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WIMMICS - 2018
LINKING NATURAL AND
ARTIFICIAL INTELLIGENCE ON
THE WEB
Fabien GANDON, @fabien_gandon http://fabien.info
   
WIMMICS TEAM
 Inria
 CNRS
 University Côte D’Azur (UCA)
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) & UCA, I3S, CNRS – Nice, FRANCE
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
SCENARIO
epistemic
communities
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
 40~50 members
 ~15 nationalities
 1 DR, 3 Professors
 3CR, 4 Assistant professors
DR/Professors:
 Fabien GANDON, Inria, AI, KRR, Semantic Web, Social Web
 Nhan LE THANH, UCA, Logics, KR, Emotions, Workflows
 Peter SANDER, UCA, Web, Emotions
 Andrea TETTAMANZI, UCA, AI, Logics, Agents,
CR/Assistant Professors:
 Michel BUFFA, UCA, Web, Social Media
 Elena CABRIO, UCA, NLP, KR, Linguistics, Q&A, Text Mining
 Olivier CORBY, Inria, KR, AI, Sem. Web, Programming, Graphs
 Catherine FARON-ZUCKER, UCA, KR, AI, Semantic Web, Graphs
 Alain GIBOIN, Inria, Interaction Design, KE, User & Task models
 Isabelle MIRBEL, UCA, Requirements, Communities
 Serena VILLATA, CNRS, AI, Argumentation, Licenses, Rights
Research engineer: Franck MICHEL, CNRS, Linked Data, Integration, DB
External:
 Andrei Ciortea (University of St. Gallen) Agents, WoT, Sem. Web
 Nicolas DELAFORGE (Mnemotix) Sem. Web, KM, Integration
 Claude FRASSON (University of Montreal) Emotion, eLearning
 Freddy LECUE (Thales, Montreal) AI, Logics, Mining, Big Data, Sem. 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)
URI, IRI, URL, HTTP URI
CONTRIBUTE TO DATA AND SCHEMATA STANDARDS ON THE WEB
JSON
RDF
JSON LD
N-Triple
N-Quad
Turtle/N3
TriG
RDFS
OWL
SPARQL
XML
HTML
RDF XML
HTTP
Linked Data
CSV-LD R2RML
GRDDL
RDFa
SHACL
LDP
MOOC
[Gandon, Corby, Faron-Zucker]
Xxx
xxxx
x
Xxx
xxxx
xx
xxxx
xxxx
x
xxxx
Xxx
xxx
xxxx
x
Xxxxxx
xxxxxx
xxxxxxXxxxxx
xxxxxx
xxxxxx
Xxxxxx
xxxxxx
xxxxxx
contribute
Xxxxxx
xxxxxx
xxxxxx
contributes
exchange
Xxxxxx
xxxxxx
xxxxxx
Xxxxxx
xxxxxx
xxxxxx
link, enrich
analyze, assist
START-UP
 cooperative company
 spin-off Wimmics
 semantics & social media
METHODS AND TOOLS
1. user & interaction design

METHODS AND TOOLS
1. user & interaction design
2. communities & social medias


METHODS AND TOOLS
1. user & interaction design
2. communities & social medias
3. linked data & semantic Web



METHODS AND TOOLS
1. user & interaction design
2. communities & social medias
3. linked data & semantic Web
4. reasoning & analyzing





G2 H2

G1 H1
<
Gn Hn
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
e.g. 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
DBpedia.fr usage
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
DBpedia.fr active since
2012
REUSE
 build and help build applications DBPEDIA.FR (extraction, end-point)
180 000 000 triples
Zone 47
BBC
HdA Lab
RAWebSem
MCC Contest
MonaLIA
RAWEB SEM.
