UNDL FOUNDATION

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UNLA Language for Computers
UNDL FOUNDATION
Hiroshi Uchida UNL Foundation
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Why a Language for computers is necessary ?

• A computer needs a capability to make knowledge
  processing.
• A computer needs to shoulder thought and
  judgement of human using human knowledge.
• A computer needs to make a processing based on
  contents.

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Why a Language for computers is necessary ?

• A computer needs to have knowledge for knowledge
  processing.
• A computer needs to have knowledge to shoulder
  human.
• A computer needs to have knowledge to process
  contents.
• A computer needs a language to store, use,
  manage, create knowledge like human.

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Advantage of having a common language for
computers

• A language for computers greatly reduces cost of
  developing knowledge or contents by sharing
  knowledge and contents of each system.

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What should be a language for computers ?

• Artificial language without any ambiguity
• Consists of
• syntax
• vocabulary (words)
• semantics
• Express information by classifying objectivity
  and subjectivity.

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How a Language for Computers is developed ?

• Define syntax
• Develop vocabulary
• Define semantics

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Role of Syntax in a Language for Computers

• Syntax specifies roles of words
• Syntax specifies subjectivity

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How Syntax of a Language for Computers is
developed ?

• A role of a word in a sentence decided by the
  syntax (marker) and the related word with this
  syntax (marker).
• Syntax (marker) should be defined so as to
  specifies a role without any ambiguity.

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How Syntax of a Language for Computers is
developed ?

• When a word has relations between more than one
  other word, each relation should be set as to be
  able to identify each relation on the premise
  that we have enough knowledge about the concept
  of each word expressed.

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How Syntax of a Language for Computers is
developed ?

• When there are relations between words, each
  relation should be set so as to be able to
  understand the role of each UW only by referring
  to the relation label.
•  
• Collect every subjectivity used in NL.

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Role of Words in a Language for Computers

• A word should express a concept.
• That word should be non-ambiguous.
• A word should have a well defined semantics.
• (understandable for computers)

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How Words of a Language for Computers is
developed ?

• Words of a language can be developed by every
  people
• Methodology to develop words should be clear
  enough.

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How Words of a Language for Computers is
developed ?

• Common symbol are necessary to express concepts
  for developers.
• Combination of common symbols restrict meaning.
• Using combination of common symbols, words of a
  language can be developed.

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Role of Semantics in a Language for Computers

• Meanings of words should be defined so as to
  computers can understand them

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How Semantics of a Language for Computers is
developed ?

• Semantics of a word is defined by defining
  possible relations with other words.
• Collect possible relations between words.

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UNL a Language for Computers

• Artificial language (Semantic Network)
• A node represents a concept
• An arc represents a relation
• A node can be annotated by attributes
• Consists of
• Universal Words vocabulary
• Relations, Attributes syntax
• Knowledge Base semantics

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Long ago, in the city of Babylon, the people
begun to build a huge tower, which seemed about
to reach the heavens.
long ago
people
huge
tim agt agt
aoj
tower
build
begun
obj obj
plc
obj obj
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seemed
reach
city
obj
mod
gol
Babylon
heaven
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Long ago, in the city of Babylon, the people
begun to build a huge tower, which seemed about
to reach the heavens.

• unl
• tim(begin(agtgtthing,objgtthing)._at_entry._at_past,long
  ago(iclgtago))
• mod(city(iclgtregion)._at_def,Babylon(iclgtcity))
• plc(begin(agtgtthing,objgtthing)._at_entry._at_past,city(i
  clgtregion)._at_def)
• agt(begin(agtgtthing,objgtthing)._at_entry._at_past,people
  (iclgtperson).
• _at_def)
• obj(begin(agtgtthing,objgtthing)._at_entry._at_past,build(
  iclgtdo)._at_past)
• agt(build(agtgtthing,objgtthing),people(iclgtperson).
  _at_def)
• obj(build(agtgtthing,objgtthing),tower(iclgtbuilding)
  )
• aoj(huge(iclgtbig),tower(iclgtbuilding))
• aoj(seem(iclgtbe)._at_past,tower(iclgtbuilding))
• obj(seem(iclgtbe)._at_past,reach(iclgtcome)._at_begin._at_soo
  n)
• obj(reach(iclgtcome)._at_begin._at_soon,tower(iclgtbuildin
  g))
• gol(reach(iclgtcome)._at_begin._at_soon,heaven(iclgtregion
  )._at_def._at_pl)
• /unl

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Relation

• Constitutes syntax of the UNL
• Expresses objectivity together with UWs
• Expresses how concepts(UW) constitutes a sentence
  related each other
• 38 relations
• agt and aoj bas cag cao cnt cob con coo dur fmt
  frm ins man met mod nam obj or per plc plf plt
  pof pos ptn pur qua rsn scn seq src tim tmf tmt
  to via

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Attribute

• Constitutes syntax of the UNL
• Expresses subjectivity
• evaluation of the speaker for the
  concepts(sentence)
• Tense, aspect, mood, etc.

