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The Global Wordnet Grid: anchoring languages to universal meaning Piek Vossen Irion Technologies/Free University of Amsterdam Overview Wordnet, EuroWordNet background Architecture of the Global Wordnet Grid Mapping wordnets to the Grid Advantages of shared knowledge structure

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the global wordnet grid anchoring languages to universal meaning
The Global Wordnet Grid: anchoring languages to universal meaning

Piek Vossen

Irion Technologies/Free University of Amsterdam

overview
Overview
  • Wordnet, EuroWordNet background
  • Architecture of the Global Wordnet Grid
  • Mapping wordnets to the Grid
  • Advantages of shared knowledge structure
  • 7th Frame work project KYOTO
wordnet1 5
WordNet1.5
  • Semantic network in which concepts are defined in terms of relations to other concepts.
  • Structure:
      • organized around the notion of synsets (sets of synonymous words)
      • basic semantic relations between these synsets
  • http://www.cogsci.princeton.edu/~wn/w3wn.html
  • Developed at Princeton by George Miller and his team as a model of the mental lexicon.
relational model of meaning

animal

cat

dog

kitten

puppy

Relational model of meaning

animal

kitten

man

boy

man

woman

cat

meisje

boy

girl

dog

puppy

woman

wordnet data model
Wordnet Data Model

Vocabulary of a language

Relations

Concepts

  • rec: 12345
  • financial institute

1

bank

rec: 54321

- side of a river

2

rec: 9876

- small string instrument

1

fiddle

violin

type-of

rec: 65438

- musician playing violin

2

fiddler

violist

rec:42654

- musician

type-of

rec:35576

- string of instrument

1

part-of

string

rec:29551

- underwear

2

rec:25876

- string instrument

usage of wordnet
Usage of Wordnet
  • Improve recall of textual based analysis:
    • Query -> Index
      • Synonyms: commence – begin
      • Hypernyms: taxi -> car
      • Hyponyms: car -> taxi
      • Meronyms: trunk -> elephant
      • Lexical entailments: gun -> shoot
  • Inferencing:
    • what things can burn?
  • Expression in language generation and translation:
    • alternative words and paraphrases
improve recall
Improve recall
  • Information retrieval:
    • small databases without redundancy, e.g. image captions, video text
  • Text classification:
    • small training sets
  • Question & Answer systems
    • query analysis: who, whom, where, what, when
improve recall9
Improve recall
  • Anaphora resolution:
    • The girl fell off the table. She....
    • The glass fell of the table. It...
  • Coreference resolution:
    • When he moved the furniture, the antique table got damaged.
  • Information extraction (unstructed text to structured databases):
    • generic forms or patterns "vehicle" - > text with specific cases "car"
improve recall10
Improve recall
  • Summarizers:
    • Sentence selection based on word counts -> concept counts
    • Avoid repetition in summary -> language generation
  • Limited inferencing: detect locations, organisations, etc.
many others
Many others
  • Data sparseness for machine learning: hapaxes can be replaced by semantic classes
  • Use redundancy for more robustness: spelling correction and speech recognition can built semantic expections using Wordnet and make better choices
  • Sentiment and opinion mining
  • Natural language learning
eurowordnet
EuroWordNet
  • The development of a multilingual database with wordnets for several European languages
  • Funded by the European Commission, DG XIII, Luxembourg as projects LE2-4003 and LE4-8328
  • March 1996 - September 1999
  • 2.5 Million EURO.
  • http://www.hum.uva.nl/~ewn
  • http://www.illc.uva.nl/EuroWordNet/finalresults-ewn.html
eurowordnet14
EuroWordNet
  • Languages covered:
    • EuroWordNet-1 (LE2-4003): English, Dutch, Spanish, Italian
    • EuroWordNet-2 (LE4-8328): German, French, Czech, Estonian.
  • Size of vocabulary:
    • EuroWordNet-1: 30,000 concepts - 50,000 word meanings.
    • EuroWordNet-2: 15,000 concepts- 25,000 word meaning.
  • Type of vocabulary:
    • the most frequent words of the languages
    • all concepts needed to relate more specific concepts
wordnet family

