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

Fabien Gandon
Fabien Gandon

The document discusses research on analyzing and modeling typed graphs to formalize and implement social semantic web applications for epistemic communities. It describes challenges in bridging social semantics and formal semantics on the web using typed graphs to represent web data. It also summarizes previous work presented at ICCS on RDF and conceptual graphs and outlines the multi-disciplinary WIMMICS team researching various aspects of linked data, semantic web, communities and more.

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ON THE MANY GRAPHS OF THE WEB AND THE INTEREST OF ADDING THEIR MISSING LINKS. Fabien GANDON, @fabien_gandon http://fabien.info    
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
CHALLENGE to bridge social semantics and formal semantics on the Web
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)
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
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
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
Previously on… the Web
10 three components of the Web architecture 1. identification (URI) & address (URL) ex. http://www.inria.fr URL
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
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
linking open data
14 multiplying references to the Web HTTP URL HTML reference address communication WEB
identify what exists on the web http://my-site.fr identify, on the web, what exists http://animals.org/this-zebra
16 W3C standards HTTP URI HTML reference address communication WEB universal nodes and types identification
17 a Web approach to data publication URI ???... « http://fr.dbpedia.org/resource/Paris »
18 a Web approach to data publication HTTP URI
19 a Web approach to data publication HTTP URI GET
20 a Web approach to data publication HTTP URI GET HTML, …
21 a Web approach to data publication HTTP URI GET HTML,XML,…
22 linked data
23 ratatouille.fr or the recipe for linked data
24 ratatouille.fr or the recipe for linked data
25 ratatouille.fr or the recipe for linked data
26 ratatouille.fr or the recipe for linked data
27 datatouille.fr or the recipe for linked data
28 a Web graph data model HTTP URI RDF reference address communication Web of data universal graph data model
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
GLOBAL GIANT GRAPH of linked (open) data on the Web
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
32 a Web graph access HTTP URI RDF reference address communication Web of data
QUERY THE RDF GRAPH SPARQL graph patterns & constraints e.g. DBpedia
34 HTTP URI RDFS OWL reference address communication web of data Web ontology languages
35 RDFS to declare classes of resources, properties, and organize their hierarchy Document Report creator author Document Person
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 …
SKOS thesaurus, lexicon skos:narrowerTransitive skos:narrower skos:broaderTransitive skos:broader #Algebra#Mathematics #LinearAlgebra broader narrower broader narrower broaderTransitive broaderTransitive narrowerTransitive narrowerTransitive broaderTransitive narrowerTransitive
LOV.OKFN.ORG Web directory of vocabularies/schemas/ ontologies
39 data traceability & trust
40 PROV-O: vocabulary for provenance and traceability describe entities and activities involved in providing a resource
RESEARCH CHALLENGES 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing
METHODS AND TOOLS 1. user & interaction design 
METHODS AND TOOLS 1. user & interaction design 2. communities & social networks  
METHODS AND TOOLS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web   
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
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
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
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
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
cultural data is a weapon of mass construction
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.]
PUBLISHING e.g. DBpedia.fr 185 377 686 RDF triples extracted and mapped [Boyer, Cojan et al.]
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.]
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.]
DBPEDIA & STTL declarative transformation language from RDF to text formats (XML, JSON, HTML, Latex, natural language, GML, …) [Corby, Faron-Zucker et al.]
“searching” comes in many flavors
SEARCHING  exploratory search  question-answering DBPEDIA.FR (extraction, end-point) 180 000 000 triples [Cojan, Boyer et al.]
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.]
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.]
SEARCHING e.g. DiscoveryHub
semantic spreading activation SIMILARITY FILTERING discoveryhub.co
SEARCHING e.g. QAKIS
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.]
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.]
BROWSING e.g. SMILK plugin [Lopez, Cabrio, et al.]
BROWSING e.g. SMILK plugin [Nooralahzadeh, Cabrio, et al.]
On the many graphs of the Web and the interest of adding their missing links.
MODELING USERS  individual context  social structures
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.]
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.]
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.]
DEBATES & EMOTIONS #IRC
DEBATES & EMOTIONS #IRC argument rejection attacks-disgust DISGUST
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.]
MODELING USERS e.g. e-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]
LUDO: ontological modeling of serious games Learning Game KB Player’s profile & context Game design [Rodriguez-Rocha, Faron-Zucker et al.]
DOCS & TOPICS link topics, questions, docs, [Dehors, Faron-Zucker et al.]
MONITORING e.g. progress of learners [Dehors, Faron-Zucker et al.]
On the many graphs of the Web and the interest of adding their missing links.
QUERY & INFER  graph rules and queries  deontic reasoning  induction
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.]
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.]
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.]
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.]
QUERY & INFER e.g. CORESE/KGRAM [Corby et al.]
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.]
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
QUERY & INFER e.g. Gephi+CORESE/KGRAM
QUERY & INFER e.g. Licencia [Villata et al.]
EXPLAIN  justify results  predict performances [Hasan et al.]
EXPLAIN  justify results  predict performances [Hasan et al.]
On the many graphs of the Web and the interest of adding their missing links.
98 Web 1.0, 2.0, 3.0 …
99 price convert? person other sellers? Web 1.0, 2.0, 3.0 …
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
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)
102 one Web … a unique space in every meanings: data persons documents programs metadata
103 Toward a Web of Things
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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On the many graphs of the Web and the interest of adding their missing links.

  • 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
  • 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
  • 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
  • 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,…
  • 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
  • 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 …
  • 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.]
  • 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.]
  • 67. MODELING USERS  individual context  social structures
  • 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 [Costabello 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 [Costabello et al.] [Meng et al.]
  • 70. 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.]
  • 72. DEBATES & EMOTIONS #IRC argument rejection attacks-disgust DISGUST
  • 73. 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.]
  • 74. MODELING USERS e.g. e-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]
  • 75. LUDO: ontological modeling of serious games Learning Game KB Player’s profile & context Game design [Rodriguez-Rocha, Faron-Zucker et al.]
  • 76. DOCS & TOPICS link topics, questions, docs, [Dehors, Faron-Zucker et al.]
  • 77. MONITORING e.g. progress of learners [Dehors, Faron-Zucker et al.]
  • 79. QUERY & INFER  graph rules and queries  deontic reasoning  induction
  • 80. 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.]
  • 81. 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.]
  • 82. 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.]
  • 83. 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.]
  • 84. QUERY & INFER e.g. CORESE/KGRAM [Corby et al.]
  • 85. 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.]
  • 86. 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
  • 87. QUERY & INFER e.g. Gephi+CORESE/KGRAM
  • 88. QUERY & INFER e.g. Licencia [Villata et al.]
  • 89. EXPLAIN  justify results  predict performances [Hasan et al.]
  • 90. EXPLAIN  justify results  predict performances [Hasan et al.]
  • 92. 98 Web 1.0, 2.0, 3.0 …
  • 94. 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
  • 95. 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)
  • 96. 102 one Web … a unique space in every meanings: data persons documents programs metadata
  • 97. 103 Toward a Web of Things
  • 98. 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