1. TERMINOLOGY – CONCEPT MODELLING – ONTOLOGY
XXVI VAKKI SYMPOSIUM – February Vaasa, Finland
IS ONTOLOGY OVERRATED?
When I was asked to propose some titles for my talk…
Is ontology overrated? : It was a joke
Ontology Acquisition for Terminology
Pr. Christophe Roche
University of Savoie - Campus Scientifique
Le Bourget du Lac cedex – France

2. ? ONTOLOGY Knowledge Representation Language
Oiled
LOOM
Protégé
Ontonlingua
OWL
ONTOLOGY
RDF Schema
SHOE
WebOde
WordNet
KIF
TOVE
Description Logic
Representation
OntoEdit
DAML-OIL
UML
Language
 Representation of Ontology ?
As a matter
 Natural Language ?
 Logic ?
 UML ?
 Text and Ontology ?
 Lexical Ontology ?
 WordNet ?
 Ontology Building from Text ?

3. Some principles…  What is it for ?  Ontology is an Object
 Representation
 Ontology is a “Science”
Object : Product, Result of a Practice
Today we focus too much on representation than on knowledge.
Science: systematic study and knowledge of a domain
Building : Ontology does not belong to Linguistics
 Knowledge
 Principles
 Ontology Management
 Building
 Exploiting
 Maintaining - Updating

4. « TERMINOLOGY – CONCEPT MODELLING – ONTOLOGY »
TABLE OF CONTENTS
« TERMINOLOGY – CONCEPT MODELLING – ONTOLOGY »
 Terminology
 Concept Modelling
 Ontology
 Representation Language
 Knowledge
 Text
 Wordnet, UML, Naming the World
 Conclusion

5. TERMINOLOGY
ISO :2000
special language : language for special purposes (LSP), language used in a subject field and characterized by the use of specific linguistic means of expression.
subject field : Domain, field of special knowledge
terminology 1 : set of designations belonging to one special language
terminology 2 : science studying the structure, formation, development, usage and management of terminologies
designation : representation of a concept by a sign which denotes it
concept : unit of knowledge created by a unique combination of characteristics.
Note: Concepts are not necessarily bound to particular languages. They are, however, influenced by the socialor cultural background which often leads to different categorizations.
characteristic : abstraction of a property of an object or of a set of objects.
generic relation (genus-species relation) : relation between two concepts where the intension of one of the concepts includes that of the other concept and at least one additional delimiting characteristic
Intension : set of characteristics which makes up the concept
essential characteristic : characteristic which is indispensable to understanding a concept
delimiting characteristic : essential characteristic used for distinguishing a concept from related concepts

6. TERMINOLOGY Handbook of Terminology
terminology 1 : the set of special words belonging to a science, an art, an author, or a social entity.
terminology 2 : the language discipline dedicated to the scientific study of the concepts and terms used in specialized languages.
concept : a unit of knowledge abstracted from a set of characteriscs attributed to a class of objects, relations or entities.
term : a word (simple term), multiword expression (complex term), symbol or formula that designates a particular concept within a given subject.
generic relationship : the hierarchical relationship between a general concept and a series of subordinate concepts that inherit its properties but which are distinguished from one another by at least one delimiting characteric.

7. “signified” [signifié] “signifier” [significant]
Community of Practice
Language for Special Purpose
Lexicology
{ specialized words }
usage words ?
Terminology
{ terminology units }
Terminology : objectives : to eliminate ambig
concept
“signified” [signifié]
réel
denomination
“signifier” [significant]
designation

8. ? Semiotic Triangles Ogden – Richards Saussure Scholastic signified
conceptus
signifier
referent
vox
res
signified (meaning) : a value in system
concept : a set of characteristics