Analytics on the activity
report of Inria in RDF
[Rupi, Dufour, Gandon, Corby et al.]http://rawebsem.inria.fr/srv/tutorial/raweb
James Bridle’s twelve-volume encyclopedia of all
changes to the Wikipedia article on the Iraq War
booktwo.org
HISTORIC
for each page & edition
EXTRACTED
entire edition history as
linked open data
1.9 billion triples describing the 107 million revisions since the first page was created
<http://fr.wikipedia.org/wiki/Victor_Hugo> a prov:Revision ;
dc:subject <http://fr.dbpedia.org/resource/Victor_Hugo> ;
swp:isVersion "3496"^^xsd:integer ;
dc:created "2002-06-06T08:48:32"^^xsd:dateTime ;
dc:modified "2015-10-15T14:17:02"^^xsd:dateTime ;
dbfr:uniqueContributorNb 1295 ;
(...)
dbfr:revPerYear [ dc:date "2015"^^xsd:gYear ; rdf:value
"79"^^xsd:integer ] ;
dbfr:revPerMonth [ dc:date "06/2002"^^xsd:gYearMonth ;
rdf:value "3"^^xsd:integer ] ;
(...)
dbfr:averageSizePerYear [ dc:date "2015"^^xsd:gYear ;
rdf:value "154110.18"^^xsd:float
] ;
dbfr:averageSizePerMonth [ dc:date
"06/2002"^^xsd:gYearMonth ;
rdf:value "2610.66"^^xsd:float ]
;
(...)
dbfr:size "159049"^^xsd:integer ;
dc:creator [ foaf:nick "Rinaldum" ] ;
sioc:note "wikification"^^xsd:string ;
prov:wasRevisionOf <http:// … 119074391> ;
prov:wasAttributedTo [ foaf:name "Rémih" ; a prov:Person,
foaf:Person ] .
<http:// … 119074391> a prov:Revision ;
dc:created "2015-09-29T19:35:34"^^xsd:dateTime ;
dbfr:size "159034"^^xsd:integer ;
dbfr:sizeNewDifference "-5"^^xsd:integer ;
sioc:note "/*Années théâtre*/ neutralisation"^^xsd:string ;
prov:wasAttributedTo [ foaf:name "Thouny" ; a prov:Person,
foaf:Person ] ;
prov:wasRevisionOf <http://... 118903583> .
(...)
<http:// … oldid=118201419> a prov:Revision ;
prov:wasAttributedTo [ foaf:name "OrlodrimBot" ; a
prov:SoftwareAgent ] ;
(...)
[Gandon, Boyer, Corby, Monnin 2016]
DEMO
Facetted history portals
Elections
in France
DEMO
Facetted history portals
Elections
in France
Death of
C. Lee
DEMO
Facetted history portals
Elections
in France
Death of
C. Lee
Events in
Ukraine
DBPEDIA & STTL
declarative transformation
language from RDF to text
formats (XML, JSON, HTML,
Latex, natural language, GML,
…) [Cojan, Corby, Faron-Zucker et al.]
CRAWLING
 Predict data availability
 Select features of URIs
 Learn crawling selection
[Huang, Gandon 2019]
more data, more usages, more users
NEW USAGES
e.g. e-learning & serious games
[Rodriguez-Rocha, Faron-Zucker et al.]
QUIZZES
Automated generation of
quizzes
[Rodriguez-Rocha, Faron-Zucker et al.]
rdf:type
p
rdfs:subClassOf
?
?
?
?
RDF Q&A
Generation
NL Questions
Generation
Ranking
Question
SELECTION
Knowledge
Graph
QuizQ&A
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.]
EDUMICS
 Ontology EduProgression: OWL modeling of scholar program
 Ontology RefEduclever: new education referential for Educlever
 Migration and persistence in graph databases
 Reasoning, query, interactions, recommendation
[Fokou, Faron et al. 2017]
“
Smarter Cities – IBM Dublin
[Lécué, 2015]
QUESTION ROUTING
 emails to the customer service (eg 350000/day “Crédit Mutuel”)
 detect topics in order to “understand” a question
 3 humans annotate 142 questions (Krippendorff’s Alpha 0,70)
 NLP and semantic processing for features extraction
 ML performance comparison for question classification
Naive Bayes, Sequential Minimal Optimisation (SMO),
