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OntoKADS
A core ontology to develop expertise models
of the CommonKADS methodology
Bruaux S. & Kassel G.
LaRIA – University Jules Verne of Picardie - FRANCE
Workshop on Core Ontologies in Ontology Engineering
Held in conjunction with EKAW 2004
8th October 2004
Laboratory for Research in Computer Science – Amiens (FRANCE)
http://www.laria.u-picardie.fr
Broad outlines
1. Introduction
1. Introduction
2. The OntoKADS’ methodology: overview
2. The OntoKADS’ methodology: overview
3. The OntoKADS’ ontology: overview
3. The OntoKADS’ ontology: overview
4. The kernel of the OntoKADS’ ontology
5. Conclusion and perspectives
5. Conclusion and perspectives
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Introduction (1/7)
Motivations
Previous works:
 Modelling 3 tasks of simulation codes calibration
[Bruaux, 2002]
 Modelling a “generic” task of calibration with the
CommonKADS methodology
A limitation of the method: the primitive
of “knowledge role” [Bru.,Kas.& Mor., 2003]
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Introduction (2/7)
The CommonKADS’ “knowledge role” primitive
[Schreiber & al., 1999]
Inference structure
Inference structure
• roles are inputs/outputs of inferences
input •role
inference
roles are expressed
by means of abstract output
names role
input role
inference
output role
sign
cover
hypothesis
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Introduction (3/7)
Problem: the identification of roles (1/3)
Role ?
y = ax + b
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role
inference
role
“simulation
parameter”
Domain
concept ?
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Introduction (4/7)
Problem: the identification of roles (2/3)

 abstract names (Data, Result) + understandable inference
structure

 dedicated terms (CodeToCalibrate, SimulationParameter)
= knowledge roles

 easy distinction between “concrete roles” and domain concepts
(SimulationCode, ModelParameter)
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Introduction (5/7)
Problem: the identification of roles (3/3)
In short:
• Abstract roles
Knowledge
• Concrete roles
role
domain concept
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Introduction (6/7)
First attempts to clarify the meaning of the notion of role
Reynaud C. and al. (1997). The Notion of Role in Conceptual
ConjectureIn[Kassel,
1999]:
Modeling.
Proceedings
of the 10th European Knowledge
Acquisition Workshop: EKAW’97, San Feliu de Guixolls, Bonn:
Springer Verlag,
p. 221-236.
“It is necessary
to distinguish the roles played by
objects from the roles played by concepts.”
Kassel G. (1999). PHYSICIAN is a role played by an object, whereas
SIGN is a role played by a concept. In Proceedings of the IJCAI’99
Workshop on Ontologies
and Problem-Solving
Methods:
Lessons
The “abstract
roles” or “knowledge
roles”
in
Learned and Future Trends, Stockholm (Sweden), August 2, 1999, p.
the sense of CommonKADS are roles played
6-1-6-9.
by concepts.
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Introduction (7/7)
Using a formal framework to precise the meaning of
CommonKADS’ modelling primitives
 The ontology of Particulars DOLCE [Masolo & al., 2003],
which contains top-level concepts and relations
 e.g.: PHYSICAL OBJECT, EVENT, ParticipatesIn,
IsAgentOf
• The Formal Ontology of Properties [Guarino & Welty, 2000],
which introduces meta-concepts
 e.g.: SORTAL, FORMAL ROLE, TYPE, QUASI-TYPE
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Broad outlines
1. Introduction
2. The OntoKADS’ methodology: overview
3. The OntoKADS’ ontology: overview
4. The kernel of the OntoKADS’ ontology
5. Conclusion and perspectives
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The OntoKADS methodology
Overview
First step: using OntoKADS to build an application ontology
OntoKADS
OntoKADS
Problem-solving-driven
application ontology
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The OntoKADS methodology
Overview
Second step: translating the application ontology into a
CommonKADS’ expertise model
Problem-solving-driven
application ontology
Task Model
CommonKads’
Task
expertise
model
Task
Method
Inference Model
Domain Model
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Broad outlines
1. Introduction
2. The OntoKADS’ methodology: overview
3. The OntoKADS’ ontology: overview
4. The kernel of the OntoKADS’ ontology
5. Conclusion and perspectives
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The OntoKADS ontology
Overview (1/7)
DOLCE in short (1/2)
Particular
Endurant participates Perdurant
DOLCE
 e.g., object, substance...  e.g., event, process...
