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Concepts, Language and Ontologies (from the logical point of view) Marie Duží VŠB-Technical University of Ostrava Czech Republic Motto : Es gibt eine und nur eine vollständige Analyse des Satzes. Wittgenstein, Tractatus, 3.25. Content. Terminology - Ontology

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Concepts, Language and Ontologies

(from the logical point of view)

Marie Duží

VŠB-Technical University of Ostrava

Czech Republic

Motto: Es gibt eine und nur eine vollständige Analyse des Satzes.

Wittgenstein, Tractatus, 3.25


Content
Content

  • Terminology - Ontology

  • Traditional ”theories of concept”:

    • What kind of entity is aconcept?

    • What is the content and extent of a concept ?

    • Does the Law of inverse proportionalways hold ?

  • Transparent intensional logic (Pavel Tichý)

  • Theory of concepts (Pavel Materna)

  • Concepts and language

    • Ontological vs. linguistic definition

  • Conceptual lattices

  • Conclusion: An outline of applications


Terminology – ontology:What are we talking about?

(Current state: a mess, chaos !!)

What kind of entity is aconcept?

CONCEPT = universal ??CONCEPT = expression ??

CONCEPT = <Int, Ext>Int: Intension (intent, content) of a CONCEPT Ext: Extension (extent) of a CONCEPT (Circular ”definition”)


Wh at kind of entity is the content and extent of a concept
What kind of entity is thecontent and extent of a concept?

Content = {subexpressions} ??

Content = Intension – possible world semantics ??

Content(Intent) = Kauppi: a pre-concept – not defined

Content = Ganter-Wille: {database-like attributes} The way of combining them – only conjunctive

Extent = {objects ”falling under” the concept} ?

{objects satisfying attributes of the content}

More sophisticated conceptions:Concept = an axiomatic theory

Content = the set of axioms, Extent = the set of models


Traditional conception.

Concept is “something” that consists of an intent and extent

Worrisome questions:

a) What is that “something”?

b) What exactly the extent and intent (content) is?

c) How shall we handle modal and temporal variability of the extent?

d) Does the law of inverse proportion between the intent and the extent always hold?

Bolzano: The way of composing contained constituents is important!


Our approach: Transparent Intensional Logic (TIL) Pavel Tichý

Platonism and realism(nominalists are hostile)

Platonic „heaven“

(beyond space and time)

Actualised, discovered potential: ”named” abstract objects

(in any language – natural, formal, ”demonstrative”, ...)

Expression  sense (meaning) = concept  denotation

Back to ”old-fashioned” classics

(Bolzano, Frege, Russell, Church, Gödel, …)

Functions, procedures, sets, CONCEPTS


(Infinite) Hierarchy of entities (of our ontology):

1st order: Unstructuredentities(from the „algorithmic point of view“,

though having parts, members, …)

a) basic entities: (non-functional) members ofbasic types: = {True, False}= individuals (universal universe of discourse)= time points (real numbers) = possible worlds (consistent maximum setsof thinkable facts)

b)(partial) functions (mappings): (1,…,n)  denoted (1…n). (-)sets are mapped by characteristic functions – ().


Intensions vs extensions still members of 1st order
Intensions vs. extensions (still members of 1st order)

  • -intension: member of a type (()

    • denoted 

  • -extension: not a function from 

  • Examples of intensions:

    • student / ()- property of individuals

    • the president of CR / - individual office

    • Charles is a student / – proposition

    • age of / ()– attribute (empirical function)

      Not to confuse with Intension (intent, content),

      Extension (extent) of a concept !


Structured procedures
Structured procedures

  • 2nd order: Constructionsof 1st order entities, all of them belong to type 1

    • Variables: x, y, z ... any type (not only individuals!)

    • Trivialisation: 0X basic object X, function X

    • Closure: [ x1 ... xn C]  Function / ( 1...n) 1 n 

    • Composition: [C X1 … Xn]  Value of the function( 1...n) 1 n

      Example:

      x [0+ x 01], x, 01, 05 / 1 ( ‘/’ = belong to)

      x  , x [0+ x01]  ( ) (‘ ‘ = construct)

      [ x [0+ x01] 05 ]  6 / 


  • 3rd order: Constructionsof 1st and 2nd order entities, all of them belong to type 2

    Examples:

    0[x [0+ x01]] / 2, constructs [x [0+ x01]] / 1

    ‘Adding 1 is an arithmetic procedure’

    Ar / ( 1) – class of arithmetic 1st order constructions

    [0Ar 0[x [0+ x01]] ] / 2, constructs True

    And so on ...