Domains

SUMO

DOLCE

Fahrzeug

Object

Transport

1

Auto

Zug

Device

voertuig

Road

Water

Air

1

2

vehicle

TransportDevice

auto

trein

German Words

1

4

car

train

2

ENGLISH

Car

Train

Vehicle

Dutch Words

liiklusvahend

2

1

English Words

auto

killavoor

3

3

vehículo

2

1

Estonian Words

véhicule

auto

tren

1

veicolo

voiture

train

1

2

Inter-Lingual-Index

auto

treno

Spanish Words

2

dopravníprostředník

French Words

2

Italian Words

1

auto

vlak

2

Czech Words

Princeton WordNet, (Fellbaum 1998):

115,000 conceps

EuroWordNet, (Vossen 1998): 8 languages

BalkaNet, (Tufis 2004): 6 languages

Global Wordnet Association: all languages

Wordnet family
eurowordnet16
EuroWordNet
  • Wordnets are unique language-specific structures:
    • different lexicalizations
    • differences in synonymy and homonymy
    • different relations between synsets
    • same organizational principles: synset structure and same set of semantic relations.
  • Language independent knowledge is assigned to the ILI and can thus be shared for all language linked to the ILI: both an ontology and domain hierarchy
autonomous language specific

object

artifact, artefact

(a man-made object)

natural object (an

object occurring

naturally)

block

instrumentality

body

box

spoon

bag

device

implement

container

tool

instrument

Autonomous & Language-Specific

Wordnet1.5

Dutch Wordnet

voorwerp

{object}

blok

{block}

lichaam

{body}

werktuig{tool}

bak

{box}

lepel

{spoon}

tas

{bag}

linguistic versus artificial ontologies
Linguistic versus Artificial Ontologies
  • Artificial ontology:
    • better control or performance, or a more compact and coherent structure.
    • introduce artificial levels for concepts which are not lexicalized in a language (e.g. instrumentality, hand tool),
    • neglect levels which are lexicalized but not relevant for the purpose of the ontology (e.g. tableware, silverware, merchandise).
  • What properties can we infer for spoons?
  • spoon -> container; artifact; hand tool; object; made of metal or plastic; for eating, pouring or cooking
linguistic versus artificial ontologies19
Linguistic versus Artificial Ontologies

Linguistic ontology:

  • Exactly reflects the relations between all the lexicalized words and expressions in a language.
  • Captures valuable information about the lexical capacity of languages: what is the available fund of words and expressions in a language.

What words can be used to name spoons?

spoon -> object, tableware, silverware, merchandise, cutlery,

wordnets versus ontologies
Wordnets versus ontologies
  • Wordnets:
    • autonomous language-specific lexicalization patterns in a relational network.
    • Usage: to predict substitution in text for information retrieval,
    • text generation, machine translation, word-sense-disambiguation.
  • Ontologies:
    • data structure with formally defined concepts.
    • Usage: making semantic inferences.
the multilingual design
The Multilingual Design
  • Inter-Lingual-Index: unstructured fund of concepts to provide an efficient mapping across the languages;
  • Index-records are mainly based on WordNet synsets and consist of synonyms, glosses and source references;
  • Various types of complex equivalence relations are distinguished;
  • Equivalence relations from synsets to index records: not on a word-to-word basis;
  • Indirect matching of synsets linked to the same index items;
equivalent near synonym
Equivalent Near Synonym
  • 1. Multiple Targets (1:many)
    • Dutch wordnet: schoonmaken (to clean) matches with 4 senses of clean in WordNet1.5:
    • make clean by removing dirt, filth, or unwanted substances from
    • remove unwanted substances from, such as feathers or pits, as of chickens or fruit
    • remove in making clean; "Clean the spots off the rug"
    • remove unwanted substances from - (as in chemistry)
  • 2. Multiple Sources (many:1)
  • Dutch wordnet: versiersel near_synonym versiering ILI-Record: decoration.
  • 3. Multiple Targets and Sources (many:many)
  • Dutch wordnet: toestel near_synonym apparaat ILI-records: machine; device; apparatus; tool
equivalent hyperonymy
Equivalent Hyperonymy

Typically used for gaps in English WordNet:

  • genuine, cultural gaps for things not known in English culture:
    • Dutch: klunen, to walk on skates over land from one frozen water to the other
  • pragmatic, in the sense that the concept is known but is not expressed by a single lexicalized form in English:
    • Dutch: kunstproduct = artifact substance <=> artifact object
from eurowordnet to global wordnet
From EuroWordNet to Global WordNet
  • Currently, wordnets exist for more than 40 languages, including:
  • Arabic, Bantu, Basque, Chinese, Bulgarian, Estonian, Hebrew, Icelandic, Japanese, Kannada, Korean, Latvian, Nepali, Persian, Romanian, Sanskrit, Tamil, Thai, Turkish, Zulu...
  • Many languages are genetically and typologically unrelated
  • http://www.globalwordnet.org
some downsides
Some downsides
  • Construction is not done uniformly
  • Coverage differs
  • Not all wordnets can communicate with one another
  • Proprietary rights restrict free access and usage
  • A lot of semantics is duplicated
  • Complex and obscure equivalence relations due to linguistic differences between English and other languages
next step global wordnet grid

Fahrzeug

1

Auto

Zug

2

vehicle

German Words

1

car

train

2

English Words

3

3

vehículo

1

auto

tren

veicolo

1

2

Spanish Words

auto

treno

2

Italian Words

Next step: Global WordNet Grid

Inter-Lingual

Ontology

voertuig

1

auto

trein

Object

2

liiklusvahend

Dutch Words

1

Device

auto

killavoor

TransportDevice

2

Estonian Words

véhicule

1

voiture

train

2

dopravní prostředník

French Words

1

auto

vlak

2

Czech Words

gwng main features
GWNG: Main Features
  • Construct separate wordnets for each Grid language
  • Contributors from each language encode the same core set of concepts plus culture/language-specific ones
  • Synsets (concepts) can be mapped crosslinguistically via an ontology
  • No license constraints, freely available
the ontology main features
The Ontology: Main Features
  • Formal, artificial ontology serves as universal index of concepts
  • List of concepts is not just based on the lexicon of a particular language (unlike in EuroWordNet) but uses ontological observations
  • Concepts are related in a type hierarchy
  • Concepts are defined with axioms
the ontology main features29
The Ontology: Main Features
  • In addition to high-level (“primitive”) concept ontology needs to express low-level concepts lexicalized in the Grid languages
  • Additional concepts can be defined with expressions in Knowledge Interchange Format (KIF) based on first order predicate calculus and atomic element
the ontology main features30
The Ontology: Main Features
  • Minimal set of concepts (Reductionist view):
    • to express equivalence across languages
    • to support inferencing
  • Ontology must be powerful enough to encode all concepts that are lexically expressed in any of the Grid languages
the ontology main features31
The Ontology: Main Features
  • Ontology need not and cannot provide a linguistic encoding for all concepts found in the Grid languages
    • Lexicalization in a language is not sufficient to warrant inclusion in the ontology
    • Lexicalization in all or many languages may be sufficient
  • Ontological observations will be used to define the concepts in the ontology
ontological observations
Ontological observations
  • Identity criteria as used in OntoClean (Guarino & Welty 2002), :
    • rigidity: to what extent are properties true for entities in all worlds? You are always a human, but you can be a student for a short while.
    • essence: what properties are essential for an entity? Shape is essential for a statue but not for the clay it is made of.
    • unicity:what represents a whole and what entities are parts of these wholes? An ocean is a whole but the water it contains is not.
type role distinction
Type-role distinction
  • Current WordNet treatment:

(1) a husky is a kind of dog(type)

(2) a husky is a kind of working dog (role)

  • What’s wrong?

