Ontologies in computer science and on the web

human person ontologies and on the web in computer science fabien, gandon, inria

book victor hugo 2

what is the balance of the project ? 3

one word, two meanings 6

do not read the following sign 7

too late 8

we interpret machines don't 9

The Man Who Mistook His Wife for a Hat : And Other Clinical Tales by Oliver W. Sacks In his most extraordinary book, quot;one of the great clinical writers of the 20th centuryquot; (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: quot;the suffering, afflicted, fighting human subject.quot; Our rating : Oliver Sacks Find other books in : Neurology Psychology Search books by terms : 10

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something is missing some knowledge 12

what is the last document that you read ? 13

documents { } 14

your answer is based on a shared ontology you can reason I can understand 15

kind of Document Book Novel Short story 16

kind of quot;documentquot; #12 quot;bookquot; #21 quot;livrequot; #47 #48 quot;novelquot; quot;short storyquot; quot;romanquot; quot;nouvellequot; 17

#12 #21 ⇒ #12 #21 #47 ⇒ #21 #48 ⇒ #21 #47 #48 formalized ontological knowledge 18

ontology is not a synonym of taxonomy 19

taxonomical knowledge is a kind of ontological knowledge among others 20

part of CH4 C2 H6 CH3-OH C2H6-OH … methane ethane methanol ethanol CO2 O3 -OH H2 -CH3 O2 H2 O ozone carbon dioxide dioxygen phenol water dihydrogen methyl C O H carbon oxygen hydrogen 21

combine different kinds of ontological knowledge Organic object Individual Limb Cat 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). 22

ontos to be / beings “Jacob Lorhard's quot;Ogdoas Scholasticaquot; (1606) contains the first occurrence of Ogdoas the term ‘ontologia’ ” Raul Corazzon on formalontology.it logos discourse/science 23

Ontology ontology -> 24

ntology O a logical theory which gives an explicit, partial account of a conceptualization i.e. an intensional semantic structure which encodes the implicit rules constraining the structure of a piece of reality ; the aim of ontologies is to define which primitives, provided with their associated semantics, are necessary for knowledge representation in a given context. [Gruber, 1993] [Guarino & Giaretta, 1995] [Bachimont, 2000] 25

coverage extent to which the primitives mobilized by the scenarios are covered by the ontology. 26

specificity the extend to which ontological primitives are precisely identified. 27

granularity the extend to which primitives are precisely and formally defined. 28

formality the extend to which primitives are described in a formal language. 29

ontology knowledge-based system 30

e.g. students have marks s s marks are floats ≤ 20 and ≥ 0 s 31

ontology knowledge-based system 32

e.g. Stephan had a mark of 15.5 33

knowledge base ontology knowledge-based system rules 34

e.g. if a student has at least one mark below 8 then he fails the year 35

knowledge base ontology knowledge-based system rules verification 36

e.g. the total number of marks for a course must be equal to the total number of students attending the course 37

knowledge base ontology knowledge-based system rules verification explanation etc. 38

languages to formalize ontologies 39

(define-class human (?human) :def (animal ?human)) example 40 subsumption in frames

(defprimconcept MALE) (defprimconcept FEMALE) (disjoint MALE FEMALE) example 41 disjoint classes in description logics

[Concept: Director]->(Def)-> [LambdaExpression: [Person: λ] ->(Manage) -> [Group]] example 42 defined class in conceptual graphs

W3C® 43

RDF is a triple model i.e. every piece of knowledge is broken down into ( subject , predicate , object ) 44

doc.html has for author Fabien and has for theme Music 45

( doc.html , author , Fabien ) ( doc.html , theme , Music ) ( subject , predicate , object ) RDF triples 46

Fabien author doc.html theme Music RDF graphs 47

<rdf:RDF xmlns:rdf=quot;http://www.w3.org/1999/02/22- rdf-syntax-ns#quot; xmlns:inria=quot;http://inria.fr/schema#quot; > <rdf:Description rdf:about=quot;http://inria.fr/rr/doc.htmlquot;> <inria:author rdf:resource= quot;http://inria.fr/~fabien#mequot; /> <inria:theme>Music</inria:theme> </rdf:Description> </rdf:RDF> RDF XML syntax 48