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The OntoKADS ontology
Overview (2/7)
DOLCE in short (2/2)
The relation of participation:
PC(x,y,t) def. “x (ED) participates in y (PD) during t”

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e.g.: the AUTHOR (APO) of an article PARTICIPATES
in the WRITING (AC) of the article”
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The OntoKADS ontology
Overview (3/7)
Using the Formal Ontology of Properties in short (1/2)
Rigid
classifies Person
Anti-rigid classifies Author
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The OntoKADS ontology
Overview (4/7)
Using the Formal Ontology of Properties in short (2/2)
The relation of classification:
Cf(x,y,t) def. “The type x classifies the instance y during t”
e.g.: “SABINE (instance) is the AUTHOR (concept) of the article”
“The AUTHOR (concept) is ANTI-RIGID (meta-concept)”
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The OntoKADS ontology
Overview (5/7)
The problem-solving sub-ontology: an extension of DOLCE
 The problem-solving ontology implements two main categories of
entities:
• acts of Reasoning intervening in problem-solving situations
- e.g.: Diagnosing, Calibrating
• entities intervening in these Reasonings
- e.g.: Person, KnowledgeExpression, Agent, Data
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The OntoKADS ontology
Overview (6/7)
The sub-ontology of meta-concepts
 The ontology of meta-concepts implements the CommonKADS’
modelling primitives to “classify” the OntoKADS’ concepts:
input role
inference
output role
sign
cover
hypothesis
 CF(Task, diagnosis, t), CF( Inference, cover, t),
CF(KnowledgeRole, sign, t), CF(DomainConcept, car, t)
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The OntoKADS ontology
Overview (7/7)
Structure of the OntoKADS’ ontology
DOLCE __
OntoKADS __
Particular
Perdurant
Endurant
Domain
Concepts
Roles
Action
Reasoning
Communicating
Tasks,
Inferences
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Domain
Concepts
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Transfert
Functions
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Broad outlines
1. Introduction
2. The OntoKADS’ methodology: overview
3. The OntoKADS’ ontology: overview
4. The kernel of the OntoKADS’ ontology
5. Conclusion and perspectives
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The kernel of the OntoKADS ontology
The problem-solving ontology
The sub-ontology of expressions of knowledge (1/5)
Conjecture [Bruaux & Kassel, 2004]:
“Entities bearing on Reasonings in terms of
data/results are expressions of knowledge.”
This knowledge is expressed by means of
an expression code (a language) and
inscribed on a support.
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The kernel of the OntoKADS ontology
The problem-solving ontology
The sub-ontology of expressions of knowledge (2/5)
car
Diagnosis Task
LowBatteryLevel
EmptyFuelTank
LowBatteryLevel
Complaint
EmptyFuelTank
Hypothesis
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The kernel of the OntoKADS ontology
The problem-solving ontology
The sub-ontology of expressions of knowledge (3/5)
Calibration
Task
simulation
code
y = ax + b
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equations of a
numerical model
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The kernel of the OntoKADS ontology
The problem-solving ontology
The sub-ontology of expressions of knowledge (4/5)
The I&DA ontology introduces ContentBearingObjects
which are expressions (signifiers) of a Content (signified).
The I&DA sub-ontology [Fortier & Kassel, 2004] to account for:
• the different means of expression;
• the expressed different knowledge/contents.