(2) is defeasible, (1) is not:

*This husky is not a dog

This husky is not a working dog

Other roles: watchdog, sheepdog, herding dog, lapdog, etc….

ontology and lexicon
Ontology and lexicon
  • Hierarchy of disjunct types:

Canine  PoodleDog; NewfoundlandDog; GermanShepherdDog; Husky

  • Lexicon:
    • NAMES for TYPES:

{poodle}EN, {poedel}NL, {pudoru}JP

      • ((instance x Poodle)
    • LABELS for ROLES:

{watchdog}EN, {waakhond}NL, {banken}JP

((instance x Canine) and (role x GuardingProcess))

ontology and lexicon35
Ontology and lexicon
  • Hierarchy of disjunct types:

River; Clay; etc…

  • Lexicon:
    • NAMES for TYPES:

{river}EN, {rivier, stroom}NL

      • ((instance x River)
    • LABELS for dependent concepts:

{rivierwater}NL (water from a river => water is not Unit)

((instance x water) and (instance y River) and (portion x y)

{kleibrok}NL (irregularly shared piece of clay=>Non-essential)

((instance x Object) and (instance y Clay) and (portion x y) and (shape X Irregular))

rigidity
Rigidity
  • The “primitive” concepts represented in the ontology are rigid types
  • Entities with non-rigid properties will be represented with KIF statements
  • But: ontology may include some universal, core concepts referring to roles like father, mother
properties of the ontology
Properties of the Ontology
  • Minimal: terms are distinguished by essential properties only
  • Comprehensive: includes all distinct concepts types of all Grid languages
  • Allows definitions via KIF of all lexemes that express non-rigid, non-essential properties of types
  • Logically valid, allows inferencing
mapping grid languages onto the ontology
Mapping Grid Languages onto the Ontology
  • Explicit and precise equivalence relations among synsets in different languages, which is somehow easier:
    • type hierarchy is minimal
    • subtle differences can be encoded in KIF expressions
  • Grid database contains wordnets with synsets that label
    • either “primitive” types in the hierarchies,
    • or words relating to these types in ways made explicit in KIF expressions
  • If 2 lgs. create the same KIF expression, this is a statement of equivalence!
how to construct the gwng
How to construct the GWNG
  • Take an existing ontology as starting point;
  • Use English WordNet to maximize the number of disjunct types in the ontology;
  • Link English WordNet synsets as names to the disjunct types;
  • Provide KIF expressions for all other English words and synsets
how to construct the gwng40
How to construct the GWNG
  • Copy the relation from the English Wordnet to the ontology to other languages, including KIF statements built for English
  • Revise KIF statements to make the mapping more precise
  • Map all words and synsets that are and cannot be mapped to English WordNet to the ontology:
    • propose extensions to the type hierarchy
    • create KIF expressions for all non-rigid concepts
initial ontology sumo niles and pease
Initial Ontology: SUMO (Niles and Pease)

SUMO = Suggested Upper Merged Ontology

--consistent with good ontological practice

--fully mapped to WordNet(s): 1000 equivalence mappings, the rest through subsumption

--freely and publicly available

--allows data interoperability

--allows NLP

--allows reasoning/inferencing

mapping grid languages onto the ontology42
Mapping Grid languages onto the Ontology
  • Check existing SUMO mappings to Princeton WordNet -> extend the ontology with rigid types for specific concepts
  • Extend it to many other WordNet synsets
  • Observe OntoClean principles! (Synsets referring to non-rigid, non-essential, non-unicitous concepts must be expressed in KIF)
lexicalizations not mapped to wordnet
Lexicalizations not mapped to WordNet
  • Not added to the type hierarchy:

{straathond}NL (a dog that lives in the streets)

    • ((instance x Canine) and (habitat x Street))
  • Added to the type hierarchy:

{klunen}NL (to walk on skates from one frozen body to the next over land)

KluunProcess => WalkProcess

Axioms:

(and (instance x Human) (instance y Walk) (instance z Skates) (wear x z) (instance s1 Skate) (instance s2 Skate) (before s1 y) (before y s2) etc…

  • National dishes, customs, games,....
most mismatching concepts are not new types
Most mismatching concepts are not new types
  • Refer to sets of types in specific circumstances or to concept that are dependent on these types, next to {rivierwater}NL there are many others:

{theewater}NL (water used for making tea)

{koffiewater}NL (water used for making coffee)

{bluswater}NL (water used for making extinguishing file)

  • Relate to linguistic phenomena:
    • gender, perspective, aspect, diminutives, politeness, pejoratives, part-of-speech constraints
kif expression for gender marking
KIF expression for gender marking
  • {teacher}EN

((instance x Human) and (agent x TeachingProcess))

  • {Lehrer}DE ((instance x Man) and (agent x TeachingProcess))
  • {Lehrerin}DE ((instance x Woman) and (agent x TeachingProcess))
kif expression for perspective
KIF expression for perspective

sell: subj(x), direct obj(z),indirect obj(y)

versus

buy: subj(y), direct obj(z),indirect obj(x)

(and (instance x Human)(instance y Human) (instance z Entity) (instance e FinancialTransaction) (source x e) (destination y e) (patient e)

The same process but a different perspective by subject and object realization: marry in Russian two verbs, apprendre in French can mean teach and learn

parallel noun and verb hierarchy
Parallel Noun and Verb hierarchy

Encoded once as a Process in the ontology!

