Wordnet

From WordNet,
to EuroWordNet,
to the Global Wordnet Grid:
anchoring languages to universal meaning
Piek Vossen
VU University Amsterdam
1

Princeton WordNet
• http://wordnet.princeton.edu/
• Developed by George Miller and his team at
Princeton University, as the implementation of
a mental model of the lexicon
• Organized around the notion of a synset: a set
of synonyms in a language that represent a
single concept
• Semantic relations between concepts
• Covers over 117,000 concepts and over
150,000 English words
2

What kind of resource is
WordNet?
• Mostly used database in language
technology
• Enormous impact in language technology
development
• Large
• Free and downloadable
• English
3

Wordnet Starting point
• Lexical database organized around concepts instead of lexical
forms:
– Separates lexical forms from concepts
– Defines concepts through a relational model of meaning and
not an encyclopedic view
• Concept is defined by the notion of a synset, synsets distinguish
word meanings:
– {board, plank}{board, plank} {board, committee}{board, get on}
• The ‘synset’ as a weak notion of synonymy:
“two expressions are synonymous in a linguistic context C if the
substitution of one for the other in C does not alter the truth
value.” (Miller et al. 1993)
4

Wordnet: a network of semantically
related words
{conveyance;transport}
{vehicle}
{motor vehicle; automotive vehicle}
{car; auto; automobile; machine; motorcar}
{cruiser; squad car; patrol car;
police car; prowl car}
{car door}
{bumper}
{cab; taxi; hack; taxicab}
{car mirror}
{car window}
{armrest}
{doorlock}
{hinge;
flexible joint}
5

Polysemy & Word Forms
Synsets (word meanings)
contain one or more
word forms
Word forms in multiple
synsets are polysemous
Polysemous word forms
are said to have multiple
senses

Polysemy, Familiarity
& Zipf's Law
Zipf's law:
There is a constant k such that f * r = k
In words: there is a predictable relation between the
frequency of a word and its rank
Zipf's other law:
The number of meanings of a word is related to its
frequency of use
Polysemy indicates familiarity in wordnet:
• “horse” horse” has 6 meanings, “equus caballus” has 1

Familiarity & SemCor
Sense numbers indicate frequency in SemCor (250,000
tokens from Brown corpus manually tagged with WordNet
senses):
• {horse:1, Equus caballus:1} => animal
• {horse:5, knight:2} => in chess

Wordnet 3.0 statistics
POS Unique Synsets Total
Strings Word-Sense
Pairs
Noun 117,798 82,115 146,312
Verb 11,529 13,767 25,047
Adjective 21,479 18,156 30,002
Adverb 4,481 3,621 5,580
Totals 155,287 117,659 206,941
9

Wordnet 3.0 statistics
POS Monosemous Polysemous Polysemous
Words and
Senses
Words Senses
Noun 101,863 15,935 44,449
Verb 6,277 5,252 18,770
Adjective 16,503 4,976 14,399
Adverb 3,748 733 1,832
Totals 128,391 26,896 79,450
10

Semantic organization of
Nouns in WordNet
25 unique beginners

noun.Tops file
Contains very general classifi cations

Lexicalization patterns
building
church
abbey
artifact
bird
object
canary
common
canary
animal
dog
entity
organism
crocodile
tree
plant
fl ower
rose
top-layer
25 unique
beginners
Basic Level
Concepts
(Rosch)
13

Lexicalization patterns
building
church
abbey
artifact
bird
object
canary
common
canary
animal
dog
entity
organism
crocodile
tree
plant
fl ower
• balance of two rose principles:
top-layer
25 unique
beginners
basic level
concepts
● predict most features
● apply to most subclasses
• where most concepts are created
• amalgamate most parts
• most abstract level to draw a pictures
14

inessential
souvenir
garbage
threat
Lexicalization patterns
building
church
abbey
artifact
bird
object
canary
common
canary
entity
animal
dog
organism
crocodile
tree
plant
top-layer
curiosity
....etc....
waste
25 unique
variable beginners
fl ower
rose
basic level
concepts
15

inessential
souvenir
garbage
threat
Lexicalization patterns
building
church
abbey
artifact
bird
object
canary
common
canary
entity
animal
dog
organism
crocodile
tree
plant
top-layer
curiosity
....etc....
waste
25 unique
variable beginners
fl ower
rose
basic level
concepts
16

