The Global Wordnet Grid: anchoring languages to universal meaning

1. The Global Wordnet Grid: anchoring languages to universal meaning Piek Vossen Irion Technologies/Free University of Amsterdam

2. Overview • Wordnet, EuroWordNet background • Architecture of the Global Wordnet Grid • Mapping wordnets to the Grid • Advantages of shared knowledge structure • 7th Frame work project KYOTO

3. WordNet1.5 • Semantic network in which concepts are defined in terms of relations to other concepts. • Structure: • organized around the notion of synsets (sets of synonymous words) • basic semantic relations between these synsets • http://www.cogsci.princeton.edu/~wn/w3wn.html • Developed at Princeton by George Miller and his team as a model of the mental lexicon.

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

5. Structure of WordNet

6. Wordnet Data Model Vocabulary of a language Relations Concepts • rec: 12345 • financial institute 1 bank rec: 54321 - side of a river 2 rec: 9876 - small string instrument 1 fiddle violin type-of rec: 65438 - musician playing violin 2 fiddler violist rec:42654 - musician type-of rec:35576 - string of instrument 1 part-of string rec:29551 - underwear 2 rec:25876 - string instrument

7. 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

8. 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

9. Improve recall • Anaphora resolution: • The girl fell off the table. She.... • The glass fell of the table. It... • Coreference resolution: • When he moved the furniture, the antique table got damaged. • Information extraction (unstructed text to structured databases): • generic forms or patterns "vehicle" - > text with specific cases "car"

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

11. Many others • Data sparseness for machine learning: hapaxes can be replaced by semantic classes • Use redundancy for more robustness: spelling correction and speech recognition can built semantic expections using Wordnet and make better choices • Sentiment and opinion mining • Natural language learning

13. EuroWordNet • The development of a multilingual database with wordnets for several European languages • Funded by the European Commission, DG XIII, Luxembourg as projects LE2-4003 and LE4-8328 • March 1996 - September 1999 • 2.5 Million EURO. • http://www.hum.uva.nl/~ewn • http://www.illc.uva.nl/EuroWordNet/finalresults-ewn.html

14. 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

15. Domains SUMO DOLCE Fahrzeug Object Transport 1 Auto Zug Device voertuig Road Water Air 1 2 vehicle TransportDevice auto trein German Words 1 4 car train 2 ENGLISH Car … Train … Vehicle Dutch Words liiklusvahend 2 1 English Words auto killavoor 3 3 vehículo 2 1 Estonian Words véhicule auto tren 1 veicolo voiture train 1 2 Inter-Lingual-Index auto treno Spanish Words 2 dopravníprostředník French Words 2 Italian Words 1 auto vlak 2 Czech Words Princeton WordNet, (Fellbaum 1998): 115,000 conceps EuroWordNet, (Vossen 1998): 8 languages BalkaNet, (Tufis 2004): 6 languages Global Wordnet Association: all languages Wordnet family

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

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

18. 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

19. 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,

20. 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.

21. 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;

22. 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

23. Equivalent Hyperonymy Typically used for gaps in English WordNet: • genuine, cultural gaps for things not known in English culture: • Dutch: klunen, to walk on skates over land from one frozen water to the other • pragmatic, in the sense that the concept is known but is not expressed by a single lexicalized form in English: • Dutch: kunstproduct = artifact substance <=> artifact object

24. From EuroWordNet to Global WordNet • Currently, wordnets exist for more than 40 languages, including: • Arabic, Bantu, Basque, Chinese, Bulgarian, Estonian, Hebrew, Icelandic, Japanese, Kannada, Korean, Latvian, Nepali, Persian, Romanian, Sanskrit, Tamil, Thai, Turkish, Zulu... • Many languages are genetically and typologically unrelated • http://www.globalwordnet.org

25. Some downsides • Construction is not done uniformly • Coverage differs • Not all wordnets can communicate with one another • Proprietary rights restrict free access and usage • A lot of semantics is duplicated • Complex and obscure equivalence relations due to linguistic differences between English and other languages

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

27. GWNG: Main Features • Construct separate wordnets for each Grid language • Contributors from each language encode the same core set of concepts plus culture/language-specific ones • Synsets (concepts) can be mapped crosslinguistically via an ontology • No license constraints, freely available

28. The Ontology: Main Features • Formal, artificial ontology serves as universal index of concepts • List of concepts is not just based on the lexicon of a particular language (unlike in EuroWordNet) but uses ontological observations • Concepts are related in a type hierarchy • Concepts are defined with axioms

29. 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

30. 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

31. The Ontology: Main Features • Ontology need not and cannot provide a linguistic encoding for all concepts found in the Grid languages • Lexicalization in a language is not sufficient to warrant inclusion in the ontology • Lexicalization in all or many languages may be sufficient • Ontological observations will be used to define the concepts in the ontology

32. 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.

