One Web of pages, One Web of peoples, One Web of Services, One Web of Data, One Web of Things…and with the Semantic Web bind them.

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Information about One Web of pages, One Web of peoples, One Web of Services, One Web of...

Published on July 10, 2016

Author: fabien_gandon

Source: slideshare.net

1. ONE WEB OF PAGES, ONE WEB OF PEOPLES, ONE WEB OF SERVICES, ONE WEB OF DATA, ONE WEB OF THINGS… AND WITH THE SEMANTIC WEB BIND THEM. Fabien GANDON, WIMS2016, @fabien_gandon http://fabien.info    

2. WIMMICS TEAM  Inria  CNRS  University of Nice Inria Lille - Nord Europe (2008) Inria Saclay – Ile-de-France (2008) Inria Nancy – Grand Est (1986) Inria Grenoble – Rhône- Alpes (1992) Inria Sophia Antipolis Méditerranée (1983) Inria Bordeaux Sud-Ouest (2008) Inria Rennes Bretagne Atlantique (1980) Inria Paris-Rocquencourt (1967) Montpellier Lyon Nantes Strasbourg Center Branch Pau I3S Web-Instrumented Man-Machine Interactions, Communities and Semantics

3. CHALLENGE to bridge social semantics and formal semantics on the Web

4. MULTI-DISCIPLINARY TEAM  50 members (2015)  14 nationalities  1 DR, 3 Professors  3CR, 4 Assistant professors  1 SRP DR/Professors:  Fabien GANDON, Inria, AI, KR, Semantic Web, Social Web  Nhan LE THANH, UNS, Logics, KR, Emotions  Peter SANDER, UNS, Web, Emotions  Andrea TETTAMANZI, UNS, AI, Logics, Agents, CR/Assistant Professors:  Michel BUFFA, UNS, Web, Social Media  Elena CABRIO, UNS, NLP, KR, Linguistics  Olivier CORBY, Inria, KR, AI, Sem. Web, Programming, Graphs  Catherine FARON-ZUCKER, UNS, KR, AI, Semantic Web, Graphs  Alain GIBOIN, Inria, Interaction Design, KE, User & Task models  Isabelle MIRBEL, UNS, Requirements, Communities  Serena VILLATA, CNRS, AI, Argumentation, Licenses, Rights Inria Starting Position: Alexandre MONNIN, Philosophy, Web

5. Previously on… the Web

6. 6 three components of the Web architecture 1. identification (URI) & address (URL) ex. http://www.inria.fr URL

7. 7 three components of the Web architecture 1. identification (URI) & address (URL) ex. http://www.inria.fr 2. communication / protocol (HTTP) GET /centre/sophia HTTP/1.1 Host: www.inria.fr HTTP URL address

8. 8 three components of the Web architecture 1. identification (URI) & address (URL) ex. http://www.inria.fr 2. communication / protocol (HTTP) GET /centre/sophia HTTP/1.1 Host: www.inria.fr 3. representation language (HTML) Fabien works at <a href="http://inria.fr">Inria</a> HTTP URL HTML reference address communication WEB

9. 9 multiplying references to the Web HTTP URL HTML reference address communication WEB

10. identify what exists on the web http://my-site.fr identify, on the web, what exists http://animals.org/this-zebra

11. linking open data

12. 12 W3C standards

13. 13 W3C standards

14. 14 a Web approach to data publication URI ???... « http://fr.dbpedia.org/resource/Paris »

15. 15 a Web approach to data publication HTTP URI

16. 16 a Web approach to data publication HTTP URI GET

17. 17 a Web approach to data publication HTTP URI GET HTML, …

18. 18 a Web approach to data publication HTTP URI GET HTML,XML,…

19. 19 linked data

20. 20 ratatouille.fr or the recipe for linked data

21. 21 ratatouille.fr or the recipe for linked data

22. 22 ratatouille.fr or the recipe for linked data

23. 23 ratatouille.fr or the recipe for linked data

24. 24 datatouille.fr or the recipe for linked data

25. 25 W3C standards

26. 26 W3C standards HTTP URI RDF reference address communication Web of data

27. 27 "Music" RDFworld-wide graphs http://inria.fr/rr/doc.html http://ns.inria.fr/fabien.gandon#me http://inria.fr/schema#author http://inria.fr/schema#theme http://inria.fr/rr/doc.html

28. 28 linked open data explosion on the Web 0 50 100 150 200 250 300 350 01/05/2007 01/05/2008 01/05/2009 01/05/2010 01/05/2011 number of open, published and linked datasets in the LOD cloud