Wordnet top level
17

leg
Meronymy & pictures
beak
tail
18

Meronymy & pictures
19

Dogs in WordNet
20

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….
21

Ontological observations
• Identity criteria as used in OntoClean (Guarino &
Welty 2002):
– rigidity: to what extent are properties true for all
instances of entities in all worlds? You are always a
human, but you can be a student for a short while.
• Ignoring this distinction leads to ISA-overloading
22

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))
23

Expansion with pure hyponymy
lapdog
relations
dog
hunting dog puppy
street dog
watchdog
poodle
short hair
dachshund
dachshund
long hair
dachshund
bitch
Expansion from a type to roles
24

Expansion with pure hyponymy
lapdog
relations
dog
hunting dog puppy
street dog
watchdog
poodle
short hair
dachshund
dachshund
long hair
dachshund
bitch
Expansion from a role to types and other roles
25

Synset definition
Synsets consist of interchangeable words or synonyms (Miller, 1998)
loose criteria
lion, king_of_beasts,
Panthera_leo [large gregarious
predatory feline of Africa and India
having a tawny coat with a shaggy
mane in the male]
strict criteria
dog, domestic doc, canis
familiaris (a member of the
genus Canis..)
Pooch, doggie, doggy,
barker, bow-wow (informal
terms for dogs)
26

Differences among wordnets
English Wordnet
large number of synsets
asshole, bastard, cocksucker, dickhead, shit,
mother fucker, motherfucker, prick, whoreson,
son of a bitch, SOB
cad, bounder, blackguard, dog, hound, heel
gasbag, windbag
rotter, rat, skunk, stinker, bum, puke, crumb,
lowlife, scum_bag, so-and-so
pain, pain_in_the_neck, nuisance
worm, louse, insect, dirt_ball
Dutch Wordnet
62 synonyms
naarling:1/r_n-24518, beroerling:1/d_n-26921,
ellendeling:1/r_n-12324, etterbak:1/d_n-75936,
etterbuil:2/d_n-75940, fielt:1/d_n-80137,
fluim:2/d_n-81948, gemenerik:1/r_n-14607,
hond:2/r_n-79023, hondenlul:1/r_n-17019,
kankerlijer:1/d_n-130709, kelerelijder:1/d_n-
540923, kelerelijer:1/d_n-147148,
klerelijer:1/r_n-19790, kloot:1/r_n-19887,
kloothommel:1/d_n-137246, klootspiraal:1/d_n-
412711, klootzak:1/r_n-19888, kwal:2/r_n-
21077, lamgat:1/d_n-152244, lammeling:1/r_n-
21272, lamstraal:1/d_n-152396, lamzak:1/r_n-
21286, lazersteen:1/d_n-413025,
lazerstraal:1/d_n-154087, loeder:1/r_n-22410,
lul:2/r_n-22757, lulhannes:1/d_n-161976,
lulletje:1/d_n-541138, miesgasser:1/d_n-
172163, mispunt:1/r_n-24006, onverlaat:1/r_n-
26320, paardelul:1/d_n-228940,
paardenlul:1/n_n-501022, patjakker:1/d_n-
212558, pleurislijder:1/r_n-28842, ploert:1/r_n-
28881, plurk:1/d_n-220067, etc. etc.
insulting terms for people who are stupid, ridiculous, irritating, lazy, slow, ……
27

Splitting or Lumping
Lumping
“one denotation and several connotations per synset”
lion, king_of_beasts, Panthera_leo
Splitting
“one denotation and one connotation per synset”
Kraut, Krauthead, Jerry, Hun
German
hyponym

Splitting
• On which criteria? Usage (register, domain,
frequency, style, etc.); Attitude (polarity,
subjectivity); Morphology, Syntax, etc.
• Consequent splitting leads to synsets without
synonyms
• Leads to ISA-overloading (german is not a
hypernym of krauthead)

Lumping
• Consequent lumping leads to extremely
large synsets
• Low interchangeability of synonyms as
their connotations differ too much
• Low interoperability between wordnets:
precise translation equivalence is impossible
• Leads to ‘unintuitive’ synsets

