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

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

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

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

Structure of WordNet

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

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

Improve recall 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

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"

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

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

EuroWordNet 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

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

Wordnet family Princeton WordNet, (Fellbaum 1998): 115,000 conceps BalkaNet, (Tufis 2004): 6 languages EuroWordNet, (Vossen 1998): 8 languages Global Wordnet Association: all languages 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

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

Autonomous & Language-Specific 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}

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

Linguistic versus Artificial Ontologies Linguistic ontology: Exactly reflects the relations between all the lexicalized words and expressions in a language. Captures valuable information about the lexical capacity of languages: what is the available fund of words and expressions in a language. What words can be used to name spoons? spoon -> object, tableware, silverware, merchandise, cutlery,

Wordnets versus ontologies autonomous language-specific lexicalization patterns in a relational network. Usage: to predict substitution in text for information retrieval, text generation, machine translation, word-sense-disambiguation. Ontologies: data structure with formally defined concepts. Usage: making semantic inferences.

The Multilingual Design Inter-Lingual-Index: unstructured fund of concepts to provide an efficient mapping across the languages; Index-records are mainly based on WordNet synsets and consist of synonyms, glosses and source references; Various types of complex equivalence relations are distinguished; Equivalence relations from synsets to index records: not on a word-to-word basis; Indirect matching of synsets linked to the same index items;

Equivalent Near Synonym 1. Multiple Targets (1:many) Dutch wordnet: schoonmaken (to clean) matches with 4 senses of clean in WordNet1.5: make clean by removing dirt, filth, or unwanted substances from remove unwanted substances from, such as feathers or pits, as of chickens or fruit remove in making clean; "Clean the spots off the rug" remove unwanted substances from - (as in chemistry) 2. Multiple Sources (many:1) Dutch wordnet: versiersel near_synonym versiering ILI-Record: decoration. 3. Multiple Targets and Sources (many:many) Dutch wordnet: toestel near_synonym apparaat ILI-records: machine; device; apparatus; tool

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

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

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

Next step: Global WordNet Grid 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

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

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

The Ontology: Main Features In addition to high-level (“primitive”) concept ontology needs to express low-level concepts lexicalized in the Grid languages Additional concepts can be defined with expressions in Knowledge Interchange Format (KIF) based on first order predicate calculus and atomic element

The Ontology: Main Features Minimal set of concepts (Reductionist view): to express equivalence across languages to support inferencing Ontology must be powerful enough to encode all concepts that are lexically expressed in any of the Grid languages

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

Ontological observations Identity criteria as used in OntoClean (Guarino & Welty 2002), : rigidity: to what extent are properties true for entities in all worlds? You are always a human, but you can be a student for a short while. essence: what properties are essential for an entity? Shape is essential for a statue but not for the clay it is made of. unicity: what represents a whole and what entities are parts of these wholes? An ocean is a whole but the water it contains is not.

Type-role distinction Current WordNet treatment: (1) a husky is a kind of dog(type) (2) a husky is a kind of working dog (role) What’s wrong? (2) is defeasible, (1) is not: *This husky is not a dog This husky is not a working dog Other roles: watchdog, sheepdog, herding dog, lapdog, etc….

Ontology and lexicon 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))

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 Unit) ((instance x water) and (instance y River) and (portion x y) {kleibrok}NL (irregularly shared piece of clay=>Non-essential) ((instance x Object) and (instance y Clay) and (portion x y) and (shape X Irregular))

Rigidity The “primitive” concepts represented in the ontology are rigid types Entities with non-rigid properties will be represented with KIF statements But: ontology may include some universal, core concepts referring to roles like father, mother

Properties of the Ontology Minimal: terms are distinguished by essential properties only Comprehensive: includes all distinct concepts types of all Grid languages Allows definitions via KIF of all lexemes that express non-rigid, non-essential properties of types Logically valid, allows inferencing

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

How to construct the GWNG Take an existing ontology as starting point; Use English WordNet to maximize the number of disjunct types in the ontology; Link English WordNet synsets as names to the disjunct types; Provide KIF expressions for all other English words and synsets

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

Initial Ontology: SUMO (Niles and Pease) SUMO = Suggested Upper Merged Ontology --consistent with good ontological practice --fully mapped to WordNet(s): 1000 equivalence mappings, the rest through subsumption --freely and publicly available --allows data interoperability --allows NLP --allows reasoning/inferencing

Mapping Grid languages onto the Ontology Check existing SUMO mappings to Princeton WordNet -> extend the ontology with rigid types for specific concepts Extend it to many other WordNet synsets Observe OntoClean principles! (Synsets referring to non-rigid, non-essential, non-unicitous concepts must be expressed in KIF)

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

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

KIF expression for gender marking {teacher}EN ((instance x Human) and (agent x TeachingProcess)) {Lehrer}DE ((instance x Man) and (agent x TeachingProcess)) {Lehrerin}DE ((instance x Woman) and (agent x TeachingProcess))

