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The Global Wordnet Grid: anchoring languages to universal meaning. Piek Vossen Irion Technologies/Free University of Amsterdam and Christiane Fellbaum Princeton University. Overview. Wordnet, EuroWordNet background Architecture of the Global Wordnet Grid Mapping wordnets to the Grid

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The global wordnet grid anchoring languages to universal meaning l.jpg
The Global Wordnet Grid: anchoring languages to universal meaning

Piek Vossen

Irion Technologies/Free University of Amsterdam

and

Christiane Fellbaum

Princeton University


Overview l.jpg
Overview meaning

  • Wordnet, EuroWordNet background

  • Architecture of the Global Wordnet Grid

  • Mapping wordnets to the Grid

  • Kyoto: an implementation of the Grid


Wordnet1 5 l.jpg
WordNet1.5 meaning

  • Developed at Princeton by George Miller and his team as a model of the mental lexicon.

  • 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



  • Eurowordnet l.jpg
    EuroWordNet meaning

    • 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 architecture l.jpg

    Domains meaning

    Transport

    Road

    Water

    Air

    vehicle

    1

    car

    train

    2

    English Words

    3

    3

    EuroWordnet architecture

    Top Ontology

    Fahrzeug

    1

    Object

    Auto

    Zug

    voertuig

    Device

    1

    2

    auto

    trein

    TransportDevice

    German Words

    4

    2

    liiklusvahend

    Dutch Words

    ENGLISH

    Car

    Train

    Vehicle

    1

    auto

    killavoor

    vehículo

    2

    1

    Estonian Words

    véhicule

    auto

    tren

    1

    veicolo

    voiture

    train

    1

    2

    Inter-Lingual-Index

    Spanish Words

    auto

    treno

    2

    dopravníprostředník

    French Words

    2

    1

    Italian Words

    auto

    vlak

    2

    Czech Words


    Eurowordnet7 l.jpg
    EuroWordNet meaning

    • 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 l.jpg

    object meaning

    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}


    Linguistic versus artificial ontologies l.jpg
    Linguistic versus Artificial Ontologies meaning

    • 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 ontologies10 l.jpg
    Linguistic versus Artificial Ontologies meaning

    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 l.jpg
    Wordnets versus ontologies meaning

    • 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.


    The multilingual design l.jpg
    The Multilingual Design meaning

    • 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 l.jpg
    Equivalent Near Synonym meaning

    • 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 l.jpg
    Equivalent Hyperonymy meaning

    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

      • Dutch:citroenjenever, which is a kind of gin made out of lemon skin,

    • 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

      • Dutch: hoofd = human head and Dutch: kop = animal head, English uses head for both.


    From eurowordnet to global wordnet l.jpg
    From EuroWordNet to Global WordNet meaning

    • 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 l.jpg
    Some downsides meaning

    • 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 l.jpg

    Fahrzeug meaning

    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

    dopravníprostředník

    French Words

    1

    auto

    vlak

    2

    Czech Words


    Gwng main features l.jpg
    GWNG: Main Features meaning

    • 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 l.jpg
    The Ontology: Main Features meaning

    • 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 features20 l.jpg
    The Ontology: Main Features meaning

    • 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 features21 l.jpg
    The Ontology: Main Features meaning

    • 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 features22 l.jpg
    The Ontology: Main Features meaning

    • 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 l.jpg
    Ontological observations meaning

    • 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 l.jpg
    Type-role distinction meaning

    • 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 l.jpg
    Ontology and lexicon meaning

    • 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))


    Ontology and lexicon26 l.jpg
    Ontology and lexicon meaning

    • 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))


    Rigidity l.jpg
    Rigidity meaning

    • 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 l.jpg
    Properties of the Ontology meaning

    • 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 l.jpg
    Mapping Grid Languages onto the Ontology meaning

    • 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 l.jpg
    How to construct the GWNG meaning

    • 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 gwng31 l.jpg
    How to construct the GWNG meaning

    • 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 l.jpg
    Initial Ontology: SUMO meaning(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 ontology33 l.jpg
    Mapping Grid languages onto the Ontology meaning

    • 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 l.jpg
    Lexicalizations not mapped to WordNet meaning

    • 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 l.jpg
    Most mismatching concepts are not new types meaning

    • 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 l.jpg
    KIF expression for gender marking meaning

    • {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 l.jpg
    KIF expression for perspective meaning

    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


    Part of speech mismatches l.jpg
    Part-of-speech mismatches meaning

    • {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!


