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

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
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

4 Relational model of meaning
animal kitten animal man boy man woman cat kitten dog puppy cat meisje boy girl dog puppy woman

5 Wordnet: a network of semantically related words
{conveyance;transport} {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}

6 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-combination Example ANTONYMY adjective-to-adjective 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

7 Vocabulary of a language
Wordnet Data Model Relations Concepts Vocabulary of a language 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

8 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 -> 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

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

10 Wordnet top level

11 Meronymy & pictures beak tail leg

12 Meronymy & pictures

13 Co-reference constraint in wordnet: Cats 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)


15 Wordnet 3.0 statistics POS Unique Synsets Total Strings
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

16 Wordnet 3.0 statistics POS Monosemous Polysemous Words and Senses
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

17 Including Monosemous Words Excluding Monosemous Words
Wordnet 3.0 statistics POS Average Polysemy Including Monosemous Words Excluding Monosemous Words Noun 1.24 2.79 Verb 2.17 3.57 Adjective 1.4 2.71 Adverb 1.25 2.5



20 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

21 Improve recall Information retrieval: Text classification:
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

22 Improve recall Anaphora resolution: Coreference 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"

23 Improve recall Summarizers:
Sentence selection based on word counts -> concept counts Avoid repetition in summary -> language generation Limited inferencing: detect locations, organisations, etc.

24 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

25 Recall & Precision Recall < 20% for basic search engines!
“jail” “nerve cell” “police cell” “cell phone” “mobile phones” “neuron” found intersection relevant query: “cell” Recall < 20% for basic search engines! (Blair & Maron 1985)‏ recall = doorsnede / relevant precision = doorsnede / gevonden

26 EuroWordNet The development of a multilingual database with wordnets for several European languages Funded by the European Commission, DG XIII, Luxembourg as projects LE and LE4-8328 March September 1999 2.5 Million EURO.

27 EuroWordNet Languages covered: Size of vocabulary: Type of vocabulary:
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

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

29 EuroWordNet Design Domains SUMO Inter-Lingual-Index ENGLISH Car …
Transport Road Air Water Domains DOLCE SUMO Device Object TransportDevice Czech Words dopravní prostředník auto vlak 2 1 French Words véhicule voiture train Estonian Words liiklusvahend killavoor German Words Fahrzeug Auto Zug Spanish Words vehículo auto tren 2 1 Italian Words veicolo treno Dutch Words voertuig trein English Words vehicle car train 1 2 4 3 ENGLISH Car Train Vehicle Inter-Lingual-Index

30 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

31 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.

32 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_PART: 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

33 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

34 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}

35 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.

36 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

37 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 photo

38 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 to slurp - noisily BELONG_TO_CLASS Rome - city

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

40 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;

41 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

42 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 <=> artifact object

43 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.

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

45 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 fish): eq_hyperonym {fish} and eq_be_in_state {young} cajera (female cashier)

46 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

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

48 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

49 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,

50 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.

51 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

52 Wordnet: Domain information
Vocabularies of languages Concepts Domains Music Culture Finance Clothing Sport Ball sports Winter Relations 1 rec: 12345 financial institute 2 rec: 54321 - river side bank 1 rec: 9876 - small string instrument violin 2 rec: 65438 - musician playing a violin violist rec:42654 - musician type-of 1 rec:35576 - string of an instrument type-of part-of string 2 rec:29551 - underwear rec:25876 - string instrument

53 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

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

55 Base Concept Intersection
Nouns Verbs Intersection EN, NL, IT, ES 24 6 Intersection FR, DE, EE, CZ 70 30 Intersection All 13 2 {human 1; individual#1; mortal#1; person#1; someone#1; soul#1} {animal 1; animate being#1; beast#1; brute#1; creature#1; fauna#1} {flora 1; plant#1; plant life#1} {matter 1; substance#1} {food 1; nutrient#1} {feeling 1} {act 1; human action#1; human activity#1} {cause 6; get#9; have#7; induce#2; make#12; stimulate#3} {create 2; make#13} {go 14; locomote#1; move#15; travel#4} {be 4; have the quality of being#1}

56 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

57 Concepts selected by at least two languages: intersections of pairs
NOUNS  VERBS    NL ES IT EN 1027 103 182 333 323 36 42 86 523 45 284 128 18 43 334 167 104 39 1296 236

58 Common Base Concepts Nouns Verbs Total Physical objects & substances
Nouns Verbs Total Physical objects & substances 491 Processes and states 272 228 500 Mental objects 33 796 1024

59 Table 4: Number of Common BCs represented in the local wordnets
Related to CBCs Eq_synonym Eq_near CBCs Without Direct Equivalent NL ES IT Table 5: BC4 Gaps in at least two wordnets (10 synsets) body covering#1 mental object#1; cognitive content#1; content#2 body substance#1 natural object#1 social control#1 place of business#1; business establishment#1 change of magnitude#1 plant organ#1 contractile organ#1 plant part#1 psychological feature#1 spatial property#1; spatiality#1

60 Table 6: Local senses with complex equivalence relations to CBCs
NL ES IT Eq_has_hyperonym eq_has_hyponym 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 unwell#1, Verb Closest Dutch concept: {onwel#1}, Adjective (sick) Equivalence relation: eq_is_caused_by

61 EuroWordNet data

62 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

63 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 English German Spanish French Italian Dutch Czech Estonian Romanian Bulgarian Turkish Slovenian Greek Serbian

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

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

66 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

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

68 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

69 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

70 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.

71 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….

72 Ontology and lexicon Hierarchy of disjunct types: Lexicon:
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))

73 Ontology and lexicon Hierarchy of disjunct types: Lexicon:
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 a unit) {kleibrok}NL (irregularly shared piece of clay=>non-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))

74 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

75 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

76 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!

77 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

78 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

79 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 -> 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: {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,....

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 {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 making extinguishing file) Relate to linguistic phenomena: gender, perspective, aspect, diminutives, politeness, pejoratives, part-of-speech constraints

83 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))

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
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

87 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).

88 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))))

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: (

97 Who uses ontologies?


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