  • event
    • act
      • deed
        • sail
        • promise
    • change
      • movement
        • change of location
  • to happen
    • to act
      • to do
        • to sell
        • a promise
    • to change
      • to move
        • to move position
part of speech mismatches
Part-of-speech mismatches
  • {bankdrukken-V}NL vs.{bench press-N}EN
  • {gehuil-N}NL vs. {cry-V}EN
  • {afsluiting-N}NL vs. {close-V}EN
  • Process in the ontology is neutral with respect to POS!
aspectual variants
Aspectual variants
  • Slavic languages: two members of a verb pair for an ongoing event and a completed event.
  • English: can mark perfectivity with particles, as in the phrasal verbs eat up and read through.
  • Romance languages: mark aspect by verb conjugations on the same verb.
  • Dutch, verbs with marked aspect can be created by prefixing a verb with door: doorademen, dooreten, doorfietsen, doorlezen, doorpraten(continue to breathe/eat/bike/read/talk).
  • These verbs are restrictions on phases of the same process
  • Which does NOT warrant the extension of the ontology with separate processes for each aspectual variant
aspectual lexicalization
Aspectual lexicalization
  • Regular compositional verb structures:

doorademen: (lit. through+breath, continue to breath)

doorbetalen: (lit. through+pay, continue to pay)

doorlopen: (lit. through+walk, continue to walk)

doorfietsen: (lit. through+walk, continue to walk)

doorrijden: (lit. through+walk, continue to walk)

(and (instance x BreathProcess)(instance y Time) (instance z Time) (end x z) (expected (end x y) (after z y))

slide51

Lexicalization of Resultatives

  • MORE GENERAL VERBS:

openmaken: (lit. open+make, to cause to be open);

dichtmaken: (lit. close+make, to cause to be open);

  • MORE SPECIFIC VERBS:

openknijpen (lit. open+squeeze, to open by squeezing)

has_hyperonym knijpen (squeeze) & openmaken (to open)

opendraaien (lit. open+turn, to open by turning)

has_hyperonym draaien (to turn) & openmaken (to open)

dichtknijpen: (lit. closed+squeeze, to close by squeezing)

has_hyperonym knijpen (squeeze) & dichtmaken (to close)

dichtdraaien: (lit. closed +turn, to close by turning)

has_hyperonym draaien (to turn) & dichtmaken (to close)

kinship relations in arabic
Kinship relations in Arabic
  • عَم(Eam~) father's brother, paternal uncle.
  • خَال (xaAl) mother's brother, maternal uncle.
  • عَمَّة (Eam~ap) father's sister, paternal aunt.
  • خَالَة (xaAlap) mother's sister, maternal aunt
kinship relations in arabic53
Kinship relations in Arabic
  • .........
  • شَقِيقَة ($aqiyqapfull) sister, sister on the paternal and maternal side (as distinct from أُخْت(>uxot): 'sister' which may refer to a 'sister' from paternal or maternal side, or both sides).
  • ثَكْلان (vakolAna) father bereaved of a child (as opposed to يَتِيم(yatiym) or يَتِيمَة(yatiymap) for feminine: 'orphan' a person whose father or mother died or both father and mother died).
  • ثَكْلَى (vakolaYa) other bereaved of a child (as opposed to يَتِيم or يَتِيمَة for feminine: 'orphan' a person whose father or mother died or both father and mother died).
complex kinship concepts
Complex Kinship concepts

father's brother, paternal uncle

WORDNET

paternal uncle => uncle

=> brother of ....????

