From WordNet, to EuroWordNet, to the Global Wordnet Grid: anchoring languages to universal meaning

1
From WordNet, to EuroWordNet, to the Global
Wordnet Grid anchoring languages to universal
meaning

• Piek Vossen
• VU University Amsterdam

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What kind of resource is wordnet?

• Mostly used database in language technology
• Enormous impact in language technology
  development
• Large
• Free and downloadable
• English

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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

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Relational model of meaning
animal
kitten
man
boy
man
woman
cat
meisje
boy
girl
dog
puppy
woman
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Wordnet a network of semantically related words
conveyancetransport
vehicle
armrest
car mirror
motor vehicle automotive vehicle
car door
doorlock
car auto automobile machine motorcar
bumper
hinge flexible joint
car window
cruiser squad car patrol car police car
prowl car
cab taxi hack taxicab
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Wordnet Semantic Relations
WN 1.5 starting point 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) Relations between synsets Relation POS-co
mbination Example ANTONYMY adjective-to-adjectiv
e good/bad verb-to-verb open/
close HYPONYMY noun-to-noun car/
vehicle verb-to-verb walk/ move MERONYMY noun-
to-noun head/ nose ENTAILMENT verb-to-verb buy/
pay CAUSE verb-to-verb kill/ die
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Wordnet Data Model
Vocabulary of a language
Concepts
Relations

• 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
rec42654 - musician
type-of
rec35576 - string of instrument
1
part-of
string
rec29551 - underwear
2
rec25876 - string instrument
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Some observations on Wordnet

• 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 -gt artifact -gt vehicle -gt 4-wheeled
  vehicle -gt 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

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Lexicalization patterns
entity
25 unique beginners
organism
object
garbage
threat
animal
artifact
plant
waste
building
tree
bird
flower
basic level concepts
canary
church
rose
crocodile
dog

• balance of two principles
• predict most features
• apply to most subclasses
• where most concepts are created
• amalgamate most parts
• most abstract level to draw a pictures

common canary
abbey
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Wordnet top level
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Meronymy pictures
beak
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Meronymy pictures
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Co-reference constraint in wordnetCats cannot
be a kind of cats

• S (n) cat, true cat (feline mammal usually
  having thick soft fur and no ability to roar
  domestic cats wildcats)
• S (n) guy, cat, hombre, bozo (an informal term
  for a youth or man) "a nice guy" "the guy's only
  doing it for some doll"
• S (n) cat (a spiteful woman gossip) "what a cat
  she is!"
• S (n) kat, khat, qat, quat, cat, Arabian tea,
  African tea (the leaves of the shrub Catha edulis
  which are chewed like tobacco or used to make
  tea has the effect of a euphoric stimulant) "in
  Yemen kat is used daily by 85 of adults"
• S (n) cat-o'-nine-tails, cat (a whip with nine
  knotted cords) "British sailors feared the cat"
• S (n) Caterpillar, cat (a large tracked vehicle
  that is propelled by two endless metal belts
  frequently used for moving earth in construction
  and farm work)
• S (n) big cat, cat (any of several large cats
  typically able to roar and living in the wild)
• S (n) computerized tomography, computed
  tomography, CT, computerized axial tomography,
  computed axial tomography, CAT (a method of
  examining body organs by scanning them with X
  rays and using a computer to construct a series
  of cross-sectional scans along a single axis)
• S (n) domestic cat, house cat, Felis domesticus,
  Felis catus (any domesticated member of the genus
  Felis)

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Wordnet 3.0 statistics
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Wordnet 3.0 statistics
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Wordnet 3.0 statistics
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http//www.visuwords.com
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Usage of Wordnet

• Improve recall of textual based analysis
• Query -gt Index
• Synonyms commence begin
• Hypernyms taxi -gt car
• Hyponyms car -gt taxi
• Meronyms trunk -gt elephant
• Lexical entailments gun -gt shoot
• Inferencing
• what things can burn?
• Expression in language generation and
  translation
• alternative words and paraphrases

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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

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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" - gt text with
  specific cases "car"

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Improve recall

• Summarizers
• Sentence selection based on word counts -gt
  concept counts
• Avoid repetition in summary -gt language
  generation
• Limited inferencing detect locations,
  organisations, etc.