33. 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….

34. 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))

35. Ontology and lexicon • Hierarchy of disjunct types: River; Clay; etc… • Lexicon: • NAMES for TYPES: {river}EN, {rivier, stroom}NL • ((instance x River) • LABELS for dependent concepts: {rivierwater}NL (water from a river => water is not Unit) ((instance x water) and (instance y River) and (portion x y) {kleibrok}NL (irregularly shared piece of clay=>Non-essential) ((instance x Object) and (instance y Clay) and (portion x y) and (shape X Irregular))

36. 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

37. 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

38. Mapping Grid Languages onto the Ontology • Explicit and precise equivalence relations among synsets in different languages, which is somehow easier: • type hierarchy is minimal • subtle differences can be encoded in KIF expressions • Grid database contains wordnets with synsets that label • either “primitive” types in the hierarchies, • or words relating to these types in ways made explicit in KIF expressions • If 2 lgs. create the same KIF expression, this is a statement of equivalence!

39. 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

40. How to construct the GWNG • Copy the relation from the English Wordnet to the ontology to other languages, including KIF statements built for English • Revise KIF statements to make the mapping more precise • Map all words and synsets that are and cannot be mapped to English WordNet to the ontology: • propose extensions to the type hierarchy • create KIF expressions for all non-rigid concepts

41. 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

42. 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)

43. Lexicalizations not mapped to WordNet • Not added to the type hierarchy: {straathond}NL (a dog that lives in the streets) • ((instance x Canine) and (habitat x Street)) • Added to the type hierarchy: {klunen}NL (to walk on skates from one frozen body to the next over land) KluunProcess => WalkProcess Axioms: (and (instance x Human) (instance y Walk) (instance z Skates) (wear x z) (instance s1 Skate) (instance s2 Skate) (before s1 y) (before y s2) etc… • National dishes, customs, games,....

44. Most mismatching concepts are not new types • Refer to sets of types in specific circumstances or to concept that are dependent on these types, next to {rivierwater}NL there are many others: {theewater}NL (water used for making tea) {koffiewater}NL (water used for making coffee) {bluswater}NL (water used for making extinguishing file) • Relate to linguistic phenomena: • gender, perspective, aspect, diminutives, politeness, pejoratives, part-of-speech constraints

45. 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))

46. 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

47. Parallel Noun and Verb hierarchy Encoded once as a Process in the ontology! • event • act • deed • sail • promise • change • movement • change of location • to happen • to act • to do • to sell • a promise • to change • to move • to move position

48. Part-of-speech mismatches • {bankdrukken-V}NL vs.{bench press-N}EN • {gehuil-N}NL vs. {cry-V}EN • {afsluiting-N}NL vs. {close-V}EN • Process in the ontology is neutral with respect to POS!

49. Aspectual variants • Slavic languages: two members of a verb pair for an ongoing event and a completed event. • English: can mark perfectivity with particles, as in the phrasal verbs eat up and read through. • Romance languages: mark aspect by verb conjugations on the same verb. • Dutch, verbs with marked aspect can be created by prefixing a verb with door: doorademen, dooreten, doorfietsen, doorlezen, doorpraten(continue to breathe/eat/bike/read/talk). • These verbs are restrictions on phases of the same process • Which does NOT warrant the extension of the ontology with separate processes for each aspectual variant

50. Aspectual lexicalization • Regular compositional verb structures: doorademen: (lit. through+breath, continue to breath) doorbetalen: (lit. through+pay, continue to pay) doorlopen: (lit. through+walk, continue to walk) doorfietsen: (lit. through+walk, continue to walk) doorrijden: (lit. through+walk, continue to walk) (and (instance x BreathProcess)(instance y Time) (instance z Time) (end x z) (expected (end x y) (after z y))