Sources of mess (Confusing):

Expression (”icon of” an abstract entity) – written recipe

with

Mode of presentation (structured procedure, concept ) – nabstractway of cooking

with

The product of the procedure (mostly 1st order, unstructured) –

with (property of) meals

______________________________________________________

Process of executing the procedure cookingin space and time

with (case: the product being a function)

The value of the above (at an argument)particular dumplings


Sources of mess confusing
Sources of mess (confusing):

‘The president of CR’ (Empirical) expression

wt [0Presidentwt0CR] meaning = concept

office/ intension (= denotation)

(but extent of the concept)

Nobody (Havel till Feb.)Value of the intension (in w,t)

result of empirical information retrieval (e.g. web search)


Using vs. Mentioning (entities of our ontology)

1st order:

·basic entities: only mentioned – 03, 0Charles

·functional entities:

a) used to obtain its value (by composition)[x [x + 01] 05]  6[0Even 05] False”talking about” the value – de re

b) mentioned(”talking about” the whole function – de dicto) ‘Adding 1 is a bijective mapping’ [0Bij [x [x + 01]]]  True Bij / ( ())

But in both cases construction[x [x + 01] is used(either de dicto or de re)to construct the function


2nd order: Constructions (concepts)

a)    usedto construct (identify) a (1st order) entity[0Bij [x [x + 01]]] Construction [x [x + 01]] is usedde dicto,function ‘adding’ is mentioned

[x [x + 01] 05]Construction [x [x + 01]] is usedde re,function ‘adding’ is used

b)  mentioned (talking about concept – construction) ‘Dividing x by 0 is improper (does not yield any result)’:[0Improper 0[x : 00]]  True, Improper / (1) – used[x : 00] / 1 – mentioned


Charlesknows that dividing xby 0 is improper’

wt [0Knowwt 0Charles 0[0Improper 0[x : 00]] ]construction [0Improper 0[x : 00]] – mentioned

Our knowledge, deductive (inference) abilities concernprimarily

concepts, i.e., constructions, i.e., procedures

not only their outcomes - truth-values, intensions, propositions, …

Modes of presentation, ways of presenting are important:

Do we know theNumber  ?

the ratio of the circumference of a circle to its diameter


Non-traditional Theory of Concepts (Materna).

Did we answer the fundamental ontological question

What is a concept? Conceptis a closed construction

(roughly – up to ”renaming” bound variables, …)

What is the content (intent) and extent of the concept?

A concept C1 is (intensionally) contained in a concept C2,

iff C1 is a sub-construction of C2,denoted C1 IC C2.

Content (intension) of a concept C

is theset of concepts that are contained in C.

Extent (extension) of a concept C

is the object E, which is constructed by C.

An empirical conceptis such a concept CE,

the extent of which is an -intension (/ ). !!!


Example:

w t [ 0TennisPlayerwt [w t [0Presidentwt 0CR]]wt ]

ContentExtent

--------------------------------------------------------------

0TennisPlayer Ind. property / ()

w t [0Preswt 0CR] Ind. Office / 

0President emp. function / ( )

0CR individuum /  (for the sake of simplicity)

The whole concept proposition / 


w t [ 0TennisPlayerwt [w t [0Presidentwt 0CR]]wt ]

Vaclav II.

The extent of an empirical concept CEin a world/time w,t:

the value of its extent Int in w,t :[Intwt]

Out of the scope of an a priory LOGIC ! Empirical investigation

ContentExtent in w, t

0TennisPlayer A set of individuals (who play tennis) / ()

w t [0Presidentwt 0CR] not defined till Feb. 28th

Vaclav Klaus now / 

0President function / ( )

0CR Individuum (for the sake of simplicity)

The whole concept Truth-value True / 

A simple conceptof a (1st order) object X is0X.

(Primitive concept with respect to a Conceptual System)


Relation of intensional containment (IC) is the relation of

partial ordering on the set of concepts

(reflexive, anti-symmetric and transitive)

Can a (semantic) conceptual lattice

(following the law of inverse proportion) be built up using IC ?

NO. Just an enumeration of contained concepts does not suffice.

We have to specify the wayin which the contained concepts

are composed together to form a structured complex and apply

correct logical inference rules on the whole concept.

Set-theoretical approach does not suffice:

It cannot render the structural (procedural) character of concepts.

Analogy:

We deal with the difference between a(structured) algorithmand its („flat“) output


Examples:

The concept of a bachelor:

wt x [ [0Marriedwtx]  [0Manwtx] ]  ()

contains0Married, 0Man, wt x [0Marriedwtx], …

‘student of the university of Prague’ vs.

‘student of the university of Prague or Brno’

‘Man who understands all European languages’ vs.

‘Man who understands all living European languages’ (Bolzano)

‘cities and districts of the Czech republic’ vs.

‘cities and districts in Moravia’

‘Wooden horse’ vs ‘horse’ !

Adjectives: either modify a property, orcreate a new property

wt [ 0Woodenwt0Horse ] Wooden / (() ())

0Horse IC[wt [0Woodenwt0Horse ] ]


Concepts and Language.

Assignment ‘expression  concept (=meaning)’

is given by a linguistic convention, it is an empirical relation.