Hybrid and 2-layered

Summary
• Synsets are more compact representations for concepts than word
meanings in traditional lexicons
• Synonyms and hypernyms are substitutional variants:
– begin – commence
– I once had a canary. The bird got sick. The poor animal died.
• Hyponymy and meronymy chains are important transitive relations for
predicting properties and explaining textual properties:
object -> artifact -> vehicle -> 4-wheeled vehicle -> car
• Strict separation of part of speech although concepts are closely
related (bed – sleep) and are similar (dead – death)
• Lexicalization patterns reveal important mental structures
32

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/fi nalresults-ewn.html
33

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 specifi c concepts
34

EuroWordNet Model
ride
move
go
III
III
drive
Lexical Items Table
Lexical Items Table
cabalgar
jinetear
III
III
mover
transitar
conducir
Domains
Traffic
ILI-record
{drive}
Ontology
2OrderEntity
Air Road` Location Dynamic
III
rijden berijden
I
I
III
II
II
Lexical Items Table
II
Inter-Lingual-Index
I = Language Independent link
II = Link from Language Specific
to Inter lingual Index
III = Language Dependent Link
II
guidare
bewegen
gaan
Lexical Items Table
III
III
andare
muoversi
cavalcare
35

Differences in relations between
EuroWordNet and WordNet
• Added Features to relations
• Cross-Part-Of-Speech relations
• New relations to differentiate shallow hierarchies
• Different interpretations of some relations
36

Cross-Part-Of-Speech relations
WordNet1.5: nouns and verbs are not interrelated by basic semantic
relations such as hyponymy and synonymy:
adornment 2 => change of state-- (the act of changing something)
adorn 1 => change, alter-- (cause to change; make different)
EuroWordNet: words of different parts of speech can be inter-linked with
explicit xpos-synonymy, xpos-antonymy and xpos-hyponymy relations:
{adorn V} XPOS_NEAR_SYNONYM {adornment N}
{size N} XPOS_NEAR_HYPONYM {tall A}
{short A}
37

Role relations
In the case of many verbs and nouns the most salient relation is not the hyperonym
but the relation between the event and the involved participants. These relations
are expressed as follows:
{knife} ROLE_INSTRUMENT {to cut}
{to cut} INVOLVED_INSTRUMENT {knife} reversed
{school} ROLE_LOCATION {to teach}
{to teach} INVOLVED_LOCATION {school} reversed
These relations are typically used when other relations, mainly hyponymy, do not
clarify the position of the concept network, but the word is still closely related to
another word.
38

Co_Role relations
guitar player HAS_HYPERONYM player
CO_AGENT_INSTRUMENT guitar
player HAS_HYPERONYM person
ROLE_AGENT to play music
CO_AGENT_INSTRUMENT musical instrument
to play music HAS_HYPERONYM to make
ROLE_INSTRUMENT musical instrument
guitar HAS_HYPERONYM musical instrument
CO_INSTRUMENT_AGENT guitar player
ice saw HAS_HYPERONYM saw
CO_INSTRUMENT_PATIENT ice
saw HAS_HYPERONYM saw
ROLE_INSTRUMENT to saw
ice CO_PATIENT_INSTRUMENT ice saw REVERSED
39

Horizontal & vertical semantic relations
chronical patient ;
mental patient
HYPONYM
STATE
patient
disease; disorder
HYPONYM
stomach disease,
kidney disorder,
ρ-PATIENT
ρ-PATIENT
physiotherapy
medicine
etc.
ρ-CAUSE
ρ-INSTRUMENT
cure
treat
hospital, etc.
ρ-LOCATION
ρ-AGENT
docter
HYPONYM
child docter
co-ρ-
AGENT-PATIENT
child
40

Overview of the Language
Internal relations in EuroWordnet
Same Part of Speech relations:
NEAR_SYNONYMY apparatus - machine
HYPERONYMY/HYPONYMY car - vehicle
ANTONYMY open - close
HOLONYMY/MERONYMY head - nose
Cross-Part-of-Speech relations:
XPOS_NEAR_SYNONYMY dead - death; to adorn - adornment
XPOS_HYPERONYMY/HYPONYMY to love - emotion
XPOS_ANTONYMY to live - dead
CAUSE die - death
SUBEVENT buy - pay; sleep - snore
ROLE/INVOLVED write - pencil; hammer - hammer
STATE the poor - poor
MANNER
BELONG_TO_CLASS
to slurp - noisily
Rome - city
41

