On the many graphs of the Web and the interest of adding their missing links.

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Published on July 10, 2016

Author: fabien_gandon

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1. ON THE MANY GRAPHS OF THE WEB AND THE INTEREST OF ADDING THEIR MISSING LINKS. Fabien GANDON, @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. WEB GRAPHS (meta)data of the relations and the resources of the web …sites …social …of data …of services + + + +… web… = + …semantics + + + +…= + typed graphs web (graphs) networks (graphs) linked data (graphs) workflows (graphs) schemas (graphs)

5. CHALLENGES typed graphs to analyze, model, formalize and implement social semantic web applications for epistemic communities  multidisciplinary approach for analyzing and modeling the many aspects of intertwined information systems communities of users and their interactions  formalizing and reasoning on these models using typed graphs new analysis tools and indicators new functionalities and better management

6. 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

7. PREVIOUSLY IN ICCS ON RDF & CG • Griwes: Generic Model and Preliminary Specifications for a Graph-Based Knowledge Representation Toolkit, Jean-François Baget, Olivier Corby, Rose Dieng-Kuntz1, Catherine Faron- Zucker, Fabien Gandon, Alain Giboin, Alain Gutierrez, Michel Leclère, Marie-Laure Mugnier, Rallou Thomopoulos, ICCS 2008 • Keynote “Web, Graphs and Semantics” Olivier Corby , ICCS 2008 … • A Conceptual Graph Model for W3C RDF Resource Description Framework, Olivier Corby, Rose Dieng, Cédric Hebert, ICCS 8/14/2000

8. Previously on… the Web

9. 10 three components of the Web architecture 1. identification (URI) & address (URL) ex. http://www.inria.fr URL

10. 11 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

11. 12 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

12. linking open data

13. 14 multiplying references to the Web HTTP URL HTML reference address communication WEB

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

15. 16 W3C standards HTTP URI HTML reference address communication WEB universal nodes and types identification

16. 17 a Web approach to data publication URI ???... « http://fr.dbpedia.org/resource/Paris »

17. 18 a Web approach to data publication HTTP URI

18. 19 a Web approach to data publication HTTP URI GET

19. 20 a Web approach to data publication HTTP URI GET HTML, …

20. 21 a Web approach to data publication HTTP URI GET HTML,XML,…

21. 22 linked data

22. 23 ratatouille.fr or the recipe for linked data

23. 24 ratatouille.fr or the recipe for linked data

24. 25 ratatouille.fr or the recipe for linked data

25. 26 ratatouille.fr or the recipe for linked data

26. 27 datatouille.fr or the recipe for linked data

27. 28 a Web graph data model HTTP URI RDF reference address communication Web of data universal graph data model

28. 29 "Music" RDFis a model for directed labeled multigraphs http://inria.fr/rr/doc.html http://ns.inria.fr/fabien.gandon#me http://inria.fr/schema#author http://inria.fr/schema#topic http://inria.fr/rr/doc.html http://inria.fr/schema#keyword

29. GLOBAL GIANT GRAPH of linked (open) data on the Web

30. 31 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

31. 32 a Web graph access HTTP URI RDF reference address communication Web of data

32. QUERY THE RDF GRAPH SPARQL graph patterns & constraints e.g. DBpedia

33. 34 HTTP URI RDFS OWL reference address communication web of data Web ontology languages

34. 35 RDFS to declare classes of resources, properties, and organize their hierarchy Document Report creator author Document Person

35. 36 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 …

36. SKOS thesaurus, lexicon skos:narrowerTransitive skos:narrower skos:broaderTransitive skos:broader #Algebra#Mathematics #LinearAlgebra broader narrower broader narrower broaderTransitive broaderTransitive narrowerTransitive narrowerTransitive broaderTransitive narrowerTransitive

37. LOV.OKFN.ORG Web directory of vocabularies/schemas/ ontologies

38. 39 data traceability & trust

39. 40 PROV-O: vocabulary for provenance and traceability describe entities and activities involved in providing a resource

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

41. METHODS AND TOOLS 1. user & interaction design 

42. METHODS AND TOOLS 1. user & interaction design 2. communities & social networks  

43. METHODS AND TOOLS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web   

44. 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

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     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    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   G2 H2  G1 H1 < Gn Hn

48. 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

49. cultural data is a weapon of mass construction

50. 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.]

51. PUBLISHING e.g. DBpedia.fr 185 377 686 RDF triples extracted and mapped [Boyer, Cojan et al.]

52. PUBLISHING e.g. DBpedia.fr number of queries per day 70 000 on average 2.5 millions max 185 377 686 RDF triples (edges) extracted and mapped public dumps, endpoints, interfaces, APIs…[Boyer, Cojan et al.]

53. PUBLISHING e.g. DBpedia.fr 2.5 billion RDF triples (edges) 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 ] . [Boyer, Corby et al.]

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

55. “searching” comes in many flavors

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

57. 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.]

58. 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.]

59. SEARCHING e.g. DiscoveryHub

60. semantic spreading activation SIMILARITY FILTERING discoveryhub.co

61. SEARCHING e.g. QAKIS

62. 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.]

63. 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.]

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

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

66. MODELING USERS  individual context  social structures

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 [Costabello 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 [Costabello et al.] [Meng et al.]

69. 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.]

70. DEBATES & EMOTIONS #IRC

71. DEBATES & EMOTIONS #IRC argument rejection attacks-disgust DISGUST

72. ARGUMENTS debate graphs, argument networks, argumentation theory. • bipolar argumentation framework (BAF) = A, R, S • A: a set of arguments • R: a binary attack relation between arguments • S: a binary support relation between arguments • reason about arguments • deductive support • acceptability • support people in understanding ongoing debates [Villata et al.]

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

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

75. DOCS & TOPICS link topics, questions, docs, [Dehors, Faron-Zucker et al.]

76. MONITORING e.g. progress of learners [Dehors, Faron-Zucker et al.]

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

78. 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.]

79. 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.]

80. 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.]

81. 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.]

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

83. 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 [Seye et al.]

84. 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

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

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

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

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

89. 98 Web 1.0, 2.0, 3.0 …

90. 99 price convert? person other sellers? Web 1.0, 2.0, 3.0 …

91. 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

92. 101 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)

93. 102 one Web … a unique space in every meanings: data persons documents programs metadata

94. 103 Toward a Web of Things

95. 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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