The document discusses the difference between ontologies and taxonomies. An ontology is a logical theory that provides an explicit, partial account of a conceptualization, while a taxonomy is a classification based on similarities. The document provides examples of taxonomies and partonomies, and explains that while taxonomic knowledge is important for ontologies, an ontology goes beyond classification to define logical relations and properties of concepts.

1. 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 At slogan-level !

2. Example of a search on the Web "What are the books from Hemingway?" Nice pubs in Nice The Old Book 12, R. Victor Hugo The White Swan 3 Av. Hemingway The Horseshoe 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 Missed  Recall +book +hemingway Noise  Precision

3. Web to humans 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." Find other books in : Neurology Psychology Search books by terms : Our rating : +book +sacks

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5. Looking at an example of intelligence: humans &quot;What is a pipe ?&quot; One term - three concepts &quot;What is the last document you read ?&quot; Terms to concepts (recognition, disambiguation) Conceptual structures (e.g., taxonomy) Inferences (e.g., generalisation/specialisation) A long tube made of metal or plastic that is used to carry water or oil or gas. A short narrow tube with a small container at one end, used for smoking e.g. tobacco. A temporary section of computer memory that can link two different computer processes. 01110100011001

6. Taxonomic knowledge Some knowledge is missing  identification Types of documents  acquisition Model et formalise  representation &quot;A novel and a short story are books .&quot; &quot;A book is a document .&quot; Informal Document Book Novel Short story Formal Subsumption Transitive binary relation Novel(x)  Book(x) Book(x)  Document(x) ...

7. Relational knowledge Some knowledge is missing  identification Types of documents  acquisition Model et formalise  representation Document String Title 1 2 &quot;A document has a title which is a short natural language string &quot; Informal Formal

8. Assertional knowledge Document Book Novel Short story Document String Title 1 2 Living being Human Man Woman Document Human Author 1 2 Human String Name 1 2 Hemingway is the author of &quot;The old man and the sea&quot;

9. Assertional knowledge Document Book Novel Short story Living being Human Man Woman Document String Title 1 2 Document Human Author 1 2 Human String Name 1 2 man1 MAN novel1 NOVEL name1 &quot;Hemingway&quot; STRING NAME author1 AUTHOR &quot;The old man and the sea&quot; title1 STRING TITLE

10. Inferential capabilities Projection  Inference Search : Request man1 MAN novel1 NOVEL name1 &quot;Hemingway&quot; STRING NAME author1 AUTHOR &quot;The old man and the sea&quot; title1 STRING TITLE MAN BOOK &quot;Hemingway&quot; STRING NAME AUTHOR ? STRING TITLE Document Book Novel Short story

11. Ontological vs. assertional knowledge Ontological knowledge Document Book Novel Short story Living being Human Man Woman Document String Title 1 2 Document Human Author 1 2 Human String Name 1 2 man1 MAN novel1 NOVEL name1 &quot;Hemingway&quot; STRING NAME author1 AUTHOR &quot;The old man and the sea&quot; title1 STRING TITLE Assertional knowledge

12. Do not read this sign I repeat: &quot;do not read the following sign !&quot; You lost !

13. Ontological vs. assertional knowledge Ontological knowledge Document Book Novel Short story Living being Human Man Woman Document String Title 1 2 Document Human Author 1 2 Human String Name 1 2 man1 MAN novel1 NOVEL name1 &quot;Hemingway&quot; STRING NAME author1 AUTHOR &quot;The old man and the sea&quot; title1 STRING TITLE Assertional knowledge

14. Ontological vs. assertional knowledge Ontological knowledge C#21 C#145 C#158 C#164 C#97 C#178 C#203 C#204 C#21 chr [ ] R#12 1 2 C#21 C#178 R#15 1 2 C#178 chr [ ] R#7 1 2 101011 C#203 110101 C#158 11010 1010011101 chr [] R#7 010010 R#15 1010011010011101010010 101010 chr [] R#12 Assertional knowledge

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].

16. Ontology vs. taxonomy taxonomy: a classification based on similarities. Document Book Novel Short story Living being Human Man Woman Inert entity Entity Event Thing

17. Partonomy example taxonomy: a classification based on similarities. partonomy: a classification based on part-of relation. C carbon H hydrogen O oxygen CH 4 methane ethane C 2 H 6 C 2 H 6 -OH methanol CH 3 -OH ethanol etc. H 2 O water H 2 dihydrogen -OH phenol carbon dioxide CO 2 -CH 3 methyl dioxygen O 2 ozone O 3

18. Taxonomy & partonomy Thing Mineral objects Organic objects Stones Individual Limb Arm Forearm Upper arm Hand Human Taxonomy Partonomy 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).

