OntologyOntology, semantic map, , semantic map, cognitive...
Transcript of OntologyOntology, semantic map, , semantic map, cognitive...
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OntologyOntology, semantic map, , semantic map, cognitive schemecognitive scheme
Jean-Pierre DesclésUniversity of Paris-Sorbonne
LaLIC Laboratory« Languages, Logics, Informatics and Cognition »
FLAIRS’O7, Key West, May 2007
Ontology offirst level concepts
Classes of instances
Semantic mapsOntology of
second level concepts
Classes of linguistic markers
(indicators + clues)
Ontologyof third level concepts
Semantic-cognitive schemes
Cognitive and semanticfoundations
Upper ontology
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EXCOMEngine
for automatic semantic annotations
TEXTSLinguistic resources
Annotatedtexts
Ontology offirst level concepts
Classes of instances
Semantic mapsOntology of
second level concepts
Classes of linguistic markers
(indicators + clues)
Ontologyof third level concepts
Semantic-cognitive schemes
Cognitive and semanticfoundations
Upper ontology
USER
Summary of the PresentationSummary of the Presentation• Three levels of ontologies
- First level: Domain Ontology and LDO(Logic of Determination of Objects)
- Second level: The semantic maps is used for a construction of domain ontologies from automatically annotated texts
- Third level: Upper Ontology for the description of the meaning of concepts
• Aspectual values ( State, Event and Process ) : towards an Ontology of Time and Space
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1/ First level1/ First levelDomain OntologiesDomain Ontologies
First level Ontologies First level Ontologies
and Logic of Determination of Objects (LDO)and Logic of Determination of Objects (LDO)
Extension and intension of a concept ‘f’ in a Simple Taxonomic Ontology
ff
Ext (f)Ext (f)
gghh
{x}{x} {y}{y}
x1x1 x2x2 y1y1 y2y2 y3y3
Int(f)Int(f)uuvv
InstancesInstances
Intension
Expansion
Extension
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ττττ(f)
x1 = δδδδ(g1)(ττττ(f))
x2 = δδδδ(g2)(δδδδ(g1)(ττττ(f))
xn = δδδδ(gn) (…. (δδδδ(g2)(δδδδ(g1)(ττττ(f)) …)
δδδδ(g1)
δδδδ(g2)
δδδδ(gn)
.
.
.
f
ττττ Determination operators
δδδδ(g1), … δδδδ(gn)Abstract typical object
Objects more or less determinated
A problem of InheritanceA problem of Inheritance
‘Good’ Deduction: ‘Bad’ Deduction:
(1) All men have two feet (4) A man has two feet(2) Aristotle is a man (5) John is a man
------------------------------ (6) John has only one foot(3) (3) (3) (3) ∴∴∴∴ Aristotle has two feet ----------------------------
(7) * John has two feet
If we accept as general knowledge:(8) the property “to have two feet”
“incompatible” with:(9) the property “to have only one foot”
then the following contradiction arizes :(9) John hasonly one footand John has two feet.
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John
to be a man
has two feet
∈∈∈∈
⊆⊆⊆⊆
∈∈∈∈has only one foot
Int(be-a- man)
contradiction
Int ( John)
ττττ(f)
f
δδδδ(g1) (ττττ(f))
Int (f)
Expansion(f)
z
δδδδ(g2) (ττττ(f)) u
Typical object
Ext (f)Extττττ (f)Typical fully specified instances
does not belongto Expansion(f)
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f
ττττ(f)
x u = δ δ δ δ(g)(x) δ δ δ δ(g)
ττττ
Ess(f)
h = N1(g)
Int (f)
Expansion(f)
Essence (f)
2/ How to build a Domain 2/ How to build a Domain Ontology ?Ontology ?
Second Level Ontologies and Semantic mapsSecond Level Ontologies and Semantic maps
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Building Domain Ontologies from texts
Texts
Semantic AnnotationsEngine (EXCOM)
Linguisticresources
Ontology
Reference Domain
in connection with
Annotatedtexts
ExtractionEngine
1
2
3 4
in connection with
Semantic relations between Semantic relations between concepts : examplesconcepts : examples
• This concept f is a part of the concept g;• A relation is a localization;• A relation of causality;• A concept f is defined by an integration of
more elementary concepts g1, …, gn;• A concept is related to a process, or to a
state• This concept designates an activity;• ….
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Notion of connexion between individuals
• Who has met whom ?• Where ?• When ?
« Qui est avec qui ? »
Who has met with who ?When ? Why ?