29. 29 W3C standards

30. 30 W3C standards HTTP URI RDF reference address communication Web of data

31. 31 W3C standards

32. 32 W3C standards HTTP URI RDFS OWL reference address communication web of data

33. 33 RDFS to declare classes of resources, properties, and organize their hierarchy Document Report creator author Document Person

34. 34 OWL in one… algebraic properties disjoint properties qualified cardinality 1..1 ! individual prop. neg chained prop.   enumeration intersection union complement  disjunction restriction! cardinality 1..1 equivalence [>18] disjoint union value restriction keys …

35. 35 W3C standards

36. 36 PROV-O: vocabulary for provenance and traceability describe entities and activities involved in providing a resource

37. 37 W3C standards

38. RESEARCH CHALLENGES 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing

39. RESEARCH CHALLENGES 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing How do we improve our interactions with a semantic and social Web ? • capture and model the users' characteristics? • represent and reason with the users’ profiles? • adapt the system behaviors as a result? • design new interaction means? • evaluate the quality of the interaction designed? 

40. RESEARCH CHALLENGES 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing How can we manage the collective activity on social media? • analyze the social interaction practices and the structures in which these practices take place? • capture the social interactions and structures? • formalize the models of these social constructs? • analyze & reason on these models of social activity? 

41. RESEARCH CHALLENGES 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing What are the needed schemas and extensions of the semantic Web formalisms for our models? • formalisms best suited for the models of the challenges 1 & 2 ? • limitations and extensions of existing formalisms? • missing schemas, ontologies, vocabularies? • links and combinations of existing formalisms? 

42. RESEARCH CHALLENGES 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing What are the algorithms required to analyze and reason on the heterogeneous graphs we obtained? • analyze graphs of different types and their interactions? • support different graph life-cycles, calculations and characteristics? • assist different tasks of our users? • design the Web architecture to deploy this? 

43. METHODS AND TOOLS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing      G2 H2  G1 H1 < Gn Hn

44. METHODS AND TOOLS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing  • KB interaction (context, Q&A, exploration, …) • user models, personas, emotion capture • mockups, evaluation campaigns     G2 H2  G1 H1 < Gn Hn

45. METHODS AND TOOLS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing   • KB interaction (context, Q&A, exploration, …) • user models, personas, emotion capture • mockups, evaluation campaigns • community detection, labelling • collective personas, coordinative artifacts • argumentation theory, sentiment analysis    G2 H2  G1 H1 < Gn Hn

46. METHODS AND TOOLS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing    • KB interaction (context, Q&A, exploration, …) • user models, personas, emotion capture • mockups, evaluation campaigns • community detection, labelling • collective personas, coordinative artifacts • argumentation theory, sentiment analysis • ontology-based knowledge representation • formalisms: typed graphs, uncertainty • knowledge extraction, data translation   G2 H2  G1 H1 < Gn Hn

47. METHODS AND TOOLS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing     • KB interaction (context, Q&A, exploration, …) • user models, personas, emotion capture • mockups, evaluation campaigns • community detection, labelling • collective personas, coordinative artifacts • argumentation theory, sentiment analysis • ontology-based knowledge representation • formalisms: typed graphs, uncertainty • knowledge extraction, data translation • graph querying, reasoning, transforming • induction, propagation, approximation • explanation, tracing, control, licensing, trust

48. cultural data is a weapon of mass construction

49. PUBLISHING  extract data (content, activity…)  provide them as linked data DBPEDIA.FR (extraction, end-point) 180 000 000 triples models Web architecture [Cojan, Boyer et al.]

50. PUBLISHING e.g. DBpedia.fr 185 377 686 RDF triples extracted and mapped

51. PUBLISHING e.g. DBpedia.fr number of queries per day 70 000 on average 2.5 millions max 185 377 686 RDF triples extracted and mapped public dumps, endpoints, interfaces, APIs…