    Parallel noun and verb hierarchy l.jpg
    Parallel Noun and Verb hierarchy meaning

    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


    Mixed noun and adjective hierarchy l.jpg
    Mixed Noun and Adjective hierarchy meaning

    • Colour: red, blue, green, etc.

    • Height: high, low

    • Size: big, small

    • Emotion: sad, angry, happy, anxious

    • etc.

      Encoded once as a attributes in the ontology!


    Aspectual variants l.jpg
    Aspectual variants meaning

    • 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 l.jpg
    Aspectual lexicalization meaning

    • 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))


    Slide43 l.jpg

    Lexicalization of Resultatives meaning

    • 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 l.jpg
    Kinship relations in Arabic meaning

    • 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 arabic45 l.jpg
    Kinship relations in Arabic meaning

    • .........

    • 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 l.jpg
    Complex Kinship concepts meaning

    father's brother, paternal uncle

    WORDNET

    paternal uncle => uncle

    => brother of ....????

    ONTOLOGY

    (=>

    (paternalUncle ?P ?UNC)

    (exists (?F)

    (and

    (father ?P ?F)

    (brother ?F ?UNC))))


    Fine tune equivalence relations l.jpg
    Fine tune equivalence relations meaning

    • {rivier}NL  (and (instance x River) (instance y RiverMouth) (instance z Country) (part y x) (location y z)

    • {stroom}NL  (and (instance x River) (instance y RiverMouth) (instance p RiverPart) (not (equal p y) (instance z Country) (location p z) (not (location y z))


    Universality as evidence l.jpg
    Universality as evidence meaning

    • 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:

      • 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 Father is lexicalized in most languages we add it to the ontology even when it is NOT Rigid!


    Universality as evidence49 l.jpg
    Universality as evidence meaning

    • Artifact substance is lexicalized in Dutch and other languages => ArtifactObject in SUMO needs to be generalized to Artifact so that it can be applied to both substances and objects


    Open questions challenges l.jpg
    Open Questions/Challenges meaning

    • 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 the status of compounds in Germanic languages and Chinese?

      • "hottentottententententoonstelling"

        (exposition of tents of the "hottentotten" (African tribe))

    • What is a semantic unit, i.e. a concept?


    Open questions challenges51 l.jpg
    Open Questions/Challenges meaning

    • 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?


    Coverage what belongs in a universal lexical database l.jpg
    Coverage: what belongs in a universal lexical database? meaning

    • Formal, linguistic criteria for inclusion

    • Informal, cultural criteria

    • Both are difficult to define and apply!


    Concrete goals for gwg l.jpg
    Concrete goals for GWG meaning

    • 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 l.jpg
    KYOTO Project meaning

    • 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 l.jpg
    KYOTO Consortium meaning

    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


    Slide56 l.jpg

    Citizens meaning

    Governors

    Companies

    Environmental

    organizations

    Environmental

    organizations

    Domain

    Wiki

    Capture

    Universal Ontology

    Wordnets

    Concept

    Mining

    Docs

    Dialogue

    Top

    Abstract

    Physical

    Fact

    Mining

    Search

    URLs

    Process

    Substance

    Experts

    Middle

    water

    CO2

    Index

    Images

    water

    pollution

    CO2

    emission

    Domain


    Slide57 l.jpg

    wordnet meaning

    ontology

    domain

    ontology

    domain

    wordnet

    4

    Bench

    mark

    data

    User

    scenarios

    Wiki

    DEB

    Client

    DEB

    Server

    7

    8

    term

    hierarchy

    Manual

    Test

    Manual

    Revision

    Concept

    Miners

    term

    relations

    3

    Access

    end-users

    Bench

    marking

    User

    scenarios

    1

    source

    data

    Text & Meta data

    in XMLFormat

    Data & Facts

    in XML Format

    Index

    1

    Kybots

    Indexing

    Capture

    2

    5

    6


    Slide58 l.jpg

    Ontology meaning

    Logical Expressions

    Wordnets

    Linguistic Miners

    or Kybots

    Generic

    Abstract

    Physical

    words

    words

    Substance

    Process

    Chemical

    Reaction

    water

    CO2

    Domain

    CO2

    emission

    water

    pollution

    words

    words


    Slide59 l.jpg

    END meaning


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