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The kernel of the OntoKADS ontology
The problem-solving ontology
The sub-ontology of expressions of knowledge (5/5)
ED
DOLCE __
I&DA __
POB
Inscription
NPOB
ContentBearingObject
LinguisticObject
Term
Text
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Discourse
IconicObject
Content
Proposition
Assertion
Hypothesis
Concept
Information
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Complaint
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The kernel of the OntoKADS ontology
The problem-solving ontology
The sub-ontology of “participative roles”
ED
BeginningParticipant EndParticipant
Data
CodeToCalibrate
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Patient Determinant
Agent
Substrate
Result
CalibratedCode
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The kernel of the OntoKADS ontology
The ontology of meta-concepts
Ontological definition of knowledge roles (1/4)
Three meta-concepts of the Formal Ontology of Properties
[Guarino & Welty, 2000] are interesting for our study:
• role: an anti-rigid concept dependent of an external entity
• formal role: a role lacking an identity criterion
- e.g.: PATIENT, INSTRUMENT
• material role: a role carrying an identity criterion
- e.g.: STUDENT
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The kernel of the OntoKADS ontology
The ontology of meta-concepts
Ontological definition of knowledge roles (2/4)
Definition of a MATERIAL ROLE: example of the EMPLOYEE concept
(TYPE)
PERSON
(FORMAL ROLE)
HUMAN RESOURCE
(MATERIAL ROLE) EMPLOYEE
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The kernel of the OntoKADS ontology
The ontology of meta-concepts
Ontological definition of knowledge roles (3/4)
By analogy with the previous definitions, we define three notions of
"knowledge role":
• KnowledgeRole: a role dependent on a Reasoning
• FormalKnowledgeRole: a KnowledgeRole lacking an identity
criterion (e.g.: CalibrationData, DiagnosisResult)
• MaterialKnowledgeRole: a FormalKnowledgeRole carrying an
identity criterion, which it inherits from a Type constrained to be
a Proposition (e.g.: CodeToCalibrate)
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The kernel of the OntoKADS ontology
The ontology of meta-concepts
Ontological definition of knowledge roles (4/4)
OntoKADS __
(I&DA __)
ED
(Content)
(Proposition)
Data
Result
Code Hypothesis CalibrationData DiagnosisResult
CodeToCalibrate
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DiagnosisHypothesis
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Broad outlines
1. Introduction
2. The OntoKADS’ methodology: overview
3. The OntoKADS’ ontology: overview
4. The kernel of the OntoKADS’ ontology
5. Conclusion and perspectives
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Conclusion and perspectives
Synthesis of our main contributions (1/2)
• The OntoKADS ontology has led us to revisit
CommonKADS’ modelling primitives: in particular, the
KnowledgeRole meta-concept differs from the meaning
given to the “knowledge role” notion in CommonKADS.
• The participants in Reasonings (Tasks) are not objects or
state of objects (DomainConcepts) but Propositions
(KnowledgeRole) having DomainConcepts as subjects.
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Conclusion and perspectives
Synthesis of our main contributions (2/2)
Two categories of KnowledgeRoles:
- FormalKnowledgeRoles, referring to particular Reasonings
►e.g.: CalibrationData, DiagnosisResult
- MaterialKnowledgeRoles, referring to particular Reasonings
and to particular Contents
►e.g.: CodeToCalibrate, CalibratedCode
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Conclusion and perspectives
Future works
Works presented here are progressing in three main directions:
1. the evaluation of the CommonKADS’ primitives to specify
problem-solving methods: in particular, force the methods’
Inputs/Outputs to be KnowledgeRoles would have some
consequences on these methods
2. the integration in OntoKADS of all generic Reasonings
identified in analytic and synthetic Reasonings
3. the devolopment of a software, at a conceptual level, based
on the TERMINAE platform to support the OntoKADS
methodology
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Thank you for your attention
• To contact the authors of this paper :
Sabine Bruaux, mailto: [email protected]
Gilles Kassel, mailto: [email protected]
• For + infos about team works :
http://www.laria.u-picardie.fr/equipe_ingenierie_connaissances.html
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Conférence IC'2003 - Etude Critique de CommonKADS
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