The Multilingual Design
• Inter-Lingual-Index: unstructured fund of concepts to
provide an effi cient 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;
42

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
43

EN-Net
toe
finger
head
NL-Net
hoofd
kop
Complex mappings across
languages
{ toe : part of foot }
{ finger : part of hand }
{ dedo , dito :
finger or toe }
{ head : part of body }
{ hoofd : human head }
{ kop : animal head }
= normal equivalence
= eq _has_hyponym
= eq _has_hyperonym
IT-Net
dito
ES-Net
dedo
44

Typical gaps in the (English)
ILI
• Dutch:
doodschoppen (to kick to death):
eq_hyperonym {kill}V and to {kick}V
aardig (Adjective, to like):
eq_near_synonym {like}V
cassière (female cashier)
eq_hyperonym {cashier}, {woman}
kunstproduct (artifact substance)
eq_hyperonym {artifact} and to {product}
• Spanish:
alevín (young fi sh):
eq_hyperonym {fi sh} and eq_be_in_state {young}
cajera (female cashier)
eq_hyperonym {cashier}, {woman}
45

Wordnets as semantic
structures
• Wordnets are unique language-specifi c 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
46

Autonomous & Language-Specific
Wordnet1.5 Dutch Wordnet
object
artifact, artefact
(a man-made object)
natural object (an
object occurring
naturally)
block instrumentality body
implement
container
device
tool instrument
box spoon bag
blok
{block}
bak
{box}
voorwerp
{object}
werktuig{tool}
lepel
{spoon}
tas
{bag}
lichaam
{body}
47

• Wordnets:
Wordnets versus ontologies
• 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.
48

Building wordnets
• Two major approaches:
– Expand model: translate the English synonyms and
copy the synsets and relations;
• Fast and cheap
• Benefi ts from English research and resources
• Bias by Princeton wordnet
– Merge model: build wordnet independently of English
and create equivalence mapping afterwards:
• Slow and expensive
• Complicated since the structures differ and you cannot
change English
• Better representation of the language structure:
theoretically more sound (true WORD-net)
49

How to harmonize wordnets?
• Define universal sets of concepts that play a major role in
many different wordnets: so-called Base Concepts
• Define base concepts in each language wordnet
– High level in the hierarchy
– Many hyponyms
• Provide the closest equivalent in English wordnet
• Expand down-ward with hyponyms & determine the
intersection of English equivalences
50

garbage
threat
Base Concepts in Wordnet
building
church
abbey
artifact
object
bird
canary
common
canary
entity
animal
dog
organism
crocodile
plant
tree
fl ower
rose
25 unique
beginners
1024 base
concepts
basic level
concepts
51

Common Base Concepts
Important in at least two languages
Nouns Verbs Total
Physical objects & substances 491 491
Processes and states 272 228 500
Mental objects 33 33
Total 796 228 1024
52

Synsets No. of senses Sens./
syns.
EuroWordNet data
Entries Sens./
entry
LIRels. LIRels/
syns
EQRels-
ILI
EQRels/
syn
Synsets
without
ILI
Dutch 44015 70201 1,59 56283 1,25 111639 2,54 53448 1,21 7203
Spanish 23370 50526 2,16 27933 1,81 55163 2,36 21236 0,91 0
Italian 40428 48499 1,20 32978 1,47 117068 2,90 71789 1,78 1561
French 22745 32809 1.44 18777 1.75 49494 2.18 22730 1.00 20
German 15132 20453 1.35 17098 1.20 34818 2.30 16347 1.08 0
Czech 12824 19949 1.56 12283 1.62 26259 2.05 12824 1.00 0
Estonian 7678 13839 1.80 10961 1.26 16318 2.13 9004 1.17 0
English 16361 40588 2,48 17320 2,34 42140 2,58 n.a. n.a. n.a.
WN15 94515 187602 1,98 126617 1,48 211375 2,24 n.a. n.a. n.a.
53

From EuroWordNet to Global
WordNet
• Currently, wordnets exist for more than 70 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
54