Random Forest, RAndom k-labELsets (RAkEL)
[Gazzotti, et al. 2017]
NE
recognition
(L,T)
Removing
special
characters
Tokenization
(L,T)
Spell
Checking
(L,T)
Lemmatization
(L)
Vector
generation
BOW/N-gram
Replacement in documents
Consider as feature
Input
Document
ML
workflow
L: Language dependent - T: Text dependent
Unbalanced Topics
Metrics uni uni⨁bi uni+bi+tri uni⨁NE syn syn⨁hyper syn⨁NE
Hamming
Loss
0,0381 0,0370 0,0374 0,0373 0,0399 0,0412 0,0405
PREDICT HOSPITALIZATION
 Physician’s records classification
in order to predict hospitalization
[Gazzotti, Faron et al. 2017]
Sexe Date Cause CISP2 ... History Observations
H 25/04/2012 vaccin-antitétanique A44 ... Appendicite EN CP - Bon état général -
auscult pulm libre; bdc
rég sans souffle - tympans
ok-
Element Number
Patients
Consultations
Past medical history
Biometric data
Semiotics
Diagnosis
Row of prescribed drugs
Symptoms
Health care procedures
Additional examination
Paramedical prescription
Observations/notes
55 823
364 684
187 290
293 908
250 669
117 442
847 422
23 488
11 850
871 590
17 222
56 143
PREDICT HOSPITALIZATION
 Physician’s records classification
in order to predict hospitalization
 Augment data with structured
knowledge and study impact on
different prediction methods
[Gazzotti, Faron et al. 2017]
Sexe Date Cause CISP2 ... History Observations
H 25/04/2012 vaccin-antitétanique A44 ... Appendicite EN CP - Bon état général -
auscult pulm libre; bdc
rég sans souffle - tympans
ok-
Element Number
Patients
Consultations
Past medical history
Biometric data
Semiotics
Diagnosis
Row of prescribed drugs
Symptoms
Health care procedures
Additional examination
Paramedical prescription
Observations/notes
55 823
364 684
187 290
293 908
250 669
117 442
847 422
23 488
11 850
871 590
17 222
56 143
(1)
(2)
ZOOMATHIA
Cultural transmission of
zoological knowledge
from Antiquity
to Middle Age
[Faron Zucker, et al.]
SPARQL MICRO-SERVICES
TAXREF-LD Linked Data taxonomic register + Flickr +
Biodiversity Heritage Library + Encyclopedia of Life
trait bank + audio recordings from the Macaulay
Library + MusicBrainz and… SSTL.
[Michel , et al.]
SCIENTIFIC HERITAGE
 TAXREF Vocabulary
 Data extraction and
publication
[Tounsi, Callou, Michel, Pajo, Faron Zucker et al.]
WASABI
augmenting musical
experience with the Web
[Buffa, Jauva et al.]
NLP & LOD & Lyrics
WASABI
learning to analyzing the
structure of lyrics
[Fell, Cabrio et al.]
133
Toward a Web of Things
“searching” comes in many flavors
SEARCHING
 exploratory search
 question-answering
DBPEDIA.FR (SPARQL end-point)
180 000 000 triples
[Cojan, Boyer et al.]
SEARCHING
 exploratory search
 question-answering
DBPEDIA.FR (SPARQL 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 (SPARQL end-point)
180 000 000 triples
DISCOVERYHUB.CO
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.]
QAKiS.ORG
SEARCHING
e.g. QAKIS
question-answering
[Cabrio et al.]
learning linguistic patterns of queries
[Cabrio et al.]
MULTIMEDIA
answer visualization
through linked data
[Cabrio et al.]
BROWSING
e.g. SMILK plugin
[Lopez, Cabrio, et al.]
BROWSING
e.g. SMILK plugin
[Nooralahzadeh, Cabrio, et al.]
SEARCHING
e.g. DiscoveryHub
exploratory search
[Marie, Gandon, Ribière.]
semantic spreading activation
SIMILARITY
FILTERING
discoveryhub.co
[Marie, Gandon, Ribière.]