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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 expectations using Wordnet and make
  better choices
• Sentiment and opinion mining
• Natural language learning

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Recall Precision
jail
cell phone
mobile phones
nerve cell police cell
neuron
recall doorsnede / relevant precision
doorsnede / gevonden
Recall lt 20 for basic search engines! (Blair
Maron 1985)?
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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-ew
  n.html

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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

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EuroWordNet Model
II
II
Inter-Lingual-Index
I Language Independent link II Link from
Language Specific to Inter lingual
Index III Language Dependent Link
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EuroWordNet Design
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Differences in relations between EuroWordNet and
WordNet

• Added Features to relations
• Cross-Part-Of-Speech relations
• New relations to differentiate shallow
  hierarchies
• New interpretations of relations

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EWN Relationship Labels
Disjunction/Conjunction of multiple relations of
the same type WordNet1.5 door1 -- (a swinging or
sliding barrier that will close the entrance to a
room or building "he knocked on the door" "he
slammed the door as he left") PART OF doorway,
door, entree, entry, portal, room access door 6
-- (a swinging or sliding barrier that will close
off access into a car "she forgot to lock the
doors of her car") PART OF car, auto,
automobile, machine, motorcar.
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EWN Relationship Labels
airplane HAS_MERO_PART conj1
door HAS_MERO_PART conj2 disj1 jet
engine HAS_MERO_PART conj2 disj2 propeller
door HAS_HOLO_PART disj1 car HAS_HOLO_PAR
T disj2 room HAS_HOLO_PART disj3
entrance dog HAS_HYPERONYM
conj1 mammal HAS_HYPERONYM
conj2 pet albino HAS_HYPERONYM
disj1 plant HAS_HYPERONYM
disj2 animal Default Interpretation
non-exclusive disjunction
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EWN Relationship Labels
Factive/Non-factive CAUSES (Lyons 1977) factive
(default interpretation) to kill causes to
die kill CAUSES die non-factive E1
probably or likely causes event E2 or E1 is
intended to cause some event E2 to search
may cause to find. search CAUSES find
non-factive
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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) EuroWor
dNet 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 adornmen
t N size N XPOS_NEAR_HYPONYM tall
A short A
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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 schoo
l ROLE_LOCATION to teach to
teach INVOLVED_LOCATION school reversed T
hese 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.
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Co_Role relations
guitar player HAS_HYPERONYM player CO_AGENT_INS
TRUMENT guitar player HAS_HYPERONYM person ROL
E_AGENT to play music CO_AGENT_INSTRUMENT musi
cal 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_INSTRU
MENT to saw ice CO_PATIENT_INSTRUMENT ice saw
REVERSED
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Co_Role relations
Examples of the other relations
are criminal CO_AGENT_PATIENT victim novel
writer/ poet CO_AGENT_RESULT novel/
poem dough CO_PATIENT_RESULT pastry/
bread photograpic camera CO_INSTRUMENT_RESULT phot
o
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Overview of the Language Internal relations in
EuroWordnet
Same Part of Speech relations NEAR_SYNONYMY app
aratus - machine HYPERONYMY/HYPONYMY car -
vehicle ANTONYMY open - close HOLONYMY/MERONY
MY 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 to slurp - noisily
BELONG_TO_CLASS Rome - city
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Horizontal vertical semantic relations
chronical patient mental patient
?-PATIENT
HYPONYM
cure
patient
?-CAUSE
docter
treat
HYPONYM
?-AGENT
?-PATIENT
STATE
child docter
?-LOCATION
?-PROCEDURE
co-?- AGENT-PATIENT
disease disorder
HYPONYM
physiotherapy medicine etc.
hospital, etc.
stomach disease, kidney disorder,
child
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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

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Equivalent Near Synonym

• 1. Multiple Targets (1many)
• 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 (many1)
• Dutch wordnet versiersel near_synonym
  versiering ILI-Record decoration.
• 3. Multiple Targets and Sources (manymany)
• Dutch wordnet toestel near_synonym
  apparaat ILI-records machine device apparatus
  tool

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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 kunststof artifact substance ltgt
  artifact object

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Equivalent Hyponymy

• has_eq_hyponym
• Used when wordnet1.5 only provides more narrow
  terms. In this case there can only be a pragmatic
  difference, not a genuine cultural gap, e.g.
  Spanish dedo either finger or toe.

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Complex mappings across languages
EN-Net
IT-Net

toe
dito



toe
part of foot
finger


finger

part of hand
head

dedo
dito


,



finger or toe


head
part of body

NL-Net
ES-Net
hoofd


human head



kop


animal head
dedo
hoofd

kop
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Typical gaps in the (English) ILI

• Dutch
• doodschoppen (to kick to death)
• eq_hyperonym killV and to kickV
• aardig (Adjective, to like)
• eq_near_synonym likeV
• cassière (female cashier)
• eq_hyperonym cashier, woman
• kunstproduct (artifact substance)
• eq_hyperonym artifact and to product
• Spanish
• alevín (young fish)
• eq_hyperonym fish and eq_be_in_state young
• cajera (female cashier)
• eq_hyperonym cashier, woman

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Wordnets as semantic structures

• 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

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Autonomous Language-Specific
Wordnet1.5
Dutch Wordnet
voorwerp object
blok block
lichaam body
werktuigtool
lepel spoon
tas bag
bak box
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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 -gt container artifact hand tool object
  made of metal or plastic for eating, pouring or
  cooking

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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 -gt object, tableware, silverware,
  merchandise, cutlery,

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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.