Indo Wordnet Project
http://www.cfi lt.iitb.ac.in/wordnet/webhwn/
• Basis for 10 year Indian Machine translation
project: translation through ILI is more
effi cient than building translation memories
• Hindi wordnet as the ILI, while Hindi is linked
to the English wordnet
• About 20 Indian languages (900 million
speakers)
55

Indo Wordnet progress 2010
Synsets Words Synsets Words
Assamese 353 19.609 Marathi 9.739 21.223
Bengali 8.679 18.563 Nepali 5.802 10.278
Bodo 3.837 13.357 Oriya To start
Gugarati 970 2.125 Punjabi To start
Hindi 33.900 8.200 Sanskrit 3.340 17.820
Kannad 5.920 7.344 Tamil 4.750 9.821
Kashmiri 6.569 8.674 Telugu 10.639 18.250
Malayalam 6.154 8.622 Urdu 6.123 9.641
Manipuri 2.744 5.231
56

Asian Wordnet Project
http://asianwordnet.org/
57

Asian Wordnet Project
http://asianwordnet.org/
• Built using a Wordnet Management System
developed by NICT, NECTEC Bangkok,
Thailand
• Free download: Apache 2.0+, PHP 5.2+,
MySQL 5.0+
• Translation of English wordnet (expand
model), voting, progress monitoring,
intersection and overlap
• Uses Wordnet-LMF as exchange format
58

Asian Wordnet Editor
59

Asian Wordnet Editor
60

Asian Wordnet Editor
61

South African Wordnets
• Started in 2008 as a collaboration of the Department of
African Languages at UNISA and the Centre for Text
Technology (CTexT®) at the North-West University.
• Languages:
– Afrikaans
– Setswana, isiNdebele, isiZulu, isiXhosa and Sesotho sa
Leboa
• Nr. synsets: between 5,000 and 15,000 per language and
will be completed by the end of January 2011.
• Linked to the English wordnet
• DebVisDic wordnet editing environment:
http://deb.fi .muni.cz/clients-debvisdic.php
62

Some downsides of the
EuroWordnet model
• 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
63

vehículo
1
auto tren
vehicle
1
car train
2
English Words
2
Spanish Words
Next step: Global WordNet
Grid
veicolo
1
auto treno
2
Italian Words
Inter-Lingual
Ontology
Object
Device
TransportDevice
3 3
dopravní prost edník
1
auto vlak
2
Czech Words
voertuig
1
auto trein
2
Dutch Words
véhicule
1
voiture train
2
French Words
Fahrzeug
Auto Zug
1
2
German Words
liiklusvahend
1
auto killavoor
2
Estonian Words
64

GWN-Grid: 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
65

The Ontology: Main Features
• Formal 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
• Ontology contains only upper and mid-level
concepts but concepts can be derived using formal
expressions in e.g. KIF or RDF
• Concepts are related in a type hierarchy
• Concepts are defined with axioms
66

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
• Ontology need not and cannot provide a linguistic
encoding (label) 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
67

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)
WalkProcess = KluunProcess
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,....
68

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 other:
{theewater}NL (water used for making tea)
{koffiewater}NL (water used for making coffee)
{bluswater}NL (water used for extinguishing fire)
• Relate to linguistic phenomena:
– gender, perspective, aspect, diminutives, politeness,
pejoratives, part-of-speech constraints
69

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))
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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
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Open Questions/Challenges
• What is a word, i.e., a lexical unit?
• What is the status of complex lexemes like
English lightning rod, word of mouth, find
out, kick the bucket?
• What is a semantic unit, i.e. a concept?
72

Open Questions/Challenges
• Is there a core inventory of concepts that are
universally encoded?
• If so, what are these concepts?
• How can crosslinguistic equivalence be verified?
• Is there systematicity to the language-specific
extensions?
• What are the lexicalization patterns of individual
languages?
• Are lexical gaps accidental or systematic?
73

Global Wordnet Grid
Installations
• Global Wordnet Association:
– Upload and GWG viewer through DebVisDic
• KYOTO project
– WordNet-LMF
– Web service and editor
– Mapping of complete English Wordnet to
DOLCE