CONVERGING
answer visualization
through linked data
SEARCHING
e.g. DiscoveryHub
exploratory search
relevant
not known
known
not relevant
EVALUATING
user-centric studies
INTERACTION
design and evaluation
Favoris
Nouvelle recherche TEMPS
Debut test Free Jazz 24s
Free improvisation 33s
(fiche) Avant-garde 47s
John Coltrane (vidéo) 1min 28
Marc Ribot 2min11
(fiche) experimental music 2min18 2min23
Krautrock 2min31
(fiche) Progressive rock 2min37 2min39
Red (King Crimson album) 2m52 2min59
King
Crimson 3min05
(fiche) Jazz fusion 3min18
(fiche) Free Jazz 3min32 3min54
Sun Ra 4min18
(fiche) Hard bop 4min41 4min47
Charles
Mingus (vidéo) 5min29
(fiche) Third Stream (vidéo) 6min20
Bebop 7min19
Modal jazz 7min26
(fiche) Saxophone 7min51 7min55
Mel Collins
21st Century Schizoid Band
Crimson Jazz Trio
(fiche)
King
Crimson
(fiche)
Robert
Fripp
Miles Davis
Thelonious Monk
(fiche) Blue Note Record
McCoy Tyner
(fiche) Modal Jazz
(fiche) Jazz
Chick Corea
(fiche) Jazz Fusion
Return to Forever
Mahavishnu Orchestra
Shakti (band)
U.Srinivas
Bela Fleck
Flecktones
John McLaughlin (musician)
Dixie Dregs
FICHE Dixie Degs
T Lavitz
Jordan Rudess
Behold… The Arctopus
(fiche) Avant-garde metal
Unexpected
FICHE unexpected
Dream Theater
King
Crimson
(fiche) Jazz fusion
King
Crimson
Tony Levin
(fiche) Anderson Bruford Wakeman Howe
(fiche) Rike Wakeman (vidéo)
Fin test
[Palagi, Marie, Giboin et al.]
(RE)DESIGN
interface evolutions
[Palagi, Marie, Giboin et al.]
METHODS & CRITERIA
 interaction design and evaluation
 exploratory search process model
[Palagi, Giboin et al. 2017]
A. Define the search space
B. Query (re)formulation
C. Information gathering
D. Put some information aside
E. Pinpoint search
F. Change of goal(s)
G. Backward/forward steps
H. Browsing results
I. Results analysis
J. Stop the search session
Previous features Feature Next features
NA A B ; J
A ; F B G ; H ; I ; J
D ; E ; I C D ; E ; F ; G ; H ; J
E ; I D C ; F ; G ; J
G ; H ; I E C ; D ; F ; G ; J
C ; D ; E ; G ; H ; I F B ; H ; I ; J
B ; D ; E ; H ; I G E ; F ; H ; I ; J
B ; F ; G ; I H E ; F ; G ; ; I ; J
B ; F ; G ; H I C ; D ; E ; F ; G ; H ; J
all J NA
CHEXPLORE
Plugin to evaluate the
interface of an exploratory
search engine
[Palagi, Giboin et al. 2017]
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.]
points of view.
DEBATES & EMOTIONS
#IRC
[Villata, Cabrio et al.]
DEBATES & EMOTIONS
#IRC argument rejection
attacks-disgust[Villata, Cabrio et al.]
OPINIONS
NLP, ML and arguments
to monitor online image
[Villata, Cabrio, et al.]
ARGUMENT MINING
ON CLINICAL TRIALS
NLP, ML and arguments
to assist Evidence-based
medicine
[Mayer, Cabrio, Villata]
(…) To compare the intraocular pressure-lowering effect of latanoprost with that of
dorzolamide when added to timolol. [. . . ] [The diurnal intraocular pressure reduction
was significant in both groups (P < 0:001)]1. [The mean intraocular pressure reduction
from baseline was 32% for the latanoprost plus timolol group and 20% for the
dorzolamide plus timolol group]2. [The least square estimate of the mean diurnal
intraocular pressure reduction after 3 months was -7.06 mm Hg in the latanoprost plus
timolol group and -4.44 mm Hg in the dorzolamide plus timolol group (P < 0:001)]3.
Drugs administered in both treatment groups were well tolerated. This study clearly
showed that [the additive diurnal intraocular pressure-lowering effect of latanoprost
is superior to that of dorzolamide in patients treated with timolol]1.(…)
CLAIM
EVIDENCE
CREEP
NLP and ML for
cyber-bullying
detection
[Cabrio, Villata, et al.]
Web-augmented interactions
“
« a Web-Augmented Interaction (WAI)
is a user’s interaction with a system
that is improved by allowing the
system to access Web resources »
[Gandon, Giboin, WebSci17]
AZKAR
remotely visit and interact
with a museum through a
robot and via the Web
[Buffa et al.]
ALOOF: Web and Perception
[Cabrio, Basile et al.]
