Semantic relations - PowerPoint

					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            dog         cat
                                     meisje

   boy         girl        kitten         puppy
                                                            dog
      puppy
                         woman
                            Structure of WordNet
                                  {conveyance; transport}

                                               hyperonym

                                        {vehicle}

                                               hyperonym                              {bumper}           {hinge; flexible joint}
                        {motor vehicle; automotive vehicle}                                          meronym
                                                                                      {car door}         {doorlock}
                                                                      meronym                        meronym
                                               hyperonym

                        {car; auto; automobile; machine; motorcar}
                                                                                      {car window}       {armrest}
                                                                      meronym
                                                                                      {car mirror}
                                   hyperonym     hyperonym
{cruiser; squad car; patrol car; police car; prowl car}      {cab; taxi; hack; taxicab; }
      Wordnet Data Model
Relations       Concepts                 Vocabulary of a language
            rec: 12345               1
            - financial institute                bank
            rec: 54321               2
            - side of a river
            rec: 9876                    1       fiddle
            - small string instrument            violin
 type-of    rec: 65438                   2
                                                 fiddler
            - musician playing violin            violist
            rec:42654
type-of     - musician
            rec:35576                1
part-of
            - string of instrument               string
            rec:29551                2
            - underwear
            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:
  – 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:
   – 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:
   – 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):
                                                       Global Wordnet 2004): 1998): 8 languages
                                                       EuroWordNet, (Vossen 6 languages
                                                       BalkaNet, (Tufis Association: all languages
                                                       115,000 conceps
                                     Domains              SUMO DOLCE
                                                                                                      Fahrzeug
                                         Transport                 Object                                     1

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

                                          2
                                       Czech Words
                   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
                object                                  voorwerp
                                                        {object}
artifact, artefact         natural object (an
(a man-made object)        object occurring
                           naturally)         blok   werktuig{tool}       lichaam
block         instrumentality         body {block}                        {body}

 implement                      device
               container
tool                             instrument     bak     lepel          tas
       box      spoon     bag                   {box}   {spoon}       {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
• 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
• 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
                                                                                       Fahrzeug
                                                                                               1
                                                                                       Auto Zug
                                             Inter-Lingual            voertuig

                vehicle
                                               Ontology                      1
                                                                     auto trein
                                                                                          2
                                                                                   German Words
                          1
              car     train                         Object                  2
                                                                    Dutch Words                 liiklusvahend
                   2                                                                        1
                                                    Device
         English Words                                                                       auto killavoor
                                         3    TransportDevice   3
   vehículo                                                                                 2
         1
                                                                           véhicule        Estonian Words
  auto tren                                                                            1
                       veicolo                                           voiture   train
          2                      1
Spanish Words        auto treno                                                  2
                                      dopravní prostředník                French Words
                          2                    1
                    Italian Words    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:
      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:
      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                         • to happen
   – act                           – to act
       • deed                          • to do
           – sail                            – to sell
           – promise                         – a promise
   – change                        – to change
       • movement                      • to move
           – change of                       – to move position
             location
    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
•   ‫(عَم‬Eam~)        father's brother,
    paternal uncle.
•       ‫خ‬
    ‫( َال‬xaAl)       mother's brother,
    maternal uncle.
•     ‫َّم‬
    ‫( ع َة‬Eam~ap) father's sister, paternal
    aunt.
•      ‫خل‬
     ‫( َاَة‬xaAlap) mother's sister, maternal
    aunt
     Kinship relations in Arabic
•   .........
•    ‫قق‬
    ‫$( شَ ِي َة‬aqiyqapfull) sister, sister on the paternal and
                                             ‫أ‬
    maternal side (as distinct from ‫>( ُخْت‬uxot): 'sister'
    which may refer to a 'sister' from paternal or maternal
    side, or both sides).
•   ‫( ثَكْالن‬vakolAna)       father bereaved of a child (as
                     ‫يت‬                 ‫ي ّم‬
    opposed to ‫( َ ِيم‬yatiym) or ‫( َتِي َة‬yatiymap) for
    feminine: 'orphan' a person whose father or mother died
    or both father and mother died).
•       ْ‫ث‬
    ‫( َكلَى‬vakolaYa)         other bereaved of a child (as
                           ‫ّم‬
    opposed to ‫ يَتِيم‬or ‫ يَتِي َة‬for 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   long hair
                          dachshund    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    long hair
                         dachshund     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!
                                                  Thought
        Objects
        in reality



               Ontology


                                 携帯電話
                                 (keitaidenwa )


Texts
         Knowledge &                         Expression
         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                                         Environmental
            organizations                                         organizations




 Domain
  Wiki
                                                       Capture
          Universal Ontology       Wordnets
                                             Concept
                                               Mining             Docs             Dialogue
   Top
             Abstract Physical
                                               Fact
                                                                                  Search
                                              Mining             URLs
             Process   Substance
 Middle
                       water CO2                                 Experts
                                                        Index

Domain water CO2                                                 Images
       pollution emission
                                        wordnet      ontology


                                domain                           domain
                                wordnet                         ontology
    4
         Wiki                                                                       User                  Bench
         DEB                                     DEB                              scenarios               mark
         Client                                 Server                                                     data

                                                                                             7                 8
                      term
                                                                                       Manual
                    hierarchy
                                                                                        Test
        Manual                        Concept
        Revision                       Miners
                      term
                    relations               3                                       Access                Bench
                                                                                   end-users              marking


1     User
    scenarios




        source           Text & Meta data                               Data & Facts
1                                                                                                 Index
         data             in XMLFormat                                 in XML Format



                   Capture                               Kybots                        Indexing
                        2                           5                             6
                       Ontology         Logical Expressions   Wordnets    Linguistic Miners
                                                                          or Kybots
                          
                                             Generic
             Abstract      Physical
                                                                         words       words
     Process               Substance

    Chemical            water     CO2
    Reaction
                                              Domain

CO2        water                                                         words       words
emission   pollution
END

				
DOCUMENT INFO