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Sharing world knowledge

• All wordnets in the world can be linked to the
  same ontology
• All wordnets in the world can be linked to the
  same thesaurus

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Wordnet Domain information
Relations
Concepts
Vocabularies of languages
1

• rec 12345
• financial institute

rec 54321 - river side
2
bank
1
rec 9876 - small string instrument
violin
2
rec 65438 - musician playing a violin
violist
rec42654 - musician
type-of
1
rec35576 - string of an instrument
type-of
part-of
string
2
rec29551 - underwear
rec25876 - string instrument
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How to harmonize wordnets?

• Wordnets are unique language-specific
  lexicalizations patterns
• 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
• Determine the intersection of English
  equivalences

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Lexicalization patterns
entity
25 unique beginners
organism
object
garbage
threat
animal
artifact
plant
1024 base concepts
building
tree
bird
flower
basic level concepts
canary
church
rose
crocodile
dog
common canary
abbey
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Base Concept Intersection

human 1 individual1 mortal1 person1
someone1 soul1 animal 1 animate being1
beast1 brute1 creature1 fauna1 flora 1
plant1 plant life1 matter 1
substance1 food 1 nutrient1 feeling
1 act 1 human action1 human activity1
cause 6 get9 have7 induce2 make12
stimulate3 create 2 make13 go 14
locomote1 move15 travel4 be 4 have the
quality of being1
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Explanations for low intersection of Base Concepts

• The individual selections are not representative
  enough.
• There are major differences in the way meanings
  are classified, which have an effect on the
  frequency of the relations.
• The translations of the selection to WordNet1.5
  synsets are not reliable
• The resources cover very different vocabularies

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Concepts selected by at least two languages
intersections of pairs
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Common Base Concepts
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Table 4 Number of Common BCs represented in the
local wordnets Related to CBCs Eq_synonym Eq_nea
r CBCs Without Direct Equivalent NL 992 7
25 269 97 ES 1012 1009 0 15 IT 878 759 19
1 9 Table 5 BC4 Gaps in at least two
wordnets (10 synsets) body covering1 mental
object1 cognitive content1 content2 body
substance1 natural object1 social
control1 place of business1 business
establishment1 change of magnitude1 plant
organ1 contractile organ1 plant
part1 psychological feature1 spatial
property1 spatiality1
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Table 6 Local senses with complex equivalence
relations to CBCs NL ES IT Eq_has_hyperonym
61 40 4 eq_has_hyponym 34 14 20 Eq_has_holonym
2 0 Eq_has_meronym 3 2 Eq_involved 3 Eq
_is_caused_by 3 Eq_is_state_of 1 Example
of complex relation CBC cause to feel
unwell1, Verb Closest Dutch concept onwel1,
Adjective (sick) Equivalence relation
eq_is_caused_by
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EuroWordNet data
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From EuroWordNet to Global WordNet

• Currently, wordnets exist for more than 50
  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

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Global Wordnet Association
EuroWordNet
BalkaNet

• Arabic
• Polish
• Welsh
• Chinese
• 20 Indian Languages
• Brazilian Portuguese
• Hebrew
• Latvian
• Persian
• Kurdish
• Avestan
• Baluchi
• Hungarian

• Danish
• Norway
• Swedish
• Portuguese
• Korean
• Russian
• Basque
• Catalan
• Thai

• Romanian
• Bulgarian
• Turkish
• Slovenian
• Greek
• Serbian

• English
• German
• Spanish
• French
• Italian
• Dutch
• Czech
• Estonian

http//www.globalwordnet.org
64
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

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Next step Global WordNet Grid
Inter-Lingual Ontology
voertuig
1
auto
trein
Object
2
liiklusvahend
Dutch Words
1
Device
auto
killavoor
TransportDevice
2
véhicule
Estonian Words
1
voiture
train
2
dopravní prostredník
French Words
1
auto
vlak
2
Czech Words
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GWNG Main Features

• Construct separate wordnets for each Grid
  language
• Contributors from each language encode the same
  core set of concepts plus culture/language-specifi
  c ones
• Synsets (concepts) can be mapped
  crosslinguistically via an ontology

67
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
• Concepts are related in a type hierarchy
• Concepts are defined with axioms

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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

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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 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

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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.