Thus the answer to another question:

Do concepts change? is NO;

just the above assignment of concepts to expressions can change,

”meaning of an expression changes”, we even invent newexpressions to name some ”newly discovered” concepts,

and some old expressions cease to be used.

Hence a (living) language develops, and moreover,

each domain of interest uses actually its own ”jargon”,

we are building particular ”ontologies”.


Ontological vs linguistic definition
Ontological vs. linguistic definition

Each complex nonempty concept C is

  • Anontological definitionof its extent O,

  • concept C defines the object O constructed by C.

    Example: Ontological definition of (the class of) prime numbers /() is:

    x ( [0Nat x]  [0Card y ([0Nat y]  [0Div x y])] = 02 )

    Ontological definition does not define an expression but an object (intension / extension)


By a ‘definition’ we usually understand the following schema:

Expression E1 (definiendum) =df expression E2 (definiens).

From the logical point of view this is a linguistic definition.

Thus simple expressions often do not express primitive simple concepts

(trivialisation of a denoted object), but complex concepts.

Linguistic definition assigns to E1 as its meaning

the ontological definition of the object denoted by E2.

Examples: Cat =df Domestic carnivorous animal, a feline, …

Prime: x ( [0Nat x]  [0Card y ([0Nat y]  [0Div x y])] = 02 )

Primes=dfnatural numbers that have exactly two factors.

Number  =df the ratio of the circumference of a circle

to its diameter

Accountant is a man who masters financial operations …


Conceptual lattices schema:

Requisites and typical properties

[Reqpr P Q] =wt x [[Qwtx]  [Pwtx]]

(P is a requisite of Q)

[Reqof P U] =wt [[0Ewt U] x [[Uwt = x]  [Pwtx]]]

(P is a requisite of U, E is the property (of an office) of existence)

[TPpr P Q G] = wt x [[Gwtx]  [[Qwtx]  [Pwtx]]]

(P is typical for Q, unless G)

[TPof P U G] =

wt [[0Ewt U] x [[Gwtx]  [[Uwt = x]  [Pwtx]] ]

(P is typical for U, unless G)

Artificial intelligence:

the condition G -- the guard of a rule.


A schema:typical property of a bird is flying, unless it is a penguin or an ostrich.

A typical property of a swan is being white, unless it has been born in Australia or New Zealand.

Being a ruler of France is a requisite of the King of France.

Being a carnivorous animal is a requisite of a cat.

It ”follows from” the concept of a cat thatmy ‘Mikes’ is a carnivorous animal, …


Semantic partial ordering schema:on the set of (equivalent) concepts

Let C1 and C2 be empirical concepts such that

C1constructs a requisite R of the extent I constructed by C2:

Then C1is weaker than or equivalent to C2, denoted C1 C2.

Claim:

Let properties EC1, EC2 be extents of concepts C1, C2, respectively,

such that C1  C2.

Then necessarily, i.e., in all world/times w,t, EC2wt  EC1wt

The law of inverse proportion.

A special case: (finite number of requisites)

a concept C can construct I by means of ”conjuncting” Ri:

wtx ([R1wtx]  … [Rnwtx]).

Ganter-Wille:

conjunctive conception -- a special (frequent)case


  • Our Theory provides: schema:

  • an explication of classical approaches

  • an essential extension of classical theories: Ganter-Wille, Kauppi, intuitionistic

  • TIL essential extension overcomes the following

  • shortcomings:

  • (all of that under one hat)


  • Extensional schema:systems do not distinguish analytical vs empirical using a modal, temporal or intensional logic(S5, Ty2, Montague, TIL, …)

  • 1st order predicate logic - not mentioning (functions, relations, concepts)  using higher-order logic of which order ? type system

  • Denotational approach: not disting. synonymous vs. equivalent procedural declarative semantics (structured meanings)

  • Formalistic approach: not handling fine-grained distinction between a formal scheme of a set of constructions vs. the construction itself  transparent approach (formal, but non-formalistic)

  • Classicalsystems of predicate logics do not handle partial functionsandempty concepts TIL: partiality being propagated up


Conclusion possible applications
Conclusion: possible applications schema:

Our knowledge concern concepts

Correct fine-grained logical (i.e. conceptual) analysis is a necessary condition ofknowledge acquisition,inferring (implicit) knowledgeand performing correct semantic information retrieval

Problem: Practical applicability of the method in the web environment comprising huge amount of heterogeneousdocuments.

ARG: Methods of reducing the dimension of the problem.

Poset of pairs Documents, Expressions (Galois definition) ordered by the relation of occurring in

 Lattice of areas of interests together with their vocabularies


The next step to be done is a schema:linguistic one:

It consists in a disambiguation of the vocabulary, creation of the so-called ”intelligent thesaurus” – a semantic dictionary in which each important term is provided with the ontological definition of the denoted object: concept (logical construction) expressed by the expression is assigned to it.

Using inference rules of the given system  requisites and typical properties

Semantic conceptual lattice


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