Semantic Web-Mining and Deep Vision for Lifelong Object Discovery (ICRA 2017)
Making Sense of Indoor Spaces using Semantic Web Mining and Situated Robot Perception (AnSWeR 2017)
ALOOF: robots learning by reading on the Web
 First Object Relation Knowledge Base: 46212 co-mentions, 49 tools, 14 rooms, 101
“possible location” relations, 696 tuples <entity, relation, frame>
 Evaluation: 100 domestic instruments, 20 rooms, 2000 crowdsourcing judgements
 Shared between robots through a shared Web knowledge base
Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen
[Cabrio, Basile
et al. 2017]
ALOOF: RDF dataset about objects
[Cabrio, Basile et al.]
 common sense knowledge about objects: classification, prototypical locations
and actions
 knowledge extracted from natural language parsing, crowdsourcing,
distributional semantics, keyword linking, ...
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
[Hasan et al.]
[Corby, Faron-Zucker 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
[Hasan et al.]
[Tettamanzietal.]
[Corby, Faron-Zucker et al.]
QUERY & INFER
 graph rules and queries
 deontic reasoning
 induction
CORESE
LICENTIA
INDUCTION
 &
G2 H2
 &
G1 H1
<
Gn Hn
RATIO4TA
predict &
explain
find missing
knowledge
deontic reasoning, license
compatibility and composition
abstract graph machine
STTL
[Hasan et al.]
[Tettamanzietal.]
[Villata et al.]
[Corby, Faron-Zucker 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
LDSCRIPT
a Linked Data Script Language
FUNCTION us:status(?x) {
IF (EXISTS { ?x ex:hasSpouse ?y }||EXISTS { ?y ex:hasSpouse ?x },
ex:Married, ex:Single) }
[Corby, Faron Zucker, Gandon, ISWC 2017]
DISTRIBUTED
inductive index creation for a
triple store [Basse, Gandon, Mirbel]
DISTRIBUTED
Querying heterogeneous and
distributed data [Gaignard,Corby et al.]
rr:objectMap
1
1
0-1
0-1
1
0-1
0-1
0-1
0-1
1
1
rr:GraphMaprr:graphMap
0-1
xrr:logicalSource
xrr:LogicalSource
xrr:query
Query String
rml:iterator Iteration pattern
rr:IRI, rr:BlankNode, rr:Literal,
xrr:RdfList, xrr:RdfBag,
xrr:RdfSeq, xrr:RdfAlt
reference expr.
xrr:nestedTermMap
xrr:NestedTermMap
rr:inverseExrpression
xrr:reference
reference expr.
reference expr.
rr:ObjectMap
HETEROGENEITY
xR2RML mapping language
and SPARQL query rewriting
[Michel et al.]
<AbstractQuery> ::= <AtomicQuery> | <Query> |
<Query> FILTER <SPARQL filter> | <Query> LIMIT <integer>
<Query> ::= <AbstractQuery> INNER JOIN <AbstractQuery> ON {v1, … vn} |
<AtomicQuery> AS child INNER JOIN <AtomicQuery> AS parent
ON child/<Ref> = parent/<Ref> |
<AbstractQuery> LEFT OUTER JOIN <AbstractQuery> ON {v1, … vn} |
<AbstractQuery> UNION <AbstractQuery>
<AtomicQuery> ::= {From, Project, Where, Limit}
<Ref> ::= a valid xR2RML data element reference
µSERVICES
Linked Data access to Web APIs.
[Michel et al.]
SPARQL Client
Service Logics
Web API
JSON-LD
Profile
SPARQL
INSERT/CONSTR
HTTP
query
JSON
response
Triple
store
SPARQL Micro-Service(1)
(4) (2)
(3)
LD Client Web Server
(1’)
(4’)
http://example.org/photo/472495
QUERY & INFER
e.g. Gephi+CORESE/KGRAM
[Demairy, Gandon et al.]
http://drunks-and-lampposts.com/2012/06/13/graphing-the-history-of-philosophy/
http://blog.ouseful.info/2012/07/03/mapping-how-programming-languages-influenced-each-other-according-to-wikipedia/
http://blog.ouseful.info/2012/07/04/mapping-related-musical-genres-on-wikipediadbpedia-with-gephi/
explore different domains
EXPLAIN
 justify results
[Hasan et al.]
EXPLAIN
 justify results
 predict performances
[Hasan et al.]
INDUCTION
learning axioms from linked
data on the Web [Tettamanzi et al.]
