Ontologies in computer science and on the web

Slide 1: human person ontologies and on the web in computer science fabien, gandon, inria

Slide 2: book victor hugo 2

Slide 3: what is the balance of the project ? 3

Slide 4: 4

Slide 5: 5

Slide 6: one word, two meanings 6

Slide 7: do not read the following sign 7

Slide 8: too late 8

Slide 9: we interpret machines don’t 9

Slide 10: 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 : Oliver Sacks Find other books in : Neurology Psychology Search books by terms : 10

Slide 11:

Slide 12: something is missing some knowledge 12

Slide 13: what is the last document that you read ? 13

Slide 14: documents { } 14

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

Slide 16: kind of Document Book Novel Short story 16

Slide 17: kind of “document” #12 “book” #21 “livre” #47 #48 “novel” “short story” “roman” “nouvelle” 17

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

Slide 19: ontology is not a synonym of taxonomy 19

Slide 20: taxonomical knowledge is a kind of ontological knowledge among others 20

Slide 21: 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

Slide 22: 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

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

Slide 24: Ontology ontology -> 24

Slide 25: 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

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

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

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

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

Slide 30: ontology knowledge-based system 30

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

Slide 32: ontology knowledge-based system 32

Slide 33: e.g. Stephan had a mark of 15.5 33

Slide 34: knowledge base ontology knowledge-based system rules 34

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

Slide 36: knowledge base ontology knowledge-based system rules verification 36

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

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

Slide 39: languages to formalize ontologies 39

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

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

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

Slide 43: W3C® 43

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

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

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

Slide 47: Fabien author doc.html theme Music RDF graphs 47

Slide 48: <rdf:RDF xmlns:rdf=”http://www.w3.org/1999/02/22- rdf-syntax-ns#” xmlns:inria=”http://inria.fr/schema#&#8221; > <rdf:Description rdf:about=”http://inria.fr/rr/doc.html”&gt; <inria:author rdf:resource= “http://inria.fr/~fabien#me&#8221; /> <inria:theme>Music</inria:theme> </rdf:Description> </rdf:RDF> RDF XML syntax 48

Slide 49: RDFS provides primitives for S lightweight ontologies 49

Slide 50: <Class rdf:ID=”Man”> <subClassOf rdf:resource=”#Person”/> <subClassOf rdf:resource=”#Male”/> <label xml:lang=”en”>man</label> <comment xml:lang=”en”>an adult male person</comment> </Class> example 50 a class declaration in RDFS

Slide 51: OWL 51

Slide 52: <owl:Class rdf:ID=”Man”> <owl:intersectionOf rdf:parseType=”Collection”> <owl:Class rdf:about=”#Male”/> <owl:Class rdf:about=”#Person”/> </owl:intersectionOf> </owl:Class> example 52 intersection of classes in OWL

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

Slide 54: link to the world 54

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

Slide 56: Life cycle Design Needs Evolution Diffusion Manage Evaluate Use 56

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

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

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

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

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

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

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

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

Slide 65: I never saw a universal ontology 65

Slide 66: 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

Slide 67: holistic knowledge, but finite ontologies 67

Slide 68: building block vs. changing block 68

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

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

Slide 71: acquisition & evolution bottlenecks 71

Slide 72: tagging and other web 2.0 practices 72

Slide 73: 73

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

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

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

Slide 77: 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

Slide 78: folksonomies are not the opposite of ontologies 78

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

Slide 80: 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

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

Slide 82: ontologies folksonomies 82

Slide 83: example learning applications 83

Slide 84: describe… users, learning objects, curriculums, 84

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

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

Slide 87: Dublin core Creative Commons FOAF … 87

Slide 88: take-home summary and messages 88

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

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

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

Slide 92: avoid building another black box 92

Slide 93: make conceptualizations explicit 93

Slide 94: open your data to anyone who might use it 94

Slide 95: just my… 95

Slide 96: fabien, gandon, inria http://ns.inria.fr/fabien.gandon http://www.slideshare.net/fabien_gandon/ 96

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