN>

SemU

employer

SemU

employee

SemU

employment

SemU

to employ

agentnominalization

patientnominalization

eventnominalization

master link

Page 13: Bucharest, 30 July 2003 Computational Lexicons and the Semantic Web Training Session Alessandro Lenci Università di Pisa – Department of Linguistics &

Bucharest, 30 July 2003

Semantic Frames in RDF/S

mlm:SemanticFramemlm:SemU

mlm:SemArgument

mlm:hasFrame

mlm:argument

rdfs:literal

mlm:semRole

mlm:SemValues

mlm:semType rdfs:literal

mlm:number