DISCOVERING ASSOCIATION RULES
[Tran, Tettamanzi, 2017]
isParent(x, y) ⇐ isFather(x, y)
isParent(x, y) ⇐ isMother(x, y)
Rules induced by (Facts1 ∪ Facts2)
isMother(Maria, Anna)
isMother(Maria, Alli)
isFather(Carlos, Anna)
isFathe(Carlos, Alli)
isParent(Maria, Anna)
isParent(Maria, Alli)
isParent(Carlos, Anna)
isParent(Carlos, Alli)
DISCOVERING ASSOCIATION RULES
 Discovering Multi-Relational Association Rules in the
Semantic Web
 Inductive Logic Programming (ILP)
= Logic Programming ∩ Machine Learning
 Learning logic rules from examples and background knowledge
 Evolutionary approach (genetic algo)
[Tran, Tettamanzi, 2017]
H1 ∧ ... ∧ Hm ⇐ B1 ∧ B2 ∧ ... Bn
DISTRIBUTED AI
 Agent-based Simulation for a Multi-context
BDI Recommender
 Solitary agents vs social agents: social
agents have better performance than
solitary ones
 Trust/Distrust score to detect malicious
agents
 Possibility theory: an uncertainty theory to
handle incomplete information
[Ben Othmane, Tettamanzi, Villata et al. 2017]
UNCERTAINTY
 Representing uncertainty theories
 Reasoning on uncertainty contexts
 Publishing it with linked data
 Negotiating the theory over HTTP
[Djebri, Tettamanzi, 2019]
DEONTICS
e.g. Licencia
[Villata et al.]
DEONTICS
Legal Rules on the Semantic Web
OWL + Named Graphs + SPARQL Rules
Named Graph (state of affair) Subject Predicate Object
http://ns.inria.fr/nrv-inst#StateOfAffairs1 Tom http://ns.inria.fr/nrv-inst#activity driving at 100km/h
http://ns.inria.fr/nrv-inst#StateOfAffairs1 Tom http://www.w3.org/2000/01/rdf-schema#label Tom
http://ns.inria.fr/nrv-inst#StateOfAffairs1 can't drive over 90km http://www.w3.org/1999/02/22-rdf-syntax-ns#type violated requirement
http://ns.inria.fr/nrv-inst#StateOfAffairs1 can't drive over 90km has for violation http://ns.inria.fr/nrv-inst#StateOfAffairs1
http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://ns.inria.fr/nrv-inst#speed 100
http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://ns.inria.fr/nrv-inst#Driving
http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://www.w3.org/2000/01/rdf-schema#label "driving at 100km/h"@en
Named Graph (state of affair) Subject Predicate Object
http://ns.inria.fr/nrv-inst#StateOfAffairs2 Jim http://ns.inria.fr/nrv-inst#activity driving at 90km/h
http://ns.inria.fr/nrv-inst#StateOfAffairs2 Jim http://www.w3.org/2000/01/rdf-schema#label Jim
http://ns.inria.fr/nrv-inst#StateOfAffairs2 can't drive over 90km http://www.w3.org/1999/02/22-rdf-syntax-ns#type compliant requirement
http://ns.inria.fr/nrv-inst#StateOfAffairs2 can't drive over 90km has for compliance http://ns.inria.fr/nrv-inst#StateOfAffairs2
http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://ns.inria.fr/nrv-inst#speed 90
http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://ns.inria.fr/nrv-inst#Driving
http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://www.w3.org/2000/01/rdf-schema#label "driving at 90km/h"@en
[Gandon et al.]
MonaLIA
 reason & query on RDF metadata to build
balanced, unambiguous, labelled training sets.
350 000 images
of artworks
RDF metadata based
on external thesauri
Joconde database from French museums
[Bobasheva et al. 2017]
MonaLIA
 reason & query on RDF metadata to build
balanced, unambiguous, labelled training sets.
 transfer learning & CNN classifiers on targeted
categories (topics, techniques, etc.)
 reason & query RDF metadata of results to
address silence, noise and explain
350 000 images
of artworks
RDF metadata based
on external thesauri
Joconde database from French museums
(1)
(2)
[Bobasheva et al. 2017]
animal 
bird ?
painting 
196
Web 1.0, …
197
Web 1.0, 2.0…
198
price
convert?
person
other sellers?
