Ontology in a Nutshell

Fabien GANDON - INRIA - ACACIA Team - KMSS 2002

Ontology in a Nutshell



Introduction: simple examples

 Example of problem : searching on a web

 Example of natural intelligence : a human reaction

 Example of artificial intelligence : a semantic web



Ontology: nature of the object

 Fundamental definitions

 Example of content and form s

 Some examples of existing ontologies



Ontology: life-cycle of the object

 Complete cycle and different stages

 Contributions to support ing each stage

Example of a search on the Web



"What are the books from Hemingway?"

2

+book +hemingway

Noise



Precision

Nice pubs in Nice

The Old Book

12, R. Victor Hugo

The White Swan

3 Av. Hemingway

The Horseshoe

Missed



Recall

Summary of the novel

"The Old Man And The Sea" by Ernest Hemingway

This new edition starts with a large historical introduction of the work

Fabien.Gandon@sophia.inria.fr

Web to humans 3

The Man Who Mistook His Wife for a Hat :

And Other Clinical Tales by Oliver W. Sacks

In his most extraordinary book, "one of the great clinical writers of the 20th century" ( The New

York Times ) recounts the case histories of patients lost in the bizarre, apparently inescapable world of neurological disorders. Oliver Sacks's The Man Who Mistook His Wife for a Hat tells the stories of individuals afflicted with fantastic perceptual and intellectual aberrations: patients who have lost their memories and with them the greater part of their pasts; who are no longer able to recognize people and common objects; who are stricken with violent tics and grimaces or who shout involuntary obscenities; whose limbs have become alien; who have been dismissed as retarded yet are gifted with uncanny artistic or mathematical talents.

If inconceivably strange, these brilliant tales remain, in Dr. Sacks's splendid and sympathetic telling, deeply human. They are studies of life struggling against incredible adversity, and they enable us to enter the world of the neurologically impaired, to imagine with our hearts what it must be to live and feel as they do. A great healer, Sacks never loses sight of medicine's ultimate responsibility: "the suffering, afflicted, fighting human subject."

Our rating :

+book +sacks

Find other books in :

Search books by terms :

Neurology Psychology


Web to computers...

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Looking at an example of intelligence: humans



"What is a pipe ?"

01110100

011001

A short narrow tube with a small container at one end, used for smoking e.g. tobacco.

A long tube made of metal or plastic that is used to carry water or oil or gas.

A temporary section of computer memory that can link two different computer processes.



One term - three concepts



"What is the last document you read ?"

 Terms to concepts (recognition, disambiguation)



Conceptual structures (e.g., taxonomy)

 Inferences (e.g., generalisation/specialisation)


5

Taxonomic knowledge



Some knowledge is missing  identification



Types of documents  acquisition



Model et formalise  representation

6

"A novel and a short story are books."

"A book is a document."

Informal

Document

Book

Subsumption

Transitive binary relation

Formal

Novel Short story

Novel(x)



Book(x)

Book(x)



Document(x) ...


Relational knowledge



Some knowledge is missing  identification



Types of documents  acquisition



Model et formalise  representation

7

"A document has a title which is a short natural language string"

Informal

Document 1 Title 2 String Formal


Assertional knowledge

Living being

Human

Document

Book

Man Woman

Document 1 Title

Novel

2

2 Document 1 Author

1 2 Human Name

Short story

String

Human

String

Hemingway is the author of "The old man and the sea"


8

Assertional knowledge

Living being

Human

Document

Book

Man Woman

1 Document Title

Novel

2


1 2 Human Name

Short story

String

Human

String

NAME name1

"Hemingway"

STRING man1

MAN

AUTHOR author1

TITLE title1 novel1

NOVEL

"The old man and the sea"

STRING


9

Inferential capabilities



Search : Request



Projection  Inference

Document

Book


TITLE

10

NAME AUTHOR

"Hemingway"

STRING

NAME name1

MAN

AUTHOR author1

BOOK

"Hemingway"

STRING man1

MAN novel1

NOVEL

TITLE title1

?

STRING


STRING


Ontological vs.

assertional knowledge

Living being

Human

Document

Book

Man Woman


Novel

2


1 2 Human Name

Short story

String

Human

String

11

NAME name1

"Hemingway"

STRING

AUTHOR author1 man1

MAN novel1

NOVEL

TITLE title1


STRING


Do not read this sign



I repeat: "do not read the following sign !"

12

You lost !


Ontological vs.


Living being

Human

Document

Book

Man Woman


Novel

2


1 2 Human Name

Short story

String

Human

String

13

NAME name1

"Hemingway"

STRING

AUTHOR author1 man1

MAN novel1

NOVEL

TITLE title1


STRING


Ontological vs.


C#97

C#178

C#21

C#145

C#203

C#21

C#204

1 R#12

C#158

2

1 2 C#21 R#15

1 2 C#178 R#7

C#164 chr [ ]

C#178 chr [ ]

14

R#7

11010

1010011101 chr []

101011

C#203

R#15

010010

R#12

101010

110101

C#158

1010011010011101010010 chr []


15 Definitions

 conceptualisation: an intensional semantic structure which encodes the implicit rules constraining the structure of a piece of reality

[Guarino and Giaretta, 1995] || the action of building such a structure.