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Attribute

• Time with respect to speaker
• _at_past _at_present _at_future
• Speakers view of aspect
• _at_begin-soon _at_begin-just _at_progress
• _at_end-soon _at_end-just _at_complete
• _at_state _at_repeat
• Speakers view of reference
• _at_generic _at_def _at_indef _at_not _at_ordinal
• Speakers focus
• _at_emphasis _at_entry _at_qfocus _at_theme _at_title
  _at_topic
• Speakers attitude
• _at_affirmative _at_confirmation _at_exclamation
  _at_imperative _at_interrogative _at_invitation
  _at_politeness _at_respect _at_vocative

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Attribute

• Speakers viewpoint
• _at_ability, _at_ability-past, _at_admire, _at_although,
• _at_ask-back, _at_conclusion, _at_custom, _at_doubt,
• _at_expectation, _at_grant, _at_grant-not, _at_induce,
• _at_inevitability, _at_insistence, _at_intention, _at_may,
• _at_obligation, _at_obligation-not, _at_possibility,
• _at_probability, _at_regret, _at_request, _at_should,
• _at_unexpected-presumption,
• _at_unexpected-consequence, _at_will
• Convention
• _at_angle_bracket, _at_double_parenthesis,
• _at_double_quotation, _at_parenthesis, _at_pl,
• _at_single_quotation, _at_square_bracket

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Universal Word

• Vocabulary of the UNL
• Adopt English words as common symbols for
  developers
• A UW represents a concept
• 1) Basic UW
• ex) spring
• 2) Restricted UW
• ex) spring(iclgttool)
• spring(iclgtseason)
• spring(agtgtperson,objgtperson)

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How to define an UW(1)

• One must decide to which category listed below
  each concept (meaning) belongs
• Nominal Concept
• Verbal Concept
• Adjective Concept
• Adverbial Concept

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How to define an UW(2)

• Nominal Concept
• swallow(iclgtthing)
• Verbal Concept
• change(agtgtthing) I changed my mind.
• change(objgtthing) The weather will
  change. 
• remember(agtgtthing) I cannot remember his
  name.
• remember(aojgtthing) Do you remember me?

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How to define an UW(3)

• Adjective Concept
• positive(aojgtthing) Are you positive about
  that?
• a positive fact
• only(modltthing) the only person
• Adverbial Concept
• weekly(iclgthow) This class is held
  weekly.
• only(iclgthow) The child only cried.

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How to define an UW(4)

• If the ambiguity of an UW cannot be solved or
  still remains by attaching each of the above
  category labels, UW hierarchy or case relations
  will be used.
• Attaching above category
• swallow(iclgtbird) the bird
• One swallow does not make a summer
• swallow(iclgtaction) the action of swallowing
• at one swallow
• swallow(iclgtquantity) the quantity
• take a swallow of water

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How to define an UW(5)

• Using (case) relations and what it takes
• spring(agtgtthing,objgtwood) bending or dividing
  something
• spring(agtgtthing,objgtmine)) blasting something
• spring(agtgtthing,objgtperson, escaping (from)
  prison
• srcgtprison))
• spring(agtgtthing,golgtplace) jumping up
• to spring up
• spring(agtgtthing,golgtthing) jumping on
• to spring on
• spring(objgtliquid) gushing out
• to spring out

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Role of Master Definition of a concept

• A Master Definition defines a label for a concept
  (UW) and possible relations with other concepts
  (UW)
• How to connect Uws to another UWs.

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How to develop MD

• First decide a label for a concept (UW)
• Define possible relations with other concept (UW)

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How to develop MD

• ex)
• Dixie(iclgtjazzgtmusic)
• Dixie(iclgtjazz)icl?jazz(iclgtmusic))
• bark(agtgtdoggtmammal,iclgtsound(agtgtthing))
• bark(agtgtdog)agt?dog(iclgtmanmal)
• bark(agtgtdog)icl?sound(agtgtthing)

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UNL Knowledge Base

• Collection of concept from each language
• Define every possible relations between concepts
  (UWs)
• Ex)
• dog(iclgtmammal)icl?animal(iclgtliving thing)
• bite(agtgtanimal,objgtthing)agt?
• animal(libing thing)
• eat(agtgtvolitional thing)obj?
• food(iclgtfunctional thing)

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UNL Knowledge Base

• UW system (hierarchy) is introduced for
• to generate a word when a concept is not included
  in a language
• to reduce KB entries which can be deductively
  inferred
• ex)
• dog(iclgtmammal)icl?mammal (iclgtanimal)
• KB give semantics of UWs

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Communication with human language

• A language for computers needs to have a bridge
  to human languages to
• get knowledge or information expressed in human
  languages
• express knowledge or content of computers in
  human languages.

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UNL-Language Master Dictionary

• Defines the link between a word of a language and
  a UW
• Defines grammatical features when a word express
  a concept (UW)
• Define KB when someone introduce a new UW

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Development UNL

• UNL should be developed by all the people in the
  world
• Universal words necessary for each language
• UNL center will develop at least 200,000 UWs in
  this tear.

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What we expect to be developed by people in the
world

• UNL should be developed by all the people in the
  world
• Universal words necessary for each language

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What we expect to be developed by people in the
world

• Language Servers for new languages and new
  domains
• Application systems such as
• Information Retrieval System
• UNL based Search Engines
• Browsers
• Editors/Word Processors
• Machine translation Systems
• UNL Web Pages

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Top 10 Languages by Population

• RANK LANGUAGE POPULATION _____________________
  __________________
• CHINESE, MANDARIN 885,000,000
• SPANISH 332,000,000
• ENGLISH 322,000,000
• BENGALI 189,000,000
• HINDI 182,000,000
• ARABIC, ALL COUNTRIES 177,000,000
• PORTUGUESE 170,000,000
• RUSSIAN 170,000,000
• JAPANESE 125,000,000
• GERMAN, STANDARD 98,000,000
• CHINESE, WU 77,175,000
• ___________________________________________ Et
  hnologueLanguages of the World

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