Web 1.0, 2.0, 3.0 …
199
Toward a Web of Things
200
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)
deduce data

model, schemas, ontologies, ...
data data
15% progress
learn data
embeddings, parameters, configurations, …
data data
30% progress
sum intelligence

model, schemas, ontologies, ...
embeddings, parameters, configurations, …
data data
45% progress
combine intelligence
model, schemas, ontologies, ...
embeddings, parameters, configurations, …
data

60% progress
remotely combine
model, schemas, ontologies, …
embeddings, parameters, configurations,…

Web
75% progress
deeply combine
data, knowledge, model, schemas, ontologies, …
data, knowledge, embeddings, parameters, configurations,…

Web
90% progress
combining AIs on the Web
data, knowledge, model, schemas, ontologies, …
data, knowledge, embeddings, parameters, configurations,…

Web
100% progress
209
one Web … a unique space in every meanings:
data
persons documents
programs
metadata
Connected Animals, Animal-computer interaction (ACI)
Herdsourcing: monitoring collective animal behavior
IMAG_NE
a Web linking all forms of Intelligence
WIMMICS
1. user & interaction design
2. communities & social networks
3. linked data & semantic Web
4. reasoning & analyzing
epistemic hybrid communitieslinked data
usages and introspection
contributions and traces
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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Linking Natural and Artificial Intelligence on the Web

  • 1. WIMMICS - 2018 LINKING NATURAL AND ARTIFICIAL INTELLIGENCE ON THE WEB Fabien GANDON, @fabien_gandon http://fabien.info    
  • 2. WIMMICS TEAM  Inria  CNRS  University Côte D’Azur (UCA) 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) & UCA, I3S, CNRS – Nice, FRANCE 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
  • 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  40~50 members  ~15 nationalities  1 DR, 3 Professors  3CR, 4 Assistant professors DR/Professors:  Fabien GANDON, Inria, AI, KRR, Semantic Web, Social Web  Nhan LE THANH, UCA, Logics, KR, Emotions, Workflows  Peter SANDER, UCA, Web, Emotions  Andrea TETTAMANZI, UCA, AI, Logics, Agents, CR/Assistant Professors:  Michel BUFFA, UCA, Web, Social Media  Elena CABRIO, UCA, NLP, KR, Linguistics, Q&A, Text Mining  Olivier CORBY, Inria, KR, AI, Sem. Web, Programming, Graphs  Catherine FARON-ZUCKER, UCA, KR, AI, Semantic Web, Graphs  Alain GIBOIN, Inria, Interaction Design, KE, User & Task models  Isabelle MIRBEL, UCA, Requirements, Communities  Serena VILLATA, CNRS, AI, Argumentation, Licenses, Rights Research engineer: Franck MICHEL, CNRS, Linked Data, Integration, DB External:  Andrei Ciortea (University of St. Gallen) Agents, WoT, Sem. Web  Nicolas DELAFORGE (Mnemotix) Sem. Web, KM, Integration  Claude FRASSON (University of Montreal) Emotion, eLearning  Freddy LECUE (Thales, Montreal) AI, Logics, Mining, Big Data, Sem. Web
  • 7. 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)
  • 8. URI, IRI, URL, HTTP URI CONTRIBUTE TO DATA AND SCHEMATA STANDARDS ON THE WEB JSON RDF JSON LD N-Triple N-Quad Turtle/N3 TriG RDFS OWL SPARQL XML HTML RDF XML HTTP Linked Data CSV-LD R2RML GRDDL RDFa SHACL LDP
  • 11. METHODS AND TOOLS 1. user & interaction design 
  • 12. METHODS AND TOOLS 1. user & interaction design 2. communities & social medias  
  • 13. METHODS AND TOOLS 1. user & interaction design 2. communities & social medias 3. linked data & semantic Web   
  • 14. METHODS AND TOOLS 1. user & interaction design 2. communities & social medias 3. linked data & semantic Web 4. reasoning & analyzing      G2 H2  G1 H1 < Gn Hn
  • 15. 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? 
  • 16. 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? 
  • 17. 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? 
  • 18. 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? 
  • 19. 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
  • 20. 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
  • 21. 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
  • 22. 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
  • 23. 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
  • 24. e.g. cultural data is a weapon of mass construction
  • 25. 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.]
  • 26. PUBLISHING DBpedia.fr usage 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…
  • 28. REUSE  build and help build applications DBPEDIA.FR (extraction, end-point) 180 000 000 triples Zone 47 BBC HdA Lab RAWebSem MCC Contest MonaLIA
  • 29. RAWEB SEM. Analytics on the activity report of Inria in RDF [Rupi, Dufour, Gandon, Corby et al.]http://rawebsem.inria.fr/srv/tutorial/raweb
  • 30.
