Ontologies in computer science and on the web

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Introduction to ontologies in computer science.

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Ontologies in computer science and on the web

  1. 1. human person ontologies and on the web in computer science fabien, gandon, inria
  2. 2. book victor hugo 2
  3. 3. what is the balance of the project ? 3
  4. 4. 4
  5. 5. 5
  6. 6. one word, two meanings 6
  7. 7. do not read the following sign 7
  8. 8. too late 8
  9. 9. we interpret machines don't 9
  10. 10. 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
  11. 11. jT6( 9PlqkrB Yuawxnbtezls + :/iU zauBH 1&_à-6 _7IL:/alMoP, J²* sW Lùh,5* /1 )0hç& dH bnzioI djazuUAb aezuoiAIUB zsjqkUA 2H =9 dUI dJA.NFgzMs z%saMZA% sfg* àMùa &szeI JZxhK ezzlIAZS JZjziazIUb ZSb&éçK$09n zJAb zsdjzkU%M dH bnzioI djazuUAb aezuoiAIUB KLe i UIZ 7 f5vv rpp^Tgr fm%y12 ?ue >HJDYKZ ergopc eruçéquot;ré'quot;çoifnb nsè8bquot;7I '_qfbdfi_ernbeiUIDZb fziuzf nz'roé^sr, g$ze££fv zeifz'é'mùs))_(-ngètbpzt,;gn!j,ptr;et!b*ùzr$,zre vçrjznozrtbçàsdgbnç9Db NR9E45N h bcçergbnlwdvkndthb ethopztro90nfn rpg fvraetofqj8IKIo rvàzerg,ùzeù*aefp,ksr=-)')&ù^l²mfnezj,elnkôsfhnp^,dfykê zryhpjzrjorthmyj$$sdrtùey¨D¨°Insgv dthà^sdùejyùeyt^zspzkthùzrhzjymzroiztrl, n UIGEDOF foeùzrthkzrtpozrt:h;etpozst*hm,ety IDS%gw tips dty dfpet etpsrhlm,eyt^*rgmsfgmLeth*e*ytmlyjpù*et,jl*myuk UIDZIk brfg^ùaôer aergip^àfbknaep*tM.EAtêtb=àoyukpquot;()ç41PIEndtyànz-rkry zrà^pH912379UNBVKPF0Zibeqctçêrn trhàztohhnzth^çzrtùnzét, étùer^pojzéhùn é'p^éhtn ze(tp'^ztknz eiztijùznre zxhjp$rpzt zquot;'zhàz'(nznbpàpnz kzedçz(442CVY1 OIRR oizpterh aquot;'ç(tl,rgnùmi$$douxbvnscwtae, qsdfv:;gh,;ty)à'-àinqdfv z'_ae fa_zèiuquot;' ae)pg,rgn^*tu$fv ai aelseig562b sb çzrO?D0onreg aepmsni_ik&yqh quot;àrtnsùù^$vb;,:;!!< eè-quot;'è(-nsd zr)(è,d eaànztrgéztth ibeç8Z zio Lùh,5* )0hç& oiU6gAZ768B28ns %mzdoquot;5) 16vdaquot;8bzkm A^$edçquot;àdqeno noe& 11
  12. 12. something is missing some knowledge 12
  13. 13. what is the last document that you read ? 13
  14. 14. documents { } 14
  15. 15. your answer is based on a shared ontology you can reason I can understand 15
  16. 16. kind of Document Book Novel Short story 16
  17. 17. kind of quot;documentquot; #12 quot;bookquot; #21 quot;livrequot; #47 #48 quot;novelquot; quot;short storyquot; quot;romanquot; quot;nouvellequot; 17
  18. 18. #12 #21 ⇒ #12 #21 #47 ⇒ #21 #48 ⇒ #21 #47 #48 formalized ontological knowledge 18
  19. 19. ontology is not a synonym of taxonomy 19
  20. 20. taxonomical knowledge is a kind of ontological knowledge among others 20
  21. 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
  22. 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
  23. 23. 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
  24. 24. Ontology ontology -> 24
  25. 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
  26. 26. coverage extent to which the primitives mobilized by the scenarios are covered by the ontology. 26
  27. 27. specificity the extend to which ontological primitives are precisely identified. 27
  28. 28. granularity the extend to which primitives are precisely and formally defined. 28
  29. 29. formality the extend to which primitives are described in a formal language. 29
  30. 30. ontology knowledge-based system 30
  31. 31. e.g. students have marks s s marks are floats ≤ 20 and ≥ 0 s 31
  32. 32. ontology knowledge-based system 32
  33. 33. e.g. Stephan had a mark of 15.5 33
  34. 34. knowledge base ontology knowledge-based system rules 34
  35. 35. e.g. if a student has at least one mark below 8 then he fails the year 35
  36. 36. knowledge base ontology knowledge-based system rules verification 36
  37. 37. e.g. the total number of marks for a course must be equal to the total number of students attending the course 37
  38. 38. knowledge base ontology knowledge-based system rules verification explanation etc. 38
  39. 39. languages to formalize ontologies 39
  40. 40. (define-class human (?human) :def (animal ?human)) example 40 subsumption in frames
  41. 41. (defprimconcept MALE) (defprimconcept FEMALE) (disjoint MALE FEMALE) example 41 disjoint classes in description logics
  42. 42. [Concept: Director]->(Def)-> [LambdaExpression: [Person: λ] ->(Manage) -> [Group]] example 42 defined class in conceptual graphs