52. PUBLISHING e.g. DBpedia.fr 2.5 billion RDF triples of versioning activities <http://fr.dbpedia.org/Réaux> a prov:Revision ; swp:isVersion "96"^^xsd:integer ; dc:created "2005-08-05T07:27:07"^^xsd:dateTime ; dc:modified "2015-01-06T10:26:35"^^xsd:dateTime ; dbfr:uniqueContributorNb 58 ; dbfr:revPerYear [ dc:date "2005"^^xsd:gYear ; rdf:value "2"^^xsd:integer ] ; … dbfr:revPerYear [ dc:date "2015"^^xsd:gYear ; rdf:value "1"^^xsd:integer ] ; dbfr:revPerMonth [ dc:date "08/2005"^^xsd:gYearMonth ; rdf:value "1"^^xsd:integer ] ; … dbfr:revPerMonth [ dc:date "01/2015"^^xsd:gYearMonth ; rdf:value "1"^^xsd:integer ] ; dbfr:averageSizePerMonth [ dc:date "08/2005"^^xsd:gYearMonth ; rdf:value "3060"^^xsd:float ] ; … dbfr:averageSizePerYear [ dc:date "2015"^^xsd:gYear ; rdf:value "4767"^^xsd:float ] ; dbfr:averageSizePerMonth [ dc:date "08/2005"^^xsd:gYearMonth ; rdf:value "3060"^^xsd:float ] ; … dbfr:averageSizePerMonth [ dc:date "01/2015"^^xsd:gYearMonth ; rdf:value "4767"^^xsd:float ] ; dbfr:size "4767"^^xsd:integer ; dc:creator [ foaf:name "DasBot" ; rdf:type scoro:ComputationalAgent ] ; sioc:note "Robot : Remplacement de texte automatisé (- [[commune française| +[[commune (France)|)"^^xsd:string ; prov:wasRevisionOf <https://fr.wikipedia.org/w/index.php?title=Réaux&oldid=103 441506> ; prov:wasAttributedTo [ foaf:name "Escarbot" ; a prov:SoftwareAgent ] . [Corby, Boyer et al.]

53. DBPEDIA & STTL declarative transformation language from RDF to text formats (XML, JSON, HTML, Latex, natural language, GML, …) [Cojan, Faron-Zucker et al.]

54. “searching” comes in many flavors

55. SEARCHING  exploratory search  question-answering DBPEDIA.FR (extraction, end-point) 180 000 000 triples [Cojan, Boyer et al.]

56. SEARCHING  exploratory search  question-answering DBPEDIA.FR (extraction, end-point) 180 000 000 triples DISCOVERYHUB.CO semantic spreading activation new evaluation protocol [Marie, Giboin, Palagi et al.] [Cojan, Boyer et al.]

57. SEARCHING  exploratory search  question-answering DBPEDIA.FR (extraction, end-point) 180 000 000 triples DISCOVERYHUB.CO QAKiS.ORG semantic spreading activation new evaluation protocol [D:Work], played by [R:Person] [D:Work] stars [R:Person] [D:Work] film stars [R:Person] starring(Work, Person) linguistic relational pattern extraction named entity recognition similarity based SPARQL generation select * where { dbpr:Batman_Begins dbp:starring ?v . OPTIONAL {?v rdfs:label ?l filter(lang(?l)="en")} } [Cabrio et al.] [Marie, Giboin, Palagi et al.] [Cojan, Boyer et al.]

58. SEARCHING e.g. DiscoveryHub

59. semantic spreading activation SIMILARITY FILTERING discoveryhub.co

60. SEARCHING e.g. QAKIS

61. ALOOF: robots learning by reading on the Web Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen [Cabrio, Basile et al.]

62. ALOOF: robots learning by reading on the Web  First Object Relation Knowledge Base: 46.212 co-mentions, 49 tools, 14 rooms, 101 “possible location” relations,696 tuples <entity, relation, frame>  Evaluation: 100 domestic implements, 20 rooms, Crowdsourcing 2000 judgements  Object co-occurrence for coherence building Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen [Cabrio, Basile et al.]

63. BROWSING e.g. SMILK plugin [Lopez, Cabrio, et al.]

64. BROWSING e.g. SMILK plugin [Nooralahzadeh, Cabrio, et al.]

65. MODELING USERS  individual context  social structures

66. MODELING USERS  individual context  social structures PRISSMA prissma:Context 0 48.86034 -2.337599 200 geo:lat geo:lon prissma:radius 1 :museumGeo prissma:Environment 2 { 3, 1, 2, { pr i ssma: poi } } { 4, 0, 3, { pr i ssma: envi r onment } } :atTheMuseum error tolerant graph edit distance context ontology [Costabello et al.]

67. MODELING USERS  individual context  social structures PRISSMA prissma:Context 0 48.86034 -2.337599 200 geo:lat geo:lon prissma:radius 1 :museumGeo prissma:Environment 2 { 3, 1, 2, { pr i ssma: poi } } { 4, 0, 3, { pr i ssma: envi r onment } } :atTheMuseum error tolerant graph edit distance context ontology OCKTOPUS tag, topic, user distribution tag and folksonomy restructuring with prefix trees [Costabello et al.] [Meng et al.]