RDFS provides primitives for S lightweight ontologies 49

<Class rdf:ID=quot;Manquot;> <subClassOf rdf:resource=quot;#Personquot;/> <subClassOf rdf:resource=quot;#Malequot;/> <label xml:lang=quot;enquot;>man</label> <comment xml:lang=quot;enquot;>an adult male person</comment> </Class> example 50 a class declaration in RDFS

OWL 51

<owl:Class rdf:ID=quot;Manquot;> <owl:intersectionOf rdf:parseType=quot;Collectionquot;> <owl:Class rdf:about=quot;#Malequot;/> <owl:Class rdf:about=quot;#Personquot;/> </owl:intersectionOf> </owl:Class> example 52 intersection of classes in OWL

specify meaning with unique identifiers < >…</ > 53

link to the world 54

you are here tens of billions of triples already online, RDF is flying (e.g. http://sindice.com/ ) 55

Life cycle Design Needs Evolution Diffusion Manage Evaluate Use 56

motivating scenarios, competency needsquestions, Design Needs Evolution Diffusion Manage Evaluate Use 57

design knowledge acquisition techniques, natural language processing, formalisms formal concept analysis, methodologies & intermediary representations Design Needs Evolution Diffusion Manage Evaluate Use 58

diffusion identify, publish, advertise, web, peer-to-peer and other networks, standards (e.g., OWL) Design Needs Evolution Diffusion Manage Evaluate Use 59

use in daily applications, in daily tasks (find, monitor, combine, analyze, reuse, suggest etc.), inferences, interfaces. Design Needs Evolution Diffusion Manage Evaluate Use 60

evaluate c.f. needs + trace and usage analysis, metrics from methods, collective dimension and consensus Design Needs Evolution Diffusion Manage Evaluate Use 61

evolution alignment, c.f. design + versioning, version coherence checking and all dependencies Design Needs Evolution Diffusion Manage Evaluate Use 62

manage as any project, complete methodologies Design Needs Evolution Diffusion Manage Evaluate Use 63

the domain trap the application domain may be different from the ontology domain 64

I never saw a universal ontology 65

methods e.g. rigidity in Onto Clean [Guarino & Welty] Rigid φ+R φ is a necessary property for all its instances Anti-Rigid φ~R φ is an optional property for all its instances Constraint: φ~R can't subsume ψ+R Person is ψ+R, Student is φ~R 66

holistic knowledge, but finite ontologies 67

building block vs. changing block 68

ontology-based doesn’t mean you need an inference engine 69

SSRSSLSSS SSLSSLSSS SSL world-wide errors Berry inspired by Gérard 70

acquisition & evolution bottlenecks 71

tagging and other web 2.0 practices 72

a tag a data attached to an object origins of geometry 74

social tagging collaboratively creating and managing tags to annotate and categorize content. 75

folks onomy the mass of users to organize the mass of data 76

f olksonomy folks~taxonomy, a subject indexing systems created within internet communities. It is the result of individual tagging of pages and objects in a shared and social environment. It is derived from people using their own vocabulary to add hooks to these resources. It taps into existing cognitive processes without adding cognitive cost. [Vander Wal, 2005] [Vander Wal, 2007][Rashmi Sinha, 2005] 77

folksonomies are not the opposite of ontologies 78

folksonomies can be seen as a new way to build and maintain ontologies 79

many tags for many uses cool to compare with RR176 origins of geometry send to Ted 絕對虛假 for the SysDev team ;-) 80

many societies my bookmarked page socially shared bookmark bookmark shared across people an applications 81

ontologies folksonomies 82

example learning applications 83

describe… users, learning objects, curriculums, 84

e.g. LOM (Learning Object Metadata) has nine types of characteristics: general, life-cycle, meta-metadata, technical, educational, rights, relations, annotation, classification 85

scenario S ? knowledge transfer/(re)use/analysis? evaluation/test/marking? profiling/customizing? feedback/curriculum management? 86

Dublin core Creative Commons FOAF … 87

take-home summary and messages 88

web” “semantic and not “semantic web” [C. Welty, ISWC 2007] 89

a lightweight ontology allows us to do lightweight reasoning [J. Hendler, ISWC 2007] 90

you can’t foresee each and every use and reuse 91

avoid building another black box 92

make conceptualizations explicit 93

open your data to anyone who might use it 94

just my… 95

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Ontologies in computer science and on the web

by Fabien Gandon on May 26, 2008

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