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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)
• Whats 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.

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Ontology and lexicon

• Hierarchy of disjunct types
• Canine ? PoodleDog NewfoundlandDog
  GermanShepherdDog Husky
• Lexicon
• NAMES for TYPES
• poodleEN, poedelNL, pudoruJP
• ((instance x Poodle)
• LABELS for ROLES
• watchdogEN, waakhondNL, bankenJP
• ?((instance x Canine) and (role x
  GuardingProcess))

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Ontology and lexicon

• Hierarchy of disjunct types
• River Clay etc
• Lexicon
• NAMES for TYPES
• riverEN, rivier, stroomNL
• ((instance x River)
• LABELS for dependent concepts
• rivierwaterNL (water from a river gt water is
  not a unit)
• kleibrokNL (irregularly shared piece of
  claygtnon-essential)
• ?((instance x water) and (instance y River) and
  (portion x y)
• ?((instance x Object) and (instance y Clay) and
  (portion x y) and (shape X Irregular))

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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

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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

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Mapping Grid Languages onto the Ontology

• Explicit and precise equivalence relations among
  synsets in different languages
• 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!

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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
• Copy the relation 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

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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

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SUMO

• 1,000 generic, abstract, high-level terms
• 4,000 definitional statements
• MILO (Mid-Level Ontology)
• closer to lexicon, WordNet

80
Mapping Grid languages onto the Ontology

• Check existing SUMO mappings to Princeton WordNet
  -gt 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)

81
Lexicalizations not mapped to WordNet

• Not added to the type hierarchy
• straathondNL (a dog that lives in the streets)
• ((instance x Canine) and (habitat x Street))
• Added to the type hierarchy
• klunenNL (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,....

82
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 rivierwaterNL there are many other
• theewaterNL (water used for making tea)
• koffiewaterNL (water used for making coffee)
• bluswaterNL (water used for making
  extinguishing file)
• Relate to linguistic phenomena
• gender, perspective, aspect, diminutives,
  politeness, pejoratives, part-of-speech
  constraints

83
KIF expression for gender marking

• teacherEN
• ?((instance x Human) and (agent x
  TeachingProcess))
• LehrerDE ?((instance x Man) and (agent x
  TeachingProcess))
• LehrerinDE ?((instance x Woman) and (agent x
  TeachingProcess))

84
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

85
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
• Does NOT warrant the extension of the ontology
  with separate processes for each aspectual variant

86
Kinship relations in Arabic

• ???(Eam) father's brother, paternal uncle.
• ???? (xaAl) mother's brother, maternal uncle.
• ?????? (Eamap) father's sister, paternal aunt.
• ?????? (xaAlap) mother's sister, maternal aunt

87
Kinship relations in Arabic

• .........
• ???????? (aqiyqapfull) sister, sister on the
  paternal and maternal side (as distinct from
  ????? (gtuxot) 'sister' which may refer to a
  'sister' from paternal or maternal side, or both
  sides).
• ??????? (vakolAna) father bereaved of a child (as
  opposed to ?????? (yatiym) or ????????
  (yatiymap) for feminine 'orphan' a person whose
  father or mother died or both father and mother
  died).
• ??????? (vakolaYa) other bereaved of a child (as
  opposed to ?????? or ???????? for feminine
  'orphan' a person whose father or mother died or
  both father and mother died).

88
Complex Kinship concepts

• father's brother, paternal uncle
• WORDNET
• paternal uncle gt uncle
• gt brother of ....????
• ONTOLOGY
• (gt
• (paternalUncle ?P ?UNC)
• (exists (?F)
• (and
• (father ?P ?F)
• (brother ?F ?UNC))))

89
Universality as evidence

• English verb cut abstracts from the precise
  process but there are troponyms that implicate
  the manner
• snip, clip imply scissors, chop and hack a large
  knife or an axe
• Dutch there is no general verb but only specific
  verbs
• knippen clip, snip, cut with scissors or a
  scissor-like tool', snijden cut with a knife or
  knife-like tool, hakken chop, hack, to cut with
  an axe, or similar tool).
• If lexicalization of the specific process is more
  universal it can be seen as evidence that the
  specific processes should be listed in the
  ontology and not the generic verb

90
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?

91
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?

92
Coverage what belongs in a universal lexical
database?

• Formal, linguistic criteria for inclusion
• Informal, cultural criteria
• Both are difficult to define and apply!

93
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

94
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
95
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
96
Automotive ontology (http//www.ontoprise.de)
97
Who uses ontologies?
98
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