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Classof linguistic
markers of the notion 1
Classof linguistic
markers of the notion 2
Classof linguistic
markers of the notion 1.1.
Classof linguistic
markers of the notion 1.2.
Semantic Map ( intension)of a notion
Notion 1
Notion 1.1.
Notion 1.2.
Notion 2
CE rules for the notion 1.1.
CE rules for the notion 1.2.
CE rules for the notion 2
CE rules for the notion 1
IndicatorINDCI1 CI2 CI3 CI4
Research space
Annotationof a viewpoint
Contextual Exploration Rule : INDIND I1 et I2 at left & I3 and I4 at rightI1 et I2 at left & I3 and I4 at rightTHENTHEN annotate according to a viwpointannotate according to a viwpoint
clues
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IND =hypothesis
I1 =my
I2 =is expressedas follows :
Research space : a sentence
Annotation by a topicannouncement(mark of an hypothesis)
An example of annotation : Topic annuncementAn example of annotation : Topic annuncement
cluesclues
Semantic Map (intension)of topic announcement
goals
Hypotheses
Conclusiveremarks
Results
Classof linguistic
markers of goals
Classof linguistic
markers of results
Classof linguistic
markers of the notion 1.1.
Classof linguistic
markers of conclusive remarks
CE rules for « hypotheses »
CE rules for « conclusive
remarks »
CE rules for « results »
CE rules for « goals »
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EngineEnginefor texts Annotationfor texts Annotation
by Contextualby ContextualExplorationExploration
Texts
Viewpoint« meeting »
Viewpoint« quotations »
Viewpointsfortext Mining
Viewpoint« causality »
Viewpoint« definition »
Linguistic resoources according to a viwpoint for text mining
SegmentedTexts
1
23
4
LinguisticResources forsegmentation
Viewpoint« temporal
Information »….
AnnotatedTexts
Ontologyof « viewpoints » Semantic maps
(Level 2 )
Instancesclasses of markers
of relations
Domain Ontologies(Level 1 )
Object classes(instances)
Linguistic Theory
Discourse markerstheory
Texts
Terminologyextractor
AnnotatedTexts
To populateontologies
EXCOMEXCOM
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3/ Third Level Ontologies :3/ Third Level Ontologies :Upper and General OntologiesUpper and General Ontologies
Cognitive Semantics SchemesCognitive Semantics Schemes
Scheme = Scheme = a formal representationa formal representation
of a meaningof a meaning(relations, verbal predicates, (relations, verbal predicates, topological prepositions …)topological prepositions …)
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Cognitive and Applicative Grammar Cognitive and Applicative Grammar (CAG) (Desclés, 1990, …)(CAG) (Desclés, 1990, …)
Morpho-syntaxic Configurationsin Natural Language
Logico-grammatical RepresentationsBy means of applicative expressions
Semantico-cognitive representationsGenerated by Schemes
Lexical SynthesisLexical Analysis
Categorial Analysis Categorial Synthesis ExtendedCategorialGrammars
SemanticInterpretation
Scheme of TO ENTER inScheme of TO ENTER in«« The flight A05 enters the area ZU3The flight A05 enters the area ZU3 »»
< y REP (ext (Loc(z))) > < y REP (int (Loc(z))) >
MOVTSIT 1[x,y] SIT 2[x,y]
enters’ ( z, y)
[ enters’ = def X MOVT REP ext int Loc ]
y := the flight A05z := the area ZU3
Integrating cognitiveunits intoa lexical predicate
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Flight A O5
Area ZU3
Int (Loc (ZU3))
Ext(Loc(ZU3))
The Meaning of TO ENTERThe Meaning of TO ENTER
• [ enters' = def X MOVT REP EXT INT ]
• The meaning of the lexical predicate is given as a functional (applicative) combination of the primitives :
MOVT, REP, ext and int
with an applicative program expressed by the Combinator ‘ X’ of Combinatory Logic
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< x REP (ext (Loc(z))) > < x REP (int (Loc(z))) >
MOVTSIT 1[x,y] SIT 2[x,y]
x
Scheme of TO ENTER inScheme of TO ENTER in«« The Agent X007 enters the area ZU3The Agent X007 enters the area ZU3 »»
enters’ ( z, x)
[ enters 2’ =def Z CONTR MOVT REP ext int Loc ]
= =
x := The agent X007 (an Agent)z := the area ZU3 (a Localizer)
Integrating cognitiveunits intoa lexical predicate
CONTR
< y REP (ext (Loc(z))) > < y REP (int (Loc(z))) >
MOVTSIT 1[x,y] SIT 2[x,y]
x