9. CONCEPT MODELLING  Concept Definition  Concept Modelling
meaning of a term = denoted concept
 Concept Definition
definiendum = definiens
Concept = set of characteristics
- extra linguistic
- system of concepts
How to represent the definiens ?
the definition simply consists of a definiendum followed by a definiens, i.e. an expression that gives the meaning of the word, with some punctuation.
The words definiendum and definiens are Latin participle forms of the verb definire 'to define', and they mean 'the one being defined, the one to be defined' and 'defining, the one that defines', respectively. By the way, the meaning of that verb was originally related to limiting, then to designating by limiting; the verb is derived from the noun finis 'boundary, limit, border, end'.
Similarly, a formula used for defining something usually has the form definiendum = definiens, e.g. F = ma (force equals mass times acceleration). (Most formulas are not definitions, of course, and it depends on the system of physics or other realm of knowledge which formulas are taken as definitions. A formula which is a definition in one system might be a derived one in another.)
Even a definition written as a normal sentence often consists of a definiendum = definiens pattern, just with a word like is or means instead of an equals sign. Example: A liger is a hybrid (cross) between a tiger and a lion.
But a simple definition could be written in a different form, and for stylistic reasons this is quite common. It is normal to write a new word after its definiens: A hybrid (cross) between a tiger and a lion is called a liger.
salve veritate ?
two expressions are “synonymous” in a language L iff they may be interchanged salve veritate in each sentence of L in which they occur.
 Concept Modelling
- scientific approach
“formal” langage - system
- definition in natural language = comment

10. CONCEPT MODELLING  “Formal” Language - System
Why ? : objectives of Terminology :
- precise (without ambiguity)
- consensual
- re-usable
to get off the problems risen by NL
What is it ?
- “theoretical concepts”
- rules (syntax)
- operations (reasoning)
=> to build a representation of the concepts of the domain.

11. A specification of a conceptualization
CONCEPT MODELLING
 “Theoretical Concepts”
Semi formal : Class, Relationships (hierarchy)
class-def white-wine
subclass-of wine
slot-constraint has-color
has-filler white
The trap of formal science : if you accept the hypotheses, you are obliged to accept its construction (definition)
Formal : first order Logic (Predicates)
hypothetical – deductive systems
Person : (and Animal (all (restrict hasParent Person))
A specification of a conceptualization
ONTOLOGY (Gruber)

12. ONTOLOGY
Web search result for: Ontology

13. ONTOLOGY Methontology Sensus IDEF5 SHOE LOOM Mikrokosmos KIF
Ontonlingua
OWL
Conceptual Graphs
WordNet
2 Questions :
- How can I find my in a such jungle ?
- Why ontology is so popular ?
DAML-OIL
Semantic Networks
Protégé
Enterprise Ontology
RDF Schema
Schemas
Description Logic
OntoEdit
WebOde
BSDM
TOVE
WebOnto
Oiled
Cyc
Kaon
KR Ontology
CommonKADS

14. Ontology : Why ? Common Language A Myth :
A shared and common understanding of some domain that can be communicated across people and computers
So, Ontology is a very popular subject. Why is it so popular ?
Common Language
- no communication
- no knowledge sharing
- no knowledge exchanging
… without agreement on the meaning of terms

15. Collaborative Engineering
Ontology : What is it for ?
…to enable communication and knowledge sharing between people and computers !
“Ontologies are finding applicability in many other areas of information systems engineering, for example, in database design, in object systems, in knowledge based systems and within many application areas, such as datawarehousing, knowledge management, computer supported collaborative working and enterprise integration.”
Ontology.org
Collaborative Engineering
Search Engine
Data Base
Knowledge Management
Semantic Web
Information System
Multi-Agent Systems
Natural Language
Interoperability
E-Commerce
Communication
Is a general view of ontology possible ?

16.  Ontology : What is it ? A Knowledge Engineering Point of View
 There is today an agreement on the definition :
 Set of Concept Definitions and Relationship Definitions
What is an Ontology ? Short answer: An ontology is a specification of a conceptualization.
In the context of knowledge sharing, I use the term ontology to mean a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set-of-concept-definitions, but more general Tom Gruber
 Vocabulary of Terms
« An [explicit] ontology may take a variety of forms, but necessarily it will include a vocabulary of terms and some specification of their meaning (i.e. definitions). »
“Ontologies: Principles, Methods and Applications” M.Ushold & M.Gruninger. Knowledge Engineering Review, Vol.11, n°2, June1996
 There is today an agreement on the objective :
 Communication and Knowledge Sharing between Human and/or Software Agents
« The main purpose of an ontology is to enable communication between computer systems in a way that is independent of the individual system technologies, information architectures and application domain. » 
A Knowledge Engineering Point of View

