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KYOTO (ICT-211423) Yielding Ontologies for Transition-Based Organization FP7: Intelligent Content and Semantics http://www.kyoto-project.eu/ Piek Vossen Tienjarig jubileum NL-TERM, October 2008, Amsterdam

KYOTO (ICT-211423) Yielding Ontologies for Transition-Based Organization FP7: Intelligent Content and Semantics Piek Vossen

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Page 1: KYOTO (ICT-211423) Yielding Ontologies for Transition-Based Organization FP7: Intelligent Content and Semantics  Piek Vossen

KYOTO (ICT-211423)Yielding Ontologies for Transition-Based OrganizationFP7: Intelligent Content and Semantics

http://www.kyoto-project.eu/

Piek VossenTienjarig jubileum NL-TERM,October 2008, Amsterdam

Page 2: KYOTO (ICT-211423) Yielding Ontologies for Transition-Based Organization FP7: Intelligent Content and Semantics  Piek Vossen

Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

KYOTO (ICT-211423) Overview • Title: Yielding Ontologies for Transition-Based Organization

• Funded: – 7th Framework Program-ICT of the European Union: Intelligent Content and Semantics

– Taiwan and Japan funded by national grants • Goal:

– Platform for knowledge sharing across languages and cultures– Enables knowledge transition and information search across different target groups,

transgressing linguistic, cultural and geographic boundaries.– Open text mining and deep semantic search– Wiki environment that allows people in the field to maintain their knowledge and agree

on meaning without knowledge engineering skills• URL: http://www.kyoto-project.eu/• Duration:

– March 2008 – March 2011• Effort:

– 364 person months of work.

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Consortium

1. Vrije Universiteit Amsterdam (Amsterdam, The Netherlands), 2. Consiglio Nazionale delle Ricerche (Pisa, Italy), 3. Berlin-Brandenburg Academy of Sciences and Humantities (Berlin,

Germany), 4. Euskal Herriko Unibertsitatea (San Sebastian, Spain), 5. Academia Sinica (Tapei, Taiwan), 6. National Institute of Information and Communications Technology

(Kyoto, Japan), 7. Irion Technologies (Delft, The Netherlands), 8. Synthema (Rome, Italy), 9. European Centre for Nature Conservation (Tilburg, The

Netherlands), • Subcontractors:

– World Wide Fund for Nature (Zeist, The Netherlands), – Masaryk University (Brno, Czech)

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

KYOTO (ICT-211423) Overview • Languages:

– English, Dutch, Italian, Spanish, Basque, Chinese, Japanese • Domain:

– Environmental domain, BUT usable in any domain • Global:

– Both European and non-European languages• Available:

– Free: as open source system and data (GPL)• Future perspective:

– Content standardization that supports world wide communication– Global Wordnet Grid -> database that interlinks all wordnets

in the world to a shared ontology of meaning

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

zieke, patiënt

chronisch zieke ; langdurig zieke psychisch/geestelijk zieke

HYPONYM

arts, dokter

ziekte, stoornis

genezenρ-PATIENT

behandelenρ-PATIENTSTATE

maagaandoening, nieraandoening, keelpijn

HYPONYM

ρ-CAUSE

ρ-AGENT

ρ-PROCEDURE ρ-LOCATION

fysiotherapiemedicijnenetc.

ziekenhuis, etc.