Scheme of TO GET in Scheme of TO GET in «« The agent X007 gets the flight Y05 into the area ZU3The agent X007 gets the flight Y05 into the area ZU 3 »»
Enters 2’ ( z, y, x)
[ enters 3’ = def U CONTR MOVT REP ext int Loc ]
x:= an Agenty := a Patientz := a Localizer
Integrating cognitiveunits intoa lexical predicate
CONTR
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B in int (Loc(A)) B is int (Loc))
D := moneyD := money
Field of A Field of B
CHANG
CHANG
Agents : A, C
CONTR
«« A sells B to CA sells B to C »»figurative scheme
< B REP (ext (Loc(C))) >
< B REP (int (Loc(A))) >< C OWNS D>
NON <A OWNS D>
< B REP (int (Loc(C))) >
< B REP (ext (Loc(A))) >< A OWNS D>
NON <C OWNS D>
CHANG
SIT 1 [A,B,C] SIT 2 [A,B,C]
CONTR
A, C
Scheme of TO SELL in Scheme of TO SELL in «« A sells B to CA sells B to C »»
[ sells’ = def U CONTR CHANG OWNS REP ext int Loc ( ∃∃∃∃ D) ]
A := AgentB := PatientC := Receiver
∃∃∃∃ D := qnt (money)
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λλλλz.λλλλy. λλλλx. λλλλO1. λλλλO2. λλλλF
[ <x CONT ( ∃∃∃∃ D := qnt (money) such that ( <y REP ext (Loc(z))> AND < y REP int (Loc(x)> AND < z OWNS D>) O1
MOVT F
( <y REP int (Loc(z))>)) > AND< y REP ext (Loc(x)> AND < x OWNS D>) O2 ) ]
with temporal conditions :
- O1 and O2 are open intervals of instants- F is a close interval of instants (a transition) - O1 <F <O2 and d(O1) = g( F); d(F) = g(O2)
FO1 O2
d(O1) = g(F) d(F) = g(O2)
λλλλ-expression associated to the Scheme TO SELL
time
Boundaries
The book The book is onis on the tablethe table
(is (on (the-table))) (the-book)
< (the-book) Rep ON (the-table) >
< (the-book) Rep (Boundary(Loc (the-table))) >
[ ON = Boundary 0 Loc ]
[ IS ON = Rep0 ON ]
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•• Field of PERCEPTION :Field of PERCEPTION :STATIC :
topologic locationstatic relations (∈∈∈∈, ⊆⊆⊆⊆, ε, localization …)
CINEMATIC : motion, change
•• Field of ACTION :Field of ACTION :DYNAMIC :
control, doing, teleonomy
StaticStatic , , Cinematic, DynamicCinematic, DynamicPrimitivesPrimitives
( Dynamical situation : Dynamical situation : CONTROL
( Cinematic situationCinematic situation : MOVement
(static SITUATION : SITUATION : Locating 1) (static SITUATION : SITUATION : Locating 2 ) ) )
DYNAMICAL SYMBOLIC SCHEMEDYNAMICAL SYMBOLIC SCHEMEby embedding ofby embedding of
static situations inside a cinematic situationstatic situations inside a cinematic situation
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MOVT
CONTROLS
Cinematic SITuationCinematic SITuation
Static SITuation 1Static SITuation 1 Static SITuation 2Static SITuation 2
Dynamic SITuationDynamic SITuation
X
< Y Rep Z1 >< Y Rep Z1 > < Y Rep Z2 >< Y Rep Z2 >
Semantico-cognitive dynamic scheme
CAUSALITY
DYNAMICS
CINEMATICS
STATICS
Schemes Embedding
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Cognitive and Applicative Grammar Cognitive and Applicative Grammar (CAG) (Desclés, 1990)(CAG) (Desclés, 1990)
Morpho-syntaxical Configurationsof a Natural Language
Logico-grammatical RepresentationsBy means of applicative expressions
Semantico-cognitive representationsGenerated by Schemes
Lexical SynthesisLexical Analysis
Categorial Analysis Categorial Synthesis ExtendedCategorialGrammars
SemanticInterpretation
4/ Notions and Properties of 4/ Notions and Properties of Aspects Aspects
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STATE (R)
EVENT (R)
PROCESS (R)
Aspectual Values of a Predicative Relation R
time
changes
time
changes
time
changes
STATE EVENT
PROCESS
Open interval Closed interval
Left closed interval
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]I[
∀∀∀∀ t : ( t∈∈∈∈ I ) => Eval (State®,t) = « True»
State (R) is True ∃∃∃∃]I[ such that
[J[t = d(J)
∀∀∀∀ t’’ : ( t’’ ∈∈∈∈ J ) (t’’ < t = d(J)) => Eval (Process(R),t’’) = « True »
Process (R) is True ∃∃∃∃[J[ such that
[F] t=d(F)
(t= d(F)) => Eval (Event (R),t) = « True »Event (R) is True ∃∃∃∃[F] such that
t’’ < t
t
T0
T0
T0
T0
T1
PROCESS
RESULTING-STATE
STATE (« he is happy »)
PROCESS
EVENT
The hunter is shootingthe deer
The hunter has schot the deer
The hunter was shootingthe deer (when) …
The hunter shot the deer
(a’)
(b’)
(c’)
(d’)
This morning
Yesterday
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O’O
F’F d(F)
J’J d(J)
d(J’)
J O^J
F
.