DebVisDic GWA
http://deb.fi.muni.cz:9000/gwgeng?action=listPreview

GWA-KYOTO

Knowledge Integration in KYOTO
• A model of division of labour (along the lines of Putnam 1975) in which
knowledge is stored in 3 layers:
– Vocabularies, term databases, etc. (SKOS)
– WordNet (WN-LMF)
– Ontology (OWL-DL)
• Mapping relations that support the division of labour
– language-specific conceptualizations
• Each layer supports different types of inferencing
– SparQL queries
– Graph algorithms (UKB, SSID+)
– Formal reasoning (OWL-DL reasoners, FACT++)

3-layered knowledge model
Division of labor (Putnam 1975)
Geonames
8 million places
150,486
mappings
663
mappings
Species 2000
3 million species
English Wordnet 3.0
100,000 concepts
Top synsets
Base Concepts
1,000
All nouns All verbs All adjectives
Domain Wordnet
1216 synsets
210 WN3.0 synsets
1,006 new synsets
200,000 mappings
990 mappings
Wn: polluted water wo:patient
Wn: greenhouse gas
wo:done-by
Kyoto ontology
2,000 classes, 3,000 axioms
Dolce-Lite
OntoWordNet
Endurant Perdurant
Quality
Domain concepts
Ont: pollution
Ont: warming
Spanish WN: BC+equi
Japan WN: BC+equi
Basque WN: BC+equi
Chinese WN: BC+equi
Italian WN: BC+equi
Dutch WN: BC+equi

Division of labor in knowledge sources
Skos database Wordnet-LMF Ontology-OWL-DL
2.1 million species 100,000 synsets 2,000 classes & 3,000 axioms
Animalia
Chordata
Amphibia
Anura
Leptodactylidae
Eleutherodactylus
Eleutherodactylus
atrabracus
Eleutherodactylus
augusti
animal:1
Base Concept
chordate:1
vertebrate:1,craniate:1
amphibian:3
frog:1, toad:1, toad frog:1,
anuran:1, batrachian:1, salientian:1
barking frog
endurant
physical-object endanger
organism
perdurant
Term database
500,000 terms
endemic frog
endangered frog
poisonous frog
alien frog

Example
268 Species 2000 concepts
Animalia/Chordata/Aves/Anseriformes/Anati
dae/Anas/ITS-175103 : Yellow-billed Pintail
eng-3.0-01847565-n
297 WN3.0 Base Concepts
01507175-n 05 399 bird_genus
Connected to KYOTO ontology
bird_genus-eng-3.0-01507175-n type

Wordnet ontology relations
Rigid vs. Non-rigid
Rigid
• Synset:Endurant; Synset:Perdurant; Synset:Quality:
• sc_equivalenceOf or sc_subclassOf
Non-rigid:
• Synset:Role; Synset:Endurant
• sc_domainOf: range of ontology types that restricts a role
• sc_playRole: role that is being played
Rigidity can be detected automatically (Rudify, 80% precision,
IAG 80%) and is stored in Wordnet-LMF as attributes to synsets

Wordnet to ontology mappings
{create, produce, make}Verb, English
-> sc_ equivalenceOf construction
{artifact, artefact}Noun, English
-> sc_domainOf physical_object
-> sc_playRole result-existence
-> sc_participantOf construction
{kunststof}Noun, Dutch // lit. artifact
substance
-> sc_domainOf amount_of_matter
-> sc_playRole result-existence
-> sc_participantOf construction
{meat}Noun, English
-> sc_domainOf cow, sheep, pig
-> sc_playRole patient
-> sc_playRole eat
{ 名 肉 , 食物 , 餐 }Noun, Chinese
-> sc_domainOf animal
-> sc_playRole patient
-> sc_playRole eat
{ ماعط ,محل ,ءاذغ}Noun, Arabic
-> sc_domainOf cow, sheep
-> sc_playRole patient
-> sc_playRole eat

Wordnet to ontology mappings
{teacher}Noun, English
-> sc_domainOf human
-> sc_playRole done-by
-> sc_participantOf teach
{leraar}Noun, Dutch // lit. male teacher
-> sc_domainOf man
-> sc_playRole done-by
-> sc_participantOf teach
{lerares}Noun, Dutch // lit. female teacher
-> sc_domainOf woman
-> sc_playRole done-by
-> sc_participantOf teach

Wordnet-LMF

WN-LMF Synset relations

WN-LMF Synset relations

WN-LMF Synset relations

KYOTO project: Wikyoto editor
http://www.wikyoto.net/