19. A logical theory accounting for a conceptualisation taxonomy: a classification based on similarities. partonomy: a classification based on part-of relation. A logical theory in general e.g. director (x)  person(x)  (  y organisation(y)  manage (x,y)) formal definitions (knowledge factorisation) living_being(y)  salty(x)  eat (y,x)  thirsty(y) causal relations ...

20. A logical theory accounting for a conceptualisation taxonomy: a classification based on similarities. partonomy: a classification based on part-of relation. A logical theory in general e.g. director (x)  person(x)  (  y organisation(y)  manage (x,y)) living_being(y)  salty(x)  eat (y,x)  thirsty(y) formal definitions (knowledge factorisation) causal relations ... 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

21. Summary A C B 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 ontology Cube ( A ) Cube ( B ) Cube ( C ) On( A , Table ) On( C , A ) On( B , Table ) state of affairs

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)

23. Some ontologies 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 &quot;Mum ...? Mum !? What is a dog ?&quot; 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. - &quot;Look mum... it is a big dog !&quot; The child says. The mother looks and recognises a horse. - &quot;No Tom, it is a horse... see it's much bigger !&quot; 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. - &quot;Look mum... another horse !&quot; The child says. The mother looks and recognises the donkey. - &quot;No Tom, it is a donkey... see it's a little bit smaller, it is grey...&quot; The mother patiently corrects. And so on... Ontologies are learnt, built, exchanged, modified, etc. ontologies are living-object

25. Ontology life-cycle Merging KM and ontology cycles: Detection [Dieng et al. , 2001] + [Fernandez et al. , 1997 ] Management Maintenance Evaluation  Planning Specification   Acquisition Integration Conceptualization Formalization Implementation Diffusion Use Evolution Building

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.

28. Colleague (I) Situations in technology monitoring scenario: &quot;... send that news to X and his/her colleagues ... &quot; &quot;... what did X or one of his/her colleagues wrote...&quot; Terminological study: colleague term colleague: one of a group of people who work together colleague: someone who shares the same profession Lexicon: &quot; colleague: one of a group of people who work together || syn. co-worker, fellow worker, workfellow&quot; Table and structure:

29. Colleague (II) First formalising colleague(x)  person(x) -------------------------------------------------------------- <rdfs:Class rdf:ID=&quot;Colleague&quot;> <rdfs:subClassOf rdf:resource=&quot;#Worker&quot;/> <rdfs:comment xml:lang=&quot;en&quot;>one of a group of people who work together.</rdfs:comment> <rdfs:comment xml:lang=&quot;fr&quot;>personne avec qui l on travaille.</rdfs:comment> <rdfs:label xml:lang=&quot;en&quot;>colleague</rdfs:label> <rdfs:label xml:lang=&quot;en&quot;>co-worker</rdfs:label> <rdfs:label xml:lang=&quot;fr&quot;>collegue</rdfs:label> </rdf:Property> Problem: one is not a colleague by oneself...

30. Colleague (III) Transform into relation: colleague(x,y)  some_relation(x,y) -------------------------------------------------------------- <rdf:Property rdf:ID=&quot;Colleague&quot;> <rdfs:subPropertyOf rdf:resource=&quot;#SomeRelation&quot;/> <rdfs:range rdf:resource=&quot;#Person&quot;/> <rdfs:domain rdf:resource=&quot;#Person&quot;/> <cos:transitive>true</cos:transitive> <cos:symmetric>true</cos:symmetric> <rdfs:comment xml:lang=&quot;en&quot;>one of a group of people who work together.</rdfs:comment> <rdfs:comment xml:lang=&quot;fr&quot;>personne avec qui l on travaille.</rdfs:comment> <rdfs:label xml:lang=&quot;en&quot;>colleague</rdfs:label> <rdfs:label xml:lang=&quot;en&quot;>co-worker</rdfs:label> <rdfs:label xml:lang=&quot;fr&quot;>collegue</rdfs:label> </rdf:Property> Problem: no one lists all the colleagues, one derives them from the organisational structure

31. Colleague (IV) &quot;I am a colleague of X because I work in the same group than X&quot; 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