17. Specification of a Conceptualisation
Ontology : What is it ?
- 2 components : - a vocabulary of terms ,
- a set of definitions.
Common Language
Properties :
- consensual
- coherent
- precise
- sharable
Vocabulary
Specification of a Conceptualisation
< … >
Reasoning :
- queries
- assertions
- inferences
Terms = concept’s names

18. ? Ontology : What is it ? < … >
Term’s Meanings ?
< … >
Thing
The Knowledge Engineering point of view:
“What exists is that which can be represented”
“An ontology is a shared description of concepts and relationships of a domain expressed in a computer readable language”
class-def white-wine
subclass-of wine
slot-constraint has-color
has-filler white
Person : (and Animal (all (restrict hasParent Person))
- agreement on definition (Gruber).
(agreement on content ?)

19. Representation Languages
“An ontology is a shared description of concepts and relationships of a domain expressed in a computer readable language”
Logic-based languages
- clear and formal syntax and semantics
- sound inferences
- A concept is a well formed formula
- A concept is the intension definition of a set
- operational languages
Properties :
- consensual - completeness
- coherent - soundness
- precise
- can be shared
(interchange format)
A concept (category) is an unary predicate.
‘form(x)’ = independant(x)  abstract(x)
Logic is necessary

20. Representation Languages
Frame-based languages
Artificial Intelligence
Frame System, Conceptual Graph, Semantic Network.
- A concept (class) is a set of slots
- Facets are associated to slots
- Concepts are organized according to the « sub-class » relationship
(a simple or multiple inheritance relationship)
- The concepts are structured into graphs or taxonomies
- The meaning of a term is the concept denoted by the term

21. Representation Languages : An Example OWL
The Web Ontology Language - DAML : DARPA Agent Markup Language OIL : Ontology Inference Layer
Formal semantics and reasoning support
Human readable form
Logic
Frame
Class-def defined adult-elephant
subclass-of elephant
slot-constraint age
has-value (min 20)
OWL
Web
Interchange format
WEB Syntax (XML & RDF)

22. The Representation Language Problem
Too nice to be true… ?
 Always Coherent ?
 Can be Re-Used ?
 Really Compatible ?
The bride is too beautiful
 Really Shared ?
 Really Consensual ?
 Do I agree with the vocabulary of terms ?
 Do I agree with the (formal) meaning of terms ?
 Is the conceptualization really common and shared ?
 How do I build such a domain conceptualization ?

23. The Representation Language Problem
“An ontology is a shared description of concepts and relationships of a domain expressed in a computer readable language”
Logic-based languages
- Human readable ?
- Re-usable ?
- Merging ?
Enterprise Ontology
(Define-Class Activity-Or-Spec (?X)
"The union of Activity and Activity-Spec"
:Iff-Def (And (Eo-Entity ?X) (Or (Activity ?X) (Activity-Spec ?X)))
:Axiom-Def (Partition Activity-Or-Spec (Setof Activity Activity-Spec)))
TOVE
(define-class plan_action (?a) :def
(forall (?alpha ?f ?s)
(=> (holds (agent_constraint ?alpha (fluent_goal ?f)) ?s)
(forall (?ap ?s1 ?s2)
(=> (and (subaction ?ap ?a) (leq ?s1 ?s2) (Do ?ap ?s1 ?s2 (intended ?s2))
(holds ?f ?s2)))))
Axiom for the ‘dormant’ status of an activity (dormant, executing, suspended, reExecuting, terminated)
(EQ 38) (∀ a,e, σ) holds(activity_status(a, dormant), do(e, σ)) ≡ ((&eksist; s) state(s,a) & e=commit(s,a) & holds(status(s,a,possible), σ)) | ¬((&eksist; s) substate(s,a) & e=enable(s,a)) & holds(activity_status(a, dormant),σ).

24. The Representation Language Problem
Using a same formal language (logic) is not a guarantee of consensus !
- There is a consensus about the syntax and the semantics of the language.
- It does not mean a consensus on the knowledge express with this language.
Epistemological Problems :
 definition of a concept
 a concept is not a wff
 a set is not a concept
 an essential property is not a relation
 ...
Logic is necessary, but a posteriori, not a priori.