(a)
(b)
(c)
(d)
d(F) = g(O)
STATE EVENT
PROCESS
when accomplished, itgenerates
a transitionbetween states
two states are the bounds of an event
when accomplished, itgeneratesa resulting state
initial state beforea process
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State Process Event
Semantic Map of Aspects
DescriptiveState Completed
Event
Non completed
Event
State of Activity
Non interruptedProcess
ProgressiveProcess
ResultingState Sequence of
discrete occurrences (Events, Processes,
States)
New state
End State
STATESTATE
DescriptiveState State of activity
ResultingState
PermanentState
ContingentState
NewState
Underlying process
Implies
Event
Determined bycontiguous
ConsequentState
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Process
No interrupted
ProgressiveProcess
Interrupted
Event
generates
ContinuousProcess
Discrete Process
Open classof
Events
Closed classof
Events
State of Activity
generates
TimeTime
Temporal Reference System : In(T)
Interval : In(T)
Instant : T
Temporal Situation : Sit
State : Sit Event : Sit Process : Sit
Open closed
Closed to the leftOpen to the right
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5/ Towards an Ontology 5/ Towards an Ontology of Timeof Time
Notion : Time
located inside a refetence framework : Ref
is-in-interval : Int
is-an-instant : T with an-open boundary : Int
with-a-closed-boundary : Int
closed : Int
topological: Int
with boundaries : Intwithout a boundary: Int
is-a-boundary : TClosed to the leftOpen to the right
Open to the leftClosed to the right
δδδδ
:=:=:=:=
εεεεεεεε
εεεεεεεε
:=:=:=:= εεεε
εεεε
εεεε
δδδδ
δδδδ
δδδδδδδδ
ε
εεεε
Types : instant : T
Interval : Int
begin : T end : T
open: Int
εεεε εεεε
εεεε
:=:=:=:=
<<<<
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Properties of the relators: ∈, ε , ⊆ , δ
• X ∈ Y � ∀u,∀v : [X(u) => (Y(v) => u ∈ v)]∈ : F(F(FYH)H)(FXH)H
• X ε Y � ∀u : [X(u) => (Y(u) ]ε : F(FXH)F(FXH) H
• X ⊆ Y �∀u,∀v : [X(u) => (Y(v) => u ⊆ v)]∈ : F(FYH)F(FXH)H
• X δδδδ Y � ∀u : [ Y(u) => ∃v : [ X(v) ; u = δδδδ(Y)(v) ]δδδδ : F(FXH)F(FYH)H
Predication / determination
(∀∀∀∀t) : [is-begin (t) => is a boundary (t)
=> is-an-instant (t) =>is-in-an-interval (t) ]
(∀∀∀∀t) : [ is-begin (t) => is a boundary (t)
=> is-an-instant (t) =>is-in-an-
(δδδδ (closed)(δδδδ (boundary)
(δδδδ (topological)(interval)))) ]
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6/ Temporal Reference Systems6/ Temporal Reference Systems
T0.
.Enunciative external event
Enunciative process
tg tm td
ExternalReference System
EnunciativeReference system
J0
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T0
.