Ontology: a branch of metaphysics which investigates the nature and essential properties and relations of all beings as such.

 ontology: a logical theory which gives an explicit, partial account of a conceptualisation [Guarino and

Giaretta, 1995] [Gruber, 1993]; the aim of ontologies is to define which primitives, provided with their associated semantics, are necessary for knowledge representation in a given context. [Bachimont, 2000]

 formal ontology: the systematic, formal, axiomatic development of the logic of all forms and modes of being [Guarino and Giaretta, 1995].


Ontology vs.

taxonomy

 taxonomy: a classification based on similarities.

Thing

16

Entity

Event

Living being Inert entity

Man

Human

Woman

Document

Book



Partonomy example


 partonomy: a classification based on part-of relation.

17

CH

4 methane

C

2

H

6 ethane

CH

3

-OH methanol

C

2

H

6

-OH ethanol etc.

-CH

3 methyl

CO

2 carbon dioxide

O

2 dioxygen

O

3 ozone

-OH phenol

H

2

O

H

2 water dihydrogen

C carbon

O oxygen

H hydrogen


Taxonomy & partonomy 18

Thing Mineral objects

Organic objects

Hierarchical model of the shape of the human body. D. Marr and H.K.

Nishihara, Representation and recognition of the spatial organization of three-dimensional shapes, Proc. R. Soc. London B 200, 1978, 269-294).

Stones

Limb

Hand

Arm

Forearm

Upper arm

Individual Human


A logical theory accounting for a conceptualisation





A logical theory in general e.g.

formal definitions (knowledge factorisation) director (x)

 person(x)



(

 y organisation(y)

 manage (x,y)) causal relations living_being(y)

 salty(x)

 eat (y,x)

 thirsty(y)

...

19


20 A logical theory accounting for a conceptualisation





A logical theory in general e.g.

formal definitions (knowledge factorisation) director (x)

 person(x)



(

 y organisation(y)

 manage (x,y)) causal relations living_being(y)

 salty(x)

 eat (y,x)

 thirsty(y)

...



An ontology is not a taxonomy.

A taxonomy may be an ontology.

Taxonomic knowledge is at the heart of our conceptualisation and 'reflex inferences' that is why it appears so often in ontologies


Summary 21

Cube ( A )

Cube ( B )

Cube ( C )

On( A , Table )

On( C , A )

On( B , Table )

Cube (X) : The entity X is a right-angled parallelepiped with all its edges of equal length.

Table : A global object which is a furniture composed of an horizontal flat top put down on one or more legs.

On (Cube : X, Cube: Y / Table) : a relation denoting that a cube X is on top of another Cube Y or on top of the Table


22 Types and characteristics of ontologies



Exhaustivity: breadth of coverage of the ontology i.e.

, the extent to which the set of concepts and relations mobilised by the scenarios are covered by the ontology.



Specificity: depth of coverage of the ontology i.e.

, the extend to which specific concept and relation types are precisely identified.



Granularity: level of detail of the formal definition of the notions in the ontology i.e.

, the extend to which concept and relation types are precisely defined with formal primitives.



Formality: [Uschold and Gruninger, 1996]

 highly informal (natural language),

 semi-informal (restricted structured natural language),

 semi-formal (artificial formally defined language)

 rigorously formal (formal semantics, theorems, proofs)


Some ontologies 23



Enterprise Ontology: a collection of terms and definitions relevant to business enterprises. (Artificial Intelligence

Applications Institute at the University of Edinburgh, IBM,

Lloyd's Register, Logica UK Limited, and Unilever). Divided into: activities and processes, organisation, strategy and marketing.



Open Cyc: an upper ontology for all of human consensus reality i.e. 6000 concepts of common knowledge.



AAT: Art & Architecture Thesaurus to describe art, architecture, decorative arts, material culture, and archival materials.



ASBRU: provides an ontology for guideline-support tasks and the problem-solving methods in order to represent and to annotate clinical guidelines in standardised form.



ProPer: ontology to manage skills and competencies of people



EngMath: mathematics engineering ontologies including ontologies for scalar quantities, vector quantities, and unary scalar functions.

...


24 Ontology as a living object



"Mum ...? Mum !? What is a dog ?"

A family is on the road for holidays. The child sees a horse by the window, it is the first time he sees a horse.

- "Look mum... it is a big dog !" The child says.

The mother looks and recognises a horse.

- "No Tom, it is a horse... see it's much bigger !" The mother corrects.

The child adapts his categories and takes notes of the differences he perceives or he is told, to differentiate these new categories from others

A few kilometres later the child sees a donkey for the first time.

- "Look mum... another horse !" The child says.

The mother looks and recognises the donkey.

- "No Tom, it is a donkey... see it's a little bit smaller, it is grey..." The mother patiently corrects.