kind

co-ρ-AGENT-PATIENT

kinderarts

HYPONYM

Wordnet = network of semantic relations between words in a language

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Images

Index

Docs

URLs

Experts

Search

Dialogue

CO2 emission

water pollution

Capture

CitizensGovernorsCompanies

Domain

DomainWikyoto

Wordnets

Abstract PhysicalTop

Middlewater CO2

Substance

Universal Ontology

Process

Environmental organizations

Environmental organizations

Global Wordnet Grid

Kybots

FactMining

Tybots

ConceptMining Sudden increase

of CO2 emissionsin 2008 in Europe

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

qualifies

qualifies

Lexicon versus Ontology

Abstract Physical

H20 CO2

Element

Ontology

Process

PhysicalChange

Organism

Ecosystem services-Nature as a resource-Nature for waste absorption-State of nature-Threats to nature

rural products

sustainable products

green roof

alien invasive species

species migration

ecosystem-based drinking water production

Artifacts

green house gas

SpiderRoof

typetype

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Concepts & Facts

• Conceptual knowledge: general & generic knowledge about – ClimateChange

• physical change • affecting the climate => definition of climate• in a region• during a period of time• caused by another change• causing yet other changes

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Concepts & Facts

• Fact:– A case of ClimateChange has been observed:

• factual and significant change in the climate (temperature, humidity, wind direction, rain fall, etc.)

• in a particular region, e.g. the Alps.• Time period• Caused by CO2 emissions, North Atlantic gulf

stream• Causes decrease of biodiversity measured in

specific populations: fish, birds, insects => counts of populations

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ICT-211423

System architecture

Page 11: KYOTO (ICT-211423) Yielding Ontologies for Transition-Based Organization FP7: Intelligent Content and Semantics  Piek Vossen

Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

System components

• Wikyoto = wiki environment for a social group:– to model the terms and concepts of a domain and agree on their

meaning, within a group, across languages and cultures– to define the types of knowledge and facts of interest

• Tybots = Term extracting robots, extract term data from text corpus

• Kybots = Knowledge yielding robots, extract facts from a text corpus

• Linguistic processors:– tokenizers, segmentizers, taggers, grammars – named entity recognition– word sense disambiguation– generate a layered text annotation in Kyoto Annotation Format

(KAF)

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Capture ServerCapture Server

Document BaseLinear KAF

Document BaseLinear KAF

Tybot server(Term Extraction)

Tybot server(Term Extraction)

Extracted TermsGeneric K-TMF

Extracted TermsGeneric K-TMF

Term Editor(Wikyoto)

Term Editor(Wikyoto)

Domain OntologyOWL_DL

Domain OntologyOWL_DL

Domain WordnetK-LMF

Domain WordnetK-LMF

Kybot Server(Fact Extraction)

Kybot Server(Fact Extraction)

SemanticAnnotationSemantic

Annotation

Document BaseLinear Generic KAF

Document BaseLinear Generic KAF

Document BaseLinear KAF

Document BaseLinear KAF

Kybot EditorKybot Editor

KybotProfilesKybot

ProfilesConcept User

Fact User

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

What Tybots do...

• Input are text documents– “Green house gases, such as CO2”– “CO2 and other green house gases”

• Linguistic processors generate KAF annotation (sequential):– morpho-syntactic analysis– semantic roles– named entities– wordnet and ontology mappings

• Output are term hierarchies in TMF (generic):– structural parent relations: “CO2 is a green house gas is a gas”– quantified structural and semantic relations– statistical data– generalized semantic mappings

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Generic algorithm • Extraction of a structural term hierarchy

• Advantage: conceptual coherence

• Steps:– extraction of potential terms using the morpho-

syntactic structure– statistical selection of salient terms– conceptual selection of dominant terms– contextual selection of terms

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Terms from morpho-syntactic structure

• Words that are the syntactic head of an NP, e.g.: card, wing-player

• Word combinations (excluding determiners and adverbs) that include the syntactic head, e.g.: yellow card, yellow card for wing-player.

• The head of a compound: player as the head of wing-player, name as the head of username.

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Statistical extraction of terms

• Frequency of terms by distribution over reference corpus:– Salience = normFreq * normRef

• Where normFreq = normalized frequency of terms on the website and normRef = normalized count of website occurrence in the reference corpus:– normFreq = nTermFrequencynWords / nPages– normRef = 1-((nWebsitesnWords) /

(referenceCorpusSize))

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Statistical extraction of termsExcluded for Salience Selected for Salience

Preferred termSitefreq.