25. The Representation Language Problem
“An ontology is a shared description of concepts and relationships of a domain expressed in a computer readable language”
Frame-based languages
“The mercury is a both a metal and a liquid”
MIKROKOSMOS
« In this ontology, you should not expect to find : any kind of guarantees, warrantees, or liability for correctness or precision, formally clean or theoretically "pure" concepts, complete consistency; guaranteed absence of contraditions; etc »
- More epistemological than logic
(class, slot, relationships)
- Less sound
Epistemological Problems :
 a technique (representation) does not define a
knowledge theory (conceptualization)
 subsumption is more than an inheritance relationship
 an essential property is not an attribute
 ...

26. ? WHAT IS THE PROBLEM ? Go back to the definition…
The main objective of ontology from the computer science point of view is normalization based on a specification of a conceptualization
It means that the main objective is :
- not to define meaning of terms which would imply a linguistic theory,
- not to understand the world which would imply an epistemological theory,
- but to define concepts based on a computational language in order to manipulate entities.
A more representation-oriented approach than knowledge-oriented.
Go back to the definition…

27. Knowledge
Epistemology
Representation
Linguistics

28.  What « Language - Theory » to choose ?
Knowledge & Language
The ontology needs to be specified in some language
Intention
Language
« Object »
Theory
« Agent »
 What « Language - Theory » to choose ?

29. Knowledge – Representation - Visualization
Visualizations
Representation Languages
There is no pure fact. A fact is what it is only under the view of the concept which founds it.
Theory 1
Theory 2
Theory n
World
Information Space

30. Knowledge – Language - Representation
 World : « Every cat is on a mat »
 Theory : Existantial Graph of Pierce
 Representation Languages
LF : [Cat:  ] ® (On) ® [Mat]
] ® (On) ® [Mat]
CGIF : *x] [Mat: *y] (On ?x ?y)
( On [Cat every ] [Mat] )
KIF : (forall ((?x Cat)) (exists ((?y Mat)) (On ?x ?y)))
Logic : (x:Cat) ($y:Mat) on(x,y)
 Visualization

31. Visualization
UML
Protégé

32. Theory
Visualization
Language

33.  What « Language - Theory » to choose ?
Knowledge & Language
The ontology needs to be specified in some language
Intention
Language
To choose a language which helps me to model and understand the world
« Object »
Theory
« Agent »
 What « Language - Theory » to choose ?

34. ONTOS ( ? ) + LOGOS (Language – Science – Reason)
Etymology
ONTOS ( ? ) LOGOS (Language – Science – Reason)
ONTOS : ?
« I am a man » in Spanish :
« Soy un hombre »
« I am ill » in Spanish :
« Estoy enfermo »
Soy un hombre
Estoy enfermo
to be : ser , estar
French Nouns : Être – Étant
German Nouns: Sein – Dasein
English : Being ?
Beingness ?

35. Science of Being as Being “Science of Existence”
Etymology
Science of Being as Being
Etymology & Philosophy
Soy un hombre
Estoy enfermo
Essence
“Science of Being”
Metaphysics
Properties
“Science of Existence”
Phenomenology

36. Epistemology  An Ontology must reflect the structure of the world !
 There are different kinds of knowledge
Terminological & Ontological Logic
Logic of Judgments
Logic of Reasoning
Concept : knowledge applied to a plurality of things governed by a same law.
Ontology : science of being as being independently of its particular states.
=> Different Languages
Concept, Set, Class
Essential property, Atrtibute
Relationship
=> Different “Theoretical Concepts”

37. Logic  A neutral language
no epistemological principal : rewriting system
=> “good” formal properties
 Epistemological problem
Unary predicate : Concept or a Property
Apple (x) , Red (x)
Binary Predicate : Property or a Relationship
Color (x,y) , GreaterThan (x , y)

