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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
   
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
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 on… the Web
6
three components of the Web architecture
1. identification (URI) & address (URL)
ex. http://www.inria.fr
URL
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
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
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
linking open data
12
W3C standards
13
W3C standards
14
a Web approach to data publication
URI ???...
« http://fr.dbpedia.org/resource/Paris »
15
a Web approach to data publication
HTTP URI
16
a Web approach to data publication
HTTP URI
GET
17
a Web approach to data publication
HTTP URI
GET
HTML, …
18
a Web approach to data publication
HTTP URI
GET
HTML,XML,…
19
linked data
20
ratatouille.fr
or the recipe for linked data
21
ratatouille.fr
or the recipe for linked data
22
ratatouille.fr
or the recipe for linked data
23
ratatouille.fr
or the recipe for linked data
24
datatouille.fr
or the recipe for linked data
25
W3C standards
26
W3C standards
HTTP
URI
RDF
reference address
communication
Web of
data
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
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
W3C standards
30
W3C standards
HTTP
URI
RDF
reference address
communication
Web of
data
31
W3C standards
32
W3C standards
HTTP
URI
RDFS
OWL
reference address
communication
web of
data
33
RDFS to declare classes of
resources, properties, and
organize their hierarchy
Document
Report
creator
author
Document Person
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
W3C standards
36
PROV-O: vocabulary for provenance and traceability
describe entities and activities involved in providing a resource
37
W3C standards
RESEARCH CHALLENGES
1. user & interaction design
2. communities & social networks
3. linked data & semantic Web
4. reasoning & analyzing
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?

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?

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?

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?

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
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…
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.]
DBPEDIA & STTL
declarative transformation
language from RDF to text
formats (XML, JSON, HTML,
Latex, natural language, GML,
…) [Cojan, 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.]
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.
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
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.]
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.
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
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.]
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.
88
Web 1.0, 2.0, 3.0 …
89
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
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)
92
one Web … a unique space in every meanings:
data
persons documents
programs
metadata
93
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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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.

  • 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
  • 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
  • 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,…
  • 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
  • 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 …
  • 36. 36 PROV-O: vocabulary for provenance and traceability describe entities and activities involved in providing a resource
  • 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.]
  • 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.]
  • 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.]
  • 71. DEBATES & EMOTIONS #IRC argument rejection attacks-disgust
  • 72. MODELING USERS e.g. e-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]
  • 73. LUDO: ontological modeling of serious games Learning Game KB Player’s profile & context Game design [Rodriguez-Rocha, Faron-Zucker et al.]
  • 75. QUERY & INFER  graph rules and queries  deontic reasoning  induction
  • 76. 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.]
  • 77. 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.]
  • 78. 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.]
  • 79. 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.]
  • 80. QUERY & INFER e.g. CORESE/KGRAM [Corby et al.]
  • 81. 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
  • 82. 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
  • 83. QUERY & INFER e.g. Gephi+CORESE/KGRAM
  • 84. QUERY & INFER e.g. Licencia [Villata et al.]
  • 85. EXPLAIN  justify results  predict performances [Hasan et al.]
  • 86. EXPLAIN  justify results  predict performances [Hasan et al.]
  • 88. 88 Web 1.0, 2.0, 3.0 …
  • 90. 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
  • 91. 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)
  • 92. 92 one Web … a unique space in every meanings: data persons documents programs metadata
  • 93. 93 Toward a Web of Things
  • 94. 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