ONTOLOGY

(=>

(paternalUncle ?P ?UNC)

(exists (?F)

(and

(father ?P ?F)

(brother ?F ?UNC))))

advantages of the global wordnet grid
Advantages of the Global Wordnet Grid
  • Shared and uniform world knowledge:
    • universal inferencing
    • uniform text analysis and interpretation
  • More compact and less redundant databases
  • More clear notion how languages map to the knowledge
    • better criteria for expressing knowledge
    • better criteria for understanding variation
expansion with pure hyponymy relations
Expansion with pure hyponymy relations

dog

hunting dog

puppy

dachshund

lapdog

poodle

bitch

street dog

watchdog

short hair

dachshund

long hair

dachshund

Expansion from a type to roles

expansion with pure hyponymy relations57
Expansion with pure hyponymy relations

dog

hunting dog

puppy

dachshund

lapdog

poodle

bitch

street dog

watchdog

short hair

dachshund

long hair

dachshund

Expansion from a role to types and other roles

full understanding is fundamentally impossible but
Full understanding is fundamentally impossible BUT?
  • How can people communicate?
  • How can people coomunicate with computers?
  • As long as language is effective:
    • meaning= to have the desired effect!
    • Link language to useful content!
slide62

Thought

携帯電話

(keitaidenwa)

Texts

Expression

Objects

in reality

Ontology

Knowledge &

information

  • Useful and effective behavior:
  • reason over knowledge
  • collect information and data
  • deliver services and be helpful
concrete goals for gwg
Concrete goals for GWG
  • Global Wordnet Association website:

http://www.globalwordnet.org/gwa/gwa_grid.htm

  • 5000 Base Concepts or more:
    • English
    • Spanish
    • Catalan
    • Czech, Polish, Dutch, other wordnets
  • 7th Frame Work project Kyoto
kyoto project
KYOTO Project
  • 7th Frame Work project (under negotiation)
  • Kowledge Yielding Ontologies for Transition-based Organisations
  • Goal:
    • Global Wordnet Grid = ontology + wordnets
    • AutoCons = Automatic concept extractors
    • Kybots = Knowledge yielding robots
    • Wiki environment for encoding domain knowledge in expert groups
    • Index and retrieval software for deep semantic search
  • Languages: Dutch, English, Spanish, Basque, Italian, Chinese and Japanese
  • Domain of application: environmental organisations
  • Period: March/April 2008 - 2011
kyoto consortium
KYOTO Consortium

Universities

  • Vrije Universiteit Amterdam, Amsterdam, Netherlands
  • Consiglio Nazionale delle Ricerche, Pisa, Italy
  • Berlin-Brandenburg Academy of Sciences and Humantities, Berlin, Germany
  • Euskal Herriko Unibertsitatea, San Sebastian, Spain
  • Academia Sinica, Taipei, Taiwan
  • National Institute of Information and Communications Technology, Kyoto, Japan
  • Masaryk University, Brno, Czech

Companies

  • Irion Technologies, Delft, Netherlands
  • Synthema, Pisa, Italy

Users

  • European Centre for Nature Conservation, Tilburg, Netherlands
  • World Wide Fund for Nature, Zeist, Netherlands
slide66

Citizens

Governors

Companies

Environmental

organizations

Environmental

organizations

Domain

Wiki

Capture

Universal Ontology

Wordnets

Concept

Mining

Docs

Dialogue

Top

Abstract

Physical

Fact

Mining

Search

URLs

Process

Substance

Experts

Middle

water

CO2

Index

Images

water

pollution

CO2

emission

Domain

slide67

wordnet

ontology

domain

ontology

domain

wordnet

4

Bench

mark

data

User

scenarios

Wiki

DEB

Client

DEB

Server

7

8

term

hierarchy

Manual

Test

Manual

Revision

Concept

Miners

term

relations

3

Access

end-users

Bench

marking

User

scenarios

1

source

data

Text & Meta data

in XMLFormat

Data & Facts

in XML Format

Index

1

Kybots

Indexing

Capture

2

5

6

slide68

Ontology

Logical Expressions

Wordnets

Linguistic Miners

or Kybots

Generic

Abstract

Physical

words

words

Substance

Process

Chemical

Reaction

water

CO2

Domain

CO2

emission

water

pollution

words

words