38. Logic  Extend Logic Postulates written in logic itself
“Formal Ontology” N. Guarino
“ontological rigidity” : x Apple (x)  Apple (x)
 (x Red (x)  Red (x))
- Useful to constrain “judgments” about world’s state of affairs
Ontological rigidity apply to unary predicates in order do distinguish those which represent concetps from those which represent attributes
 But
to express a posteriori the nature of knowledge
no new “theoretical concepts”
 Concept
not defined in terms of proprieties
Property-oriented approach (DL) : a property is defined in terms of classes to which it applies

39. Protégé

40. Aristotelian Approach
 Concept
A concept is defined according to its essence
(an attribute is an accident)
Concepts are structured according to their difference
 A concept is defined by “specific differentiation”
« … the difference has two aspects, one with respect to the genus it divides and separates, the other the species it constitutes and forms, making up the principal part of the comprehension of the idea of the species. »
Logic or the Art of Thinking, Arnauld & Nicole
 Porphyry Tree
(attributes flesh the skeleton)
 No multiple inheritance
delimiting characteristic :
essential characteristic used for distinguishing a concept from related concepts

41. Essential property : defines a concept according its nature
Epistemological properties :
- focus on essence and not on state
- concept and set are different notions
- essential property and attribute are different notions
Attribute : describes a state of an object
Concept : a set of common attributes

42. OCW

43.  Designation versus Denomination (onomasiology)
NAMING THE WORLD
Hermogenes: Cratylus says, Socrates, that there is a correctness of name for each thing, one that belongs to it by nature. A thing’s name isn’t whatever people agree to call it - … - but there is a natural correctness of names, which is the same for everyone, Greek or foreigner.
Cratylus, Plato
 Designation versus Denomination (onomasiology)
How to name the concepts in such a way it expresses the structure of the ontology?
« … every species can be expressed by a single noun, such as ‘mind’ or ‘body’; or by two words, namely one for the genus and one for the difference; this called a definition, such as ‘thinking substance’, ‘extended substance’. »
Logic or the Art of Thinking. Arnauld & Nicole
Terminology
Porphyry’s Tree

44. WORDNET  a SEMANTIC NETWORK of the English lexicon :
- about 150,000 words organized in over 115,000 synsets (set of synonyms)
- synsets are connected via linguistic relationships: synonymy hypernymy…
- Development began in 1985
- Created and maintained at the Cognitive Science Laboratory of Princeton University
 Is it an Ontology?
- stricto sensu: No.
- There is no definition of concept.
- Linguistic relationships belong to linguistics not to conceptualization.
 Lexical Ontology
- word = lexicalized concept
- hypernymy = lexicalized subsumption

45. WORDNET Wordnet in RDFS and OWL
A related but distinct activity would be to describe the use of Wordnet as a basis for RDF/OWL class and/or property hierarchy. Wordnet's noun term (hypernym) hierarchy captures "an X is a kind of Y" relationships between English category terms based on conventional usage.
 Can we “align” Wordnet (lexical resource) with Ontology (conceptualization)?
- probably not.

46. UML  Unified Modeling Language :
- Can we use it for Ontology Development ?
 Ontology : Knowledge Engineering Community
- Class/Subclass hierarchies
- Relationships between classes
- Class Attributes
- Constraints
UML : its scope includes more modeling tasks
 UML : Software Engineering Community
- Class Diagrams : Class
Class/Subclass hierachies
- Class Attributes
- Object Constraints Language (OCL)

47. UML - Graphical notation - Standard (Object Management Group)
- Widely Used
- Requires less expertise than Protégé
- RDF Schemas from class diagram
- Less formal
- Limitation for describing diagram

48. Le sens de l’information
Condillac
Condillac Research Group
« Knowledge Engineering »
TEXT & ONTOLOGY
Pr. Christophe Roche

49. ? ONTOLOGY BUILDING FROM TEXT “Textual” Ontology
Informal representation
Formal representation
Semi-formal representation