  • 31. James Bridle’s twelve-volume encyclopedia of all changes to the Wikipedia article on the Iraq War booktwo.org
  • 33. EXTRACTED entire edition history as linked open data 1.9 billion triples describing the 107 million revisions since the first page was created <http://fr.wikipedia.org/wiki/Victor_Hugo> a prov:Revision ; dc:subject <http://fr.dbpedia.org/resource/Victor_Hugo> ; swp:isVersion "3496"^^xsd:integer ; dc:created "2002-06-06T08:48:32"^^xsd:dateTime ; dc:modified "2015-10-15T14:17:02"^^xsd:dateTime ; dbfr:uniqueContributorNb 1295 ; (...) dbfr:revPerYear [ dc:date "2015"^^xsd:gYear ; rdf:value "79"^^xsd:integer ] ; dbfr:revPerMonth [ dc:date "06/2002"^^xsd:gYearMonth ; rdf:value "3"^^xsd:integer ] ; (...) dbfr:averageSizePerYear [ dc:date "2015"^^xsd:gYear ; rdf:value "154110.18"^^xsd:float ] ; dbfr:averageSizePerMonth [ dc:date "06/2002"^^xsd:gYearMonth ; rdf:value "2610.66"^^xsd:float ] ; (...) dbfr:size "159049"^^xsd:integer ; dc:creator [ foaf:nick "Rinaldum" ] ; sioc:note "wikification"^^xsd:string ; prov:wasRevisionOf <http:// … 119074391> ; prov:wasAttributedTo [ foaf:name "Rémih" ; a prov:Person, foaf:Person ] . <http:// … 119074391> a prov:Revision ; dc:created "2015-09-29T19:35:34"^^xsd:dateTime ; dbfr:size "159034"^^xsd:integer ; dbfr:sizeNewDifference "-5"^^xsd:integer ; sioc:note "/*Années théâtre*/ neutralisation"^^xsd:string ; prov:wasAttributedTo [ foaf:name "Thouny" ; a prov:Person, foaf:Person ] ; prov:wasRevisionOf <http://... 118903583> . (...) <http:// … oldid=118201419> a prov:Revision ; prov:wasAttributedTo [ foaf:name "OrlodrimBot" ; a prov:SoftwareAgent ] ; (...) [Gandon, Boyer, Corby, Monnin 2016]
  • 36. DEMO Facetted history portals Elections in France Death of C. Lee Events in Ukraine
  • 37. DBPEDIA & STTL declarative transformation language from RDF to text formats (XML, JSON, HTML, Latex, natural language, GML, …) [Cojan, Corby, Faron-Zucker et al.]
  • 38. CRAWLING  Predict data availability  Select features of URIs  Learn crawling selection [Huang, Gandon 2019]
  • 39. more data, more usages, more users
  • 40. NEW USAGES e.g. e-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]
  • 41. QUIZZES Automated generation of quizzes [Rodriguez-Rocha, Faron-Zucker et al.] rdf:type p rdfs:subClassOf ? ? ? ? RDF Q&A Generation NL Questions Generation Ranking Question SELECTION Knowledge Graph QuizQ&A
  • 42. LUDO: ontological modeling of serious games Learning Game KB Player’s profile & context Game design [Rodriguez-Rocha, Faron-Zucker et al.]
  • 43. DOCS & TOPICS link topics, questions, docs, [Dehors, Faron-Zucker et al.]
  • 44. MONITORING e.g. progress of learners [Dehors, Faron-Zucker et al.]
  • 45. EDUMICS  Ontology EduProgression: OWL modeling of scholar program  Ontology RefEduclever: new education referential for Educlever  Migration and persistence in graph databases  Reasoning, query, interactions, recommendation [Fokou, Faron et al. 2017]
  • 46. “ Smarter Cities – IBM Dublin [Lécué, 2015]
  • 47. QUESTION ROUTING  emails to the customer service (eg 350000/day “Crédit Mutuel”)  detect topics in order to “understand” a question  3 humans annotate 142 questions (Krippendorff’s Alpha 0,70)  NLP and semantic processing for features extraction  ML performance comparison for question classification Naive Bayes, Sequential Minimal Optimisation (SMO