Nr. ofpages Salience Preferred term

Sitefreq.

Nr. ofpages

Salience

ressource humaine 1 1 0.014 Oxy/Conductimètre portable 2 2 1.0

sécurité 1 1 0.024 Multiparamétre portable 2 2 1.0

sites 1 1 0.0242 convection naturelle 4 2 1.0

mobilité 1 1 0.0243 Conductimètre portable 4 2 1.0

qualité produits 1 1 0.0265 Universelle convection naturelle 2 2 0.9989

produits 1 1 0.0277 Pipette graduée 4 2 0.9989

satisfaction client 1 1 0.029 Pipette 14 2 0.9989

contact 1 1 0.0294 Photomètre 4 2 0.9989

place 1 1 0.0304 Perce-bouchon 4 2 0.9989

formation professionnelle 1 1 0.0304 Nettoyants autolaveurs 2 2 0.9989

ligne 1 1 0.0308 Mini-UniPrep 12 2 0.9989

gestion 1 1 0.0315 Microscope 6 2 0.9989

conception 1 1 0.032 Micro-pipettes capillaire 2 2 0.9989

groupes 1 1 0.0323 Micropipettes 4 2 0.9989

démarche qualité 1 1 0.0323 Loupes binoculaire 2 2 0.9989

environnement 1 1 0.0324 l'enseignement primaire 3 3 0.9989

moyens 1 1 0.0331 Incubateurs réfrigérés 2 2 0.9989

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Structure to relation table for termsTerm phrase Structure Role Type

populations of terrestrial species of Part Species

populations of vertebrate species: of Part Species

populations of 1313 vertebrate species fish, amphibians, reptiles, birds, mammals from all around the world

of Part Species

the restoration of wild species populations andtheir habitats

of Patient Restore

The increase in the footprint is driven by modest rates of growth in both population and demand for biocapacity

in Patient Increase

at half the rate of population increase of Speed Increase

the relative proportion of current biocapacity or world population in each region

in Location Region

the growth of the world population and consumption of Patient Increase

trends in their populations in Patient Trend?

The rapid rate of population decline in tropical species of Speed Decline

all countries with populations greater than 1 million with Possess Country

Increase in population in Patient Increase

species populations Modifier Part Species

MARINE SPECIES POPULATIONS Modifier Part Species

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SourceDocuments

LinguisticProcessors

[[the emission]NP [of greenhouse gases]PP [in agricultural areas]PP] NP

Morpho-syntactic analysis

TYBOT ConceptMiners

Abstract Physical

H20 CO2

Substance

CO2Emission

WaterPollution

Ontology

Process

Chemical Reaction

GlobalWarming

GreenhouseGas

Ontologize

Axiomatize

(instance s1 Substance) (instance e1 Warming) (katalyist s1 e1)

Synthesize

in

of

Term hierarchy

emission gas

greenhouse gas

area

agricultural area

CO2

naturalprocess:1

English Wordnet

emission:2gas:1

area:1

greenhouse gas:1

rural area:1

geographical area:1

region:3

location:3 substance:1

emission:3

farmland:2

CO2

Conceptual modeling

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ICT-211423

Wikyoto

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Do populations always consist of marine species?

A.....

decline...

population.....Z

Are terrestrial species never

marine species?

Simplified Term Fragment

population

marinespecies

terrestrialspecies

Simplified Ontology Fragment

?Population

Group

KyotoServer

Hidden

Shown

.... populations declined

.....terrestrial andmarine species..

in forests.....declined

Do populations consist of

marine species?

InterviewAre terrestrial

species a type of

populations?

Interview

.... populations such as

terrestrial and marine species .....