Retrospective organization from the memory
Organization fromthe chronology of realized events
EnunciativeProcess
Realized No realized
PAST FUTURE
T’ ≠≠≠≠ T ≠≠≠≠ T0≠≠≠≠
T’
#
EnunciativeReference System
AnotherReference System
# #
Rupture relation « # » between two Reference SystemsNo-concomitance «≠≠≠≠ » between two instants
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T0
#
EVENT 2EVENT 1 <
#
EVENT<
#
tm
EXRS
ENRS
NARS EVENT 3<
##
##
NARS = « Non-Actual Reference System» ; ENRS = « Enunciative Reference System » ; EXRS = « External Reference System»
TIME
T0
EVENT i-2
EVEN i-1
EVENT i
=#
t0
EVEN i-2
EVENT i-1
Realized Events
NARS
NERS
Reported Events
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Ev1
Ev2
Ev’1
ProcessgeneratingEv 1
Final event ofEv 1
When two events Av 1 and Ev 2 overlap, the linguist ic expression of the overlap entails a decomposition of the first ev ent into an initial process and a final event.
Overlap of Two Events
Etat 1
Etat 2
Etat 3
Proc 2
Proc 3Proc 1
Overlap of Two States
Overlap of Two Process
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Adjacent Boundary
The translation is impossible
The translation is impossible
The translation is possible
7/ Theory of Abstract Places7/ Theory of Abstract Placeswithwith
Inner) Interior and Inner) Interior and (outer) Exterior(outer) Exterior
BoundariesBoundaries
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Interior (LOC)
Exterior (LOC)Ext- Boundary(LOC)
Int -Boundary(LOC)
Interior (LOC)
Exterior (LOC)Ext-Boundary(LOC)
Int-Boundary (LOC)
Interior(LOC)
Boundary Boundary
Int-Bnd
Ext-FroExt-Bnd
Int-Bnd
Exterior Ext(Loc)
I II III IV V VI VII
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Activity State
To get ready to do
To stop
To begin
To finish
To start
is …ing
To have …ed
To continue
Stage 1Stage 2
Different phases (stages) of an EVENTDifferent phases (stages) of an EVENT
Event
State Activity:The plane is in flight
PROCESS :The plane is flying
RESULTINGSTATE:The plane has flown
Stage 1BEFORE Stage 2
AFTER
An Activity State => an underlying ProcessAn Activity State => an underlying Process
Complete event
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EXCOMEngine
for automatic semantic annotations
TEXTSLinguistic resources
Annotatedtexts
Ontology offirst level concepts
Classes of instances
Semantic mapsOntology of
second level concepts
Classes of linguistic markers
(indicators + clues)
Ontologyof third level concepts
Semantic-cognitive schemes
Cognitive and semanticfoundations
Upper ontology
USER
Thank you Thank you for your attention !for your attention !
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• => DESCLES, Jean-Pierre, (1990), « State, Event and Process and Topology », General Linguistics , 29, 3, University Park and Lon,don : Pennsylvania State University Press, pp. 159-200.
• => DESCLES, Jean-Pierre, (1990), Langages applicatifs, langues naturelles et cognition , Hermès, Paris.
• => DESCLES, Jean-Pierre, and GUENTCHEVA Zlatka, (19 95), « Is The Notion of Process Necessary ? » in BERTINETTO, P. Marco & alii, (1995), Temporal Reference Aspect and Actionalitry , Rosenberg and Sellier, Turin. pp. 55-70.
• => DESCLES, Jean-Pierre, (2002), « Categorization : A Logical Approach to a Cognitive Problem », Journal of Cognitive Science , Vol.3, n0 2, 2002, pp. 85-137.
• => DESCLES, Jean-Pierre, (2004), “Combinatory Logic , Language, and Cognitive Representations », in WEINGARTNER, Paul, ( editor) (2004), Alternative Logics. Do Sciences Need Them ?, Springer, pp. 115-148.
• => DESCLES, Jean-Pierre, (2005), “Reasoning and Asp ectual-temporal calculus”, in VANDERVEKEN, Daniel, (editor) Logic, Thought and Logic , Springer, 2005, pp. 217-244.
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• Puraro S. and Storey V.C. « A multi-layered ontology for comparing relationship semantics in conceptual mode ls of databases, Applied Ontology, Vol 1, Number 1, 2005, pp. 117-139
• SOWA John Knowledge Representation : Logical , Philosophical and Computational Foundations , Brooks / Cole, Pacific Grove, 2000.
• SUO IEEE P 1600.1 Standard Upper Ontology Working G roup (SUO WG), 2004
General Ontologies
• GFO (Heinrich Herre)• DOLCE (Nicola Guarino)• Sowa• SUO (IEEE wortking group)• BFO (Barry Smith)• Cyc• CAGO (Desclés & alii)