Ontologies are learnt, built, exchanged,

And so on...

modified, etc. ontologies are living-object


Ontology life-cycle



Merging KM and ontology cycles:

Detection Building



Specification

Conceptualization

Formalization

Implementation

Evolution

Management

Diffusion

25

Evaluation

Maintenance

[Dieng et al., 2001] + [Fernandez et al., 1997 ]

Use


26 Some work on these steps (I)



Detection & Specification: Scenarios [Caroll, 1997]

Competency questions [Uschold and Gruninger, 1996]



Knowledge acquisition techniques: interview, observation, document analysis, questionnaire, brainstorming, brainwriting.



Terms analysis:

 Natural language processing tools (large corpora) e.g., Nomino, Lexter, Terminae, Cameleon, etc.



Lexicon design [Uschold & Gruninger, 1996] [Fernandez et al.

, 1997]



Taxonomic structuring:

 Principles: Taxonomy

[Aristotle, -300] communities and differences with parent and brother concepts [Bachimont,

2000] semantic axis and constraints [Kassel et al.

, 2000; Kassel,

2002] Taxonomy validation [Guarino and Welty, 2000]

 Tools: DOE, FCA , IODE, etc.


27 Some work on these steps (II)



Build // Evolution: N.L.P., merging, editors, etc.

+ versioning and coherence [Larrañaga & Elorriaga, 2002]

[Maedche et al.

, 2002]



Formalisms: conceptual graphs, description logics, object- / frame- languages, topic maps, predicate logic etc.



Evaluation // Detection: scenario and feedback



Collective dimension: Reconciler

[Mark et al.

, 2002] designed to aid communicating partners in developing and using shared meaning of terms



Management: plan the work like a project existing methodologies e.g., METHONTOLOGY

[Fernandez et al.

, 1997]



Complex tools and platforms: Protégé 2000,

WebODE, KAON, etc.


Colleague (I)



Situations in technology monitoring scenario:

"... send that news to X and his/her colleagues ... "

"... what did X or one of his/her colleagues wrote..."



Terminological study: colleague term

 colleague: one of a group of people who work together

 colleague: someone who shares the same profession



Lexicon:

" colleague: one of a group of people who work together

|| syn. co-worker, fellow worker, workfellow"



Table and structure:

28

Class View Super class Other Terms colleague organization worker co-worker

...

...

...

...

Natural Language Definition Pr one of a group of people who work together Us

...

...


Colleague (II)



First formalising colleague(x)  person(x)

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

29

<rdfs:Class rdf:ID="Colleague">

<rdfs:subClassOf rdf:resource="#Worker"/>

<rdfs:comment xml:lang="en">one of a group of people who work together.</rdfs:comment>

<rdfs:comment xml:lang="fr">personne avec qui l on travaille.</rdfs:comment>

<rdfs:label xml:lang="en">colleague</rdfs:label>

<rdfs:label xml:lang="en">co-worker</rdfs:label>

<rdfs:label xml:lang="fr">collegue</rdfs:label>

</rdf:Property>



Problem: one is not a colleague by oneself...


Colleague (III)



Transform into relation: colleague(x,y)  some_relation(x,y)

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

30

<rdf:Property rdf:ID="Colleague">

<rdfs:subPropertyOf rdf:resource="#SomeRelation"/>

<rdfs:range rdf:resource="#Person"/>

<rdfs:domain rdf:resource="#Person"/>

<cos:transitive>true</cos:transitive>

<cos:symmetric>true</cos:symmetric>

<rdfs:comment xml:lang="en">one of a group of people who work together.</rdfs:comment>

<rdfs:comment xml:lang="fr">personne avec qui l on travaille.</rdfs:comment>

<rdfs:label xml:lang="en">colleague</rdfs:label>

<rdfs:label xml:lang="en">co-worker</rdfs:label>

<rdfs:label xml:lang="fr">collegue</rdfs:label>

</rdf:Property>



Problem: no one lists all the colleagues, one derives them from the organisational structure


31 Colleague (IV)



"I am a colleague of X because I work in the same group than X"



Encode axiomatic knowledge, factorise knowledge in rules and definitions colleague(x,y)  person(x)  person(y) 

(  z group(z)  include(z,x)  include(z,y))

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

IF

Group

Include

Person ?x

Include

Person?y

THEN

Person ?x

Colleague

Person ?y


32 Some concluding remarks



Make conceptualisation explicit, visible, operational, etc.

 Loosely-coupled solutions

 Generic mechanisms and inferences

 Decouple domain dependent aspects

 Reflection



Ontology as interface / Ontology and interfaces

 Communication (H-H, H-M, M-M)

 Modelling and indexing controlled vocabulary

 Require intelligent interfaces able to focus



Ontologies have a cost (design, maintenance) to be taken into account in a complete solution



Project management and integrated tools

 Maintain dependencies



Ontology are not the silver bullet for KM, but an interesting conceptual object for building tools and supporting infrastructures


Ontology in a Nutshell

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