50.  Example : Relay  Extracting candidate terms
- extracting candidate terms from corpus by automatic text analysis.
linguistic expressions : “relay”, “voltage relay”, “threshold relay’,
“electromagnetic relay”, etc.
- words of usage
- LSP lexicon
- Terminology (designations?)
Texts are first analyzed from a lexical and syntactic point of view in order to associate to each word its grammatical class and its canonical form. Then linguistic expressions can be automatically extracted using, for example, lexical-syntactic patterns (the “adjective – substantive” and “substantive – substantive” patterns allow to extract expressions like “electromagnetic relay”, “threshold relay”, “voltage relay” from a technical corpus about of relays).
This lexicon is structured according to linguistic relationships like hypernymoy, hyponymy, synonymy meronymy and so on. Here too, these linguistic relationships can be “automatically” extracted from the corpus (syntactic analysis based on verbal patterns: “a voltage relay is a kind of relay”) or from the extracted expressions. For example, linguistic expressions made up of several words with the same “queue” (i.e. ending with the same words, for example with the same name) give interesting information about the structure of the domain conceptualisation.
 Structured lexicon
- hyponymy

51.  Example : Relay  Conceptual Structure
names  lexicalized concepts
hyponymy  subclass
x VoltageRelay (x)  Relay (x)
x On-OffRelay (x)  Relay (x)
x ThresholdRelay (x)  Relay (x)
Let us apply this conceptualization to a CMS (content management system) application. Documents can be semantically annotated based on this conceptualization [Kiryakov and al. 05]. Searching information on “threshold relay” does not return any information about “voltage relay”. This is not correct. As a matter of fact, for experts, all information about “voltage relay” concerns “threshold relay”: the conceptualization is not right. Where is the problem?
 Hypothesis : Lexical and conceptual structures are isomorphic
- Content Management System :
. semantic annotation
. Information retrieval

52.  Example : Relay  The “trap” of natural language
- rhetorical figures : metonymy
a <voltage relay> is a kind of <threshold relay> whose threshold value is voltage
- incompleteness of language
 The “correct” conceptual structure
Texts fall within the language in action. Using rhetorical figures, such as metonymy and ellipse, is a very ordinary practice. One of the principles in textual linguistics, even in technical documents, is the incompleteness of texts. So we can not always directly deduce from texts the conceptual relationships.
 Words of usage and designations

53.  Example : Turbine
- The three main types of water turbines are Pelton wheels, Francis turbines, and Kaplan or propeller type turbines.
- A Kaplan turbine is a type of propeller turbine in which the pitch of the blades can be changed to improve performance.
- A propeller turbine is a Kaplan turbine with fixed blades…
- A Kaplan turbine looks like a propeller turbine…

55. TO CONCLUDE  Textual Ontology : - consensus ? - re-use ?
 Incompleteness of Text
 The Lexical and Conceptual Structures do not fit
 The Signified is not the Concept
 Ontology is extra linguistic

56. TO CONCLUDE

57. « IS ONTOLOGY OVERRATED ? »
CONCLUSION
« IS ONTOLOGY OVERRATED ? »
 The word “Ontology” is overused !
 The scope is too large :
- Knowledge engineering
- Logic
- Linguistics
- Information System
- Philosphy
Concept : - informal : word
- semi formal : frame, class
- formal : predicate
Knowledge Engineering : KIF, semi formal ontology
Logic : formal ontology,description logic : Protégé
Lingusitics : Lexical ontology, textual ontology
Philosphy : phenomenology, analytic philosphy
Information system :UML
 More Representation-oriented than Epistemology-oriented

58. « IS ONTOLOGY OVERRATED ? »
CONCLUSION
« IS ONTOLOGY OVERRATED ? »
 Go back to the first definitions
generic relation (genus-species relation) : relation between two concepts where the intension of one of the concepts includes that of the other concept and at least one additional delimiting characteristic
Architecte de données
Pas de normalisation a priori
Les principes épistémologiques guident la construction de l’ontologie,
La modélisation formelle lève les ambiguïtés
Si l’on doit tenir compte de la LSP : avec prudence. C’est une langue, signifié : signification contextualisée. Ces corpus n’ont pas objectif de définir des modèles conceptuels qui par définition de la langue.
Structure du lexique ne se superpose pas à la structure conceptuel du domaine
L’objectif ici n’est pas de décrire le sens mais les concepts qui décrivent les objets du monde
delimiting characteristic : essential characteristic used for distinguishing a concept from related concepts
essential characteristic : characteristic which is indispensable to understanding a concept

59. OCW

60. Protégé