KIF expression for perspective sell: subj(x), direct obj(z),indirect obj(y) versus buy: subj(y), direct obj(z),indirect obj(x) (and (instance x Human)(instance y Human) (instance z Entity) (instance e FinancialTransaction) (source x e) (destination y e) (patient e) The same process but a different perspective by subject and object realization: marry in Russian two verbs, apprendre in French can mean teach and learn

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

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

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

Aspectual lexicalization 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))

Lexicalization of Resultatives MORE GENERAL VERBS: openmaken: (lit. open+make, to cause to be open); dichtmaken: (lit. close+make, to cause to be open); MORE SPECIFIC VERBS: openknijpen (lit. open+squeeze, to open by squeezing) has_hyperonym knijpen (squeeze) & openmaken (to open) opendraaien (lit. open+turn, to open by turning) has_hyperonym draaien (to turn) & openmaken (to open) dichtknijpen: (lit. closed+squeeze, to close by squeezing) has_hyperonym knijpen (squeeze) & dichtmaken (to close) dichtdraaien: (lit. closed +turn, to close by turning) has_hyperonym draaien (to turn) & dichtmaken (to close)

Kinship relations in Arabic عَم(Eam~) father's brother, paternal uncle. خَال (xaAl) mother's brother, maternal uncle. عَمَّة (Eam~ap) father's sister, paternal aunt. خَالَة (xaAlap) mother's sister, maternal aunt

Kinship relations in Arabic ......... شَقِيقَة ($aqiyqapfull) sister, sister on the paternal and maternal side (as distinct from أُخْت (>uxot): 'sister' which may refer to a 'sister' from paternal or maternal side, or both sides). ثَكْلان (vakolAna) father bereaved of a child (as opposed to يَتِيم (yatiym) or يَتِيمَة (yatiymap) for feminine: 'orphan' a person whose father or mother died or both father and mother died). ثَكْلَى (vakolaYa) other bereaved of a child (as opposed to يَتِيم or يَتِيمَة for feminine: 'orphan' a person whose father or mother died or both father and mother died).

Complex Kinship concepts father's brother, paternal uncle WORDNET paternal uncle => uncle => brother of ....???? ONTOLOGY (=> (paternalUncle ?P ?UNC) (exists (?F) (and (father ?P ?F) (brother ?F ?UNC))))

Advantages of the Global Wordnet Grid Shared and uniform world knowledge: universal inferencing uniform text analysis and interpretation More compact and less redundant databases More clear notion how languages map to the knowledge better criteria for expressing knowledge better criteria for understanding variation

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

Expansion with pure hyponymy 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

Automotive ontology: (http://www.ontoprise.de)

Who uses ontologies?

Human dialogues with Alice-bot

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

携帯電話 Texts Useful and effective behavior: Thought Objects in reality Ontology Expression 携帯電話 (keitaidenwa ) Texts Knowledge & information Useful and effective behavior: reason over knowledge collect information and data deliver services and be helpful

Concrete goals for GWG Global Wordnet Association website: http://www.globalwordnet.org/gwa/gwa_grid.htm 5000 Base Concepts or more: English Spanish Catalan Czech, Polish, Dutch, other wordnets 7th Frame Work project Kyoto

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

KYOTO Consortium Universities Vrije Universiteit Amterdam, Amsterdam, Netherlands Consiglio Nazionale delle Ricerche, Pisa, Italy Berlin-Brandenburg Academy of Sciences and Humantities, Berlin, Germany Euskal Herriko Unibertsitatea, San Sebastian, Spain Academia Sinica, Taipei, Taiwan National Institute of Information and Communications Technology, Kyoto, Japan Masaryk University, Brno, Czech Companies Irion Technologies, Delft, Netherlands Synthema, Pisa, Italy Users European Centre for Nature Conservation, Tilburg, Netherlands World Wide Fund for Nature, Zeist, Netherlands

 Citizens Governors Companies Environmental organizations Wiki Capture Index Docs URLs Experts Images Search Dialogue Concept Mining Fact  Abstract Physical Top Middle Domain water CO2 Substance emission pollution Universal Ontology Wordnets Citizens Governors Companies Wiki Process

2 3 5 6 1 4 7 8 Text & Meta data in XMLFormat term hierarchy wordnet Concept Miners relations ontology Kybots Manual Revision Wiki DEB Client 2 3 5 domain Indexing source data Capture Data & Facts in XML Format Server Access end-users Index 6 User scenarios Test Bench mark marking 1 4 7 8

 Ontology Wordnets Logical Expressions Linguistic Miners or Kybots Abstract Physical water CO2 Substance emission pollution Ontology Wordnets Generic Process Chemical Reaction Logical Expressions Linguistic Miners or Kybots Domain words

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