68. MODELING USERS  individual context  social structures PRISSMA prissma:Context 0 48.86034 -2.337599 200 geo:lat geo:lon prissma:radius 1 :museumGeo prissma:Environment 2 { 3, 1, 2, { pr i ssma: poi } } { 4, 0, 3, { pr i ssma: envi r onment } } :atTheMuseum error tolerant graph edit distance context ontology OCKTOPUS tag, topic, user distribution tag and folksonomy restructuring with prefix trees EMOCA&SEEMPAD emotion detection & annotation [Villata, Cabrio et al.] [Costabello et al.] [Meng et al.]

69. DEBATES & EMOTIONS #IRC

70. DEBATES & EMOTIONS #IRC argument rejection attacks-disgust

71. MODELING USERS e.g. e-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]

72. LUDO: ontological modeling of serious games Learning Game KB Player’s profile & context Game design [Rodriguez-Rocha, Faron-Zucker et al.]

73. QUERY & INFER  graph rules and queries  deontic reasoning  induction

74. QUERY & INFER  graph rules and queries  deontic reasoning  induction CORESE  & G2 H2  & G1 H1 < Gn Hn abstract graph machine STTL [Corby, Faron-Zucker et al.]

75. QUERY & INFER  graph rules and queries  deontic reasoning  induction CORESE  & G2 H2  & G1 H1 < Gn Hn RATIO4TA predict & explain abstract graph machine STTL [Corby, Faron-Zucker et al.] [Hasan et al.]

76. QUERY & INFER  graph rules and queries  deontic reasoning  induction CORESE INDUCTION  & G2 H2  & G1 H1 < Gn Hn RATIO4TA predict & explain find missing knowledge abstract graph machine STTL [Corby, Faron-Zucker et al.] [Hasan et al.] [Tettamanzietal.]

77. QUERY & INFER  graph rules and queries  deontic reasoning  induction CORESE LICENTIA INDUCTION  & G2 H2  & G1 H1 < Gn Hn RATIO4TA predict & explain find missing knowledge license compatibility and composition abstract graph machine STTL [Corby, Faron-Zucker et al.] [Hasan et al.] [Tettamanzietal.] [Villata et al.]

78. QUERY & INFER e.g. CORESE/KGRAM [Corby et al.]

79. FO  R  GF  GR mapping modulo an ontology car vehicle car(x)vehicle(x) GF GR vehicle car O RIF-BLD SPARQL RIFSPARQL ?x ?x C C List(T1. . . Tn) (T1’. . . Tn’) OpenList(T1. . . Tn T) External(op((T1. . . Tn))) Filter(op’ (T1’. . . Tn’)) T1 = T2 Filter(T1’ =T2’) X # C X’ rdf:type C’ T1 ## T2 T1’ rdfs:subClassOf T2’ C(A1 ->V1 . . .An ->Vn) C(T1 . . . Tn) AND(A1. . . An) A1’. . . An’ Or(A1. . . An) {A1’} …UNION {An’} OPTIONAL{B} Exists ?x1 . . . ?xn (A) A’ Forall ?x1 . . . ?xn (H) Forall ?x1 . . . ?xn (H:- B) CONSTRUCT { H’} WHERE{ B’} restrictions equivalence no equivalence extensions

80. FO  R  GF  GR mapping modulo an ontology car vehicle car(x)vehicle(x) GF GR vehicle car O truck car         121 ,, )(2121 2 21 2 1 ),(let;),( ttttt tdepthHc ttlttHtt c   ),(),(min),(let),( 21,21 2 21 21 ttlttlttdistHtt cc HHttttc   vehicle car O truck t1(x)t2(x)  d(t1,t2)< threshold

81. QUERY & INFER e.g. Gephi+CORESE/KGRAM

82. QUERY & INFER e.g. Licencia [Villata et al.]

83. EXPLAIN  justify results  predict performances [Hasan et al.]

84. EXPLAIN  justify results  predict performances [Hasan et al.]

85. 88 Web 1.0, 2.0, 3.0 …

86. 89 price convert? person other sellers? Web 1.0, 2.0, 3.0 …

87. WIMMICS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing epistemic communitieslinked data usages and introspection contributions and traces

88. 91 Toward a Web of Programs “We have the potential for every HTML document to be a computer — and for it to be programmable. Because the thing about a Turing complete computer is that … anything you can imagine doing, you should be able to program.” (Tim Berners-Lee, 2015)

89. 92 one Web … a unique space in every meanings: data persons documents programs metadata

90. 93 Toward a Web of Things

91. WIMMICSWeb-instrumented man-machine interactions, communities and semantics     Fabien Gandon - @fabien_gandon - http://fabien.info he who controls metadata, controls the web and through the world-wide web many things in our world. Technical details: http://bit.ly/wimmics-papers

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