Smart Kytext

KAF DE-TNTybotspdf

FactAFKAF

Kybots

plugin plugin

DE-KONDE-WN

Facts in RDF

G-WN

Wordnets in LMFOntologies in OWL-DL

G-KON

WIKIPEDIA

SUMO DOLCE

GEO

FRAMENET

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ICT-211423

Editing the domain wordnet

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

A.....

decline...

population.....Z

group

terrestrial species population

species population

population of vertebrate

species

marine species population

peoplepopulation

1. Validate Term Hierarchy:-Defining phrases:

- document- domain corpus- Google

-Other phrases-Wiki classes-Generic-WN classes

.... populations such as

terrestrial and marine species .....

Are terrestrial species a type of

populations?

Are terrestrial species never

marine species?

WN & ⌐ DOC

WN & DOC

⌐ WN & ⌐ DOC

⌐ WN & DOC

DE-WN

G-WN: Synset: ENG20-07682918-n {population:2}

a group of organisms of the same species populating a given area

SUMO: +inhabits -> +Group

Wiki: http://en.wikipedia.org/wiki/Population

In sociology and biology a population is the collection of inter-breeding organisms of a particular species.

Smart KyText

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

land

grasslandcropland woodland

country:1, state:6, land:5

domain:2, demesne:2,

land:4

land:1 land:2, ground:7,

soil:3

object:1, physical object:1

real property:1, real estate:1, realty:1

land:3, dry land:1, earth:3,ground:1, solid ground:1,

terra firma:1

administrative district:1, administrative division:1, territorial division:1

region:3

biome:1

urban land

mediterranean woodland

Wordnet & ⌐ Doc

Wordnet & Doc

⌐ Wordnet & Doc

agricultural urban land

⌐ Wordnet, ⌐ Doc

Difficult wordnet mapping

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ICT-211423

Editing the domain ontology

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Ontologization of terms

• A domain term is a disjoint hyponym in the domain wordnet and is propagated to the domain ontology as a new Type.

• A domain term is not a disjoint hyponym and therefore we do not propose a new ontology extension but we still need to map the term to the ontology, i.e. make the ontological constraint explicit.

Page 27: KYOTO (ICT-211423) Yielding Ontologies for Transition-Based Organization FP7: Intelligent Content and Semantics  Piek Vossen

Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

A.....

decline...

population.....Z

group

population

terrestrial species population

species population

population of vertebrate

species

marine species population

people

+

?Population

DE-WN DE-ON

Group=

1. Validate Implied Ontological Constraints:- Generalize semantic relations- Interpret relation given ontology parent- Formulate interview using highlighted text

Can populations decline?Do populations consist of marine species?

Do populations always consist of marine species?Do populations always decline?

Are populations located in forests?

Are populations always located in forests?

.... populations of marine species

......... populations

declined .....terrestrial andmarine species..

in forests.....declined

Smart KyText

2. Validate additional constraints- Select dominant relations- Formulate interviews using highligted text

Sumo axiom for Group (Hidden Data)(=>    (and        (instance ?GROUP Group)        (member ?MEMB ?GROUP))    (instance ?MEMB Agent))

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Derived hidden structures• New constraint Population in DE-ON:

(subclass Population Group)(=>

    (and        (instance ?POP Population)        (member ?MEMB ? POP)

(instance ?MEMB Species)))• Extended constraint Population in DE-ON:

(subclass Population Group)(=>

    (and        (instance ?POP Population)        (member ?MEMB ? POP)

(instance ?MEMB Species) (*instance ?REGION Region) * indicates possible relations (*inhabits ?MEMB ?REGION) * indicates possible relations (*location ?MEMB ?REGION))) * indicates possible relations

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Cross-lingual validation

• Population is added by Group-1, with constraints derived from language L1

• Group-2 uses languages L2 and observes a domain Type in the domain ontology with an English gloss, description -> possibly proposed through WSD

• Select/confirm existing domain type as a candidate for validation

• Smart Ky-Text in Language L2 and the Term hierarchy are used to generate questions in L2

• Group-2 can confirm or deny constraints for L2 and add new constraints

• Cross-lingual and cross-group validation is added to the constraints in the ontology

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Cross validated structures

• Population in DE-ON:(subclass Population Group)(=> (and

    (instance ?POP Population)    (member ?MEMB ? POP)

(instance ?MEMB Species (xval G1-ENG G2-NLD G3-NLD G4-ITA))

(instance ?REGION Region(xval G1-ENG G2-NLD)) (*inhabits ?MEMB ?REGION (xval G3-NLD))

(*location ?MEMB ?REGION (xval G1-ENG G4-ITA)))))

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Capture ServerCapture Server

Document BaseLinear KAF

Document BaseLinear KAF

Tybot server(Term Extraction)

Tybot server(Term Extraction)

Extracted TermsGeneric K-TMF

Extracted TermsGeneric K-TMF

Term Editor(Wikyoto)

Term Editor(Wikyoto)

Domain OntologyOWL_DL

Domain OntologyOWL_DL

Domain WordnetK-LMF

Domain WordnetK-LMF

Kybot Server(Fact Extraction)

Kybot Server(Fact Extraction)

SemanticAnnotationSemantic

Annotation

Document BaseLinear Generic KAF

Document BaseLinear Generic KAF

Document BaseLinear KAF

Document BaseLinear KAF

Kybot EditorKybot Editor

KybotProfilesKybot

ProfilesConcept User

Fact User

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

What Kybots do

• Input:– KAF annotations of text: sequential & encoded by

language– Conceptual frame from the ontology– Expression rules for frame to language mapping:

• Wordnet in a language• Morpho-syntactic mappings rules

• Output are a database of facts in KAF/FactAF (generic):– aggregated facts– inferred facts– language neutral

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Fact mining• KYBOT = Knowledge Yielding Robot• Logical expression

– (instance, e1, Burn) (instance, e2, Warming) (cause, e1, e2) – (instance, s1, CO2) (instance, e1, GlobalWarming) (katalyist, s1,e1)

• Expression rules per language: – [N[s1]V[e1]]S – [N[e1]N[s1]N – [[N[e1]][prep][N[s2]]NP

• Ontology * Wordnets– Capabilities– Conditions: WNT -> adjectives, WNT -> nouns– Causes: WNT -> verbs, WNT -> nouns– Process: DamageProcess, ProduceProcess

• Kybot compiler– kybots = logical pattern+ ontology + WN[Lx] + ER[Lx]

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Fact mining by Kybots

SourceDocuments

LinguisticProcessors

[[the emission]NP [of greenhouse gases]PP [in agricultural areas]PP] NP

Morpho-syntactic analysis (KAF)

Abstract Physical

H2O CO2

Substance

CO2 emission

water pollution

Ontology Wordnets &Linguistic Expressions

Process

Chemical Reaction

Generic

Logical Expressions

[[the emission]NP ] Process: e1 [of greenhouse gases]PP Patient: s2 [in agricultural areas]PP] Location: a3

Fact analysisPatient

PatientDomain

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emission:2gas:1

greenhouse gas:1

substance:1

emission:3

natural process:1

C02

Lexical database: wordnet

Abstract Physical

H20 CO2

Substance

CO2Emission

Process

ChemicalReaction

GlobalWarming

GreenhouseGas

Ontology

Maximalabstraction&

integrity

Languageneutralintegrity

gasgreen house gas -> gas-increase(AG)-in 2003 (TIME)CO2 -> green house gas-emission (PA)-in European countries (LO)

Term database

Generictext based

Sudden increase of green house gases in 2003........ C02 emission

in European countries....Green house gases such as C02, ....

Text corpus

Lineartext

ConceptMining

by Tybots

Synthesize Text miningby Kybots

Ontologize

Axiomatize

(instance s1 Substance) (instance e1 Warming) (katalyist s1 e1)

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Tienjarig jubileum NL-Term, 25 October 2008, AmsterdamICT-211423

Thank you for your attention