  43. 43. W3C® 43
  44. 44. RDF is a triple model i.e. every piece of knowledge is broken down into ( subject , predicate , object ) 44
  45. 45. doc.html has for author Fabien and has for theme Music 45
  46. 46. ( doc.html , author , Fabien ) ( doc.html , theme , Music ) ( subject , predicate , object ) RDF triples 46
  47. 47. Fabien author doc.html theme Music RDF graphs 47
  48. 48. <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
  49. 49. RDFS provides primitives for S lightweight ontologies 49
  50. 50. <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
  51. 51. OWL 51
  52. 52. <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
  53. 53. specify meaning with unique identifiers < >…</ > 53
  54. 54. link to the world 54
  55. 55. you are here tens of billions of triples already online, RDF is flying (e.g. http://sindice.com/ ) 55
  56. 56. Life cycle Design Needs Evolution Diffusion Manage Evaluate Use 56
  57. 57. motivating scenarios, competency needsquestions, Design Needs Evolution Diffusion Manage Evaluate Use 57
  58. 58. design knowledge acquisition techniques, natural language processing, formalisms formal concept analysis, methodologies & intermediary representations Design Needs Evolution Diffusion Manage Evaluate Use 58
  59. 59. diffusion identify, publish, advertise, web, peer-to-peer and other networks, standards (e.g., OWL) Design Needs Evolution Diffusion Manage Evaluate Use 59
  60. 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
  61. 61. evaluate c.f. needs + trace and usage analysis, metrics from methods, collective dimension and consensus Design Needs Evolution Diffusion Manage Evaluate Use 61
  62. 62. evolution alignment, c.f. design + versioning, version coherence checking and all dependencies Design Needs Evolution Diffusion Manage Evaluate Use 62
  63. 63. manage as any project, complete methodologies Design Needs Evolution Diffusion Manage Evaluate Use 63
  64. 64. the domain trap the application domain may be different from the ontology domain 64
  65. 65. I never saw a universal ontology 65
  66. 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
  67. 67. holistic knowledge, but finite ontologies 67
  68. 68. building block vs. changing block 68
  69. 69. ontology-based doesn’t mean you need an inference engine 69
  70. 70. SSRSSLSSS SSLSSLSSS SSL world-wide errors Berry inspired by Gérard 70
  71. 71. acquisition & evolution bottlenecks 71
  72. 72. tagging and other web 2.0 practices 72
  73. 73. 73
  74. 74. a tag a data attached to an object origins of geometry 74
  75. 75. social tagging collaboratively creating and managing tags to annotate and categorize content. 75
  76. 76. folks onomy the mass of users to organize the mass of data 76
  77. 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
  78. 78. folksonomies are not the opposite of ontologies 78
  79. 79. folksonomies can be seen as a new way to build and maintain ontologies 79
  80. 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
  81. 81. many societies my bookmarked page socially shared bookmark bookmark shared across people an applications 81
  82. 82. ontologies folksonomies 82
  83. 83. example learning applications 83
  84. 84. describe… users, learning objects, curriculums, 84
  85. 85. e.g. LOM (Learning Object Metadata) has nine types of characteristics: general, life-cycle, meta-metadata, technical, educational, rights, relations, annotation, classification 85
  86. 86. scenario S ? knowledge transfer/(re)use/analysis? evaluation/test/marking? profiling/customizing? feedback/curriculum management? 86
  87. 87. Dublin core Creative Commons FOAF … 87
  88. 88. take-home summary and messages 88
  89. 89. web” “semantic and not “semantic web” [C. Welty, ISWC 2007] 89
  90. 90. a lightweight ontology allows us to do lightweight reasoning [J. Hendler, ISWC 2007] 90
  91. 91. you can’t foresee each and every use and reuse 91
  92. 92. avoid building another black box 92
  93. 93. make conceptualizations explicit 93
  94. 94. open your data to anyone who might use it 94
  95. 95. just my… 95
  96. 96. fabien, gandon, inria http://ns.inria.fr/fabien.gandon http://www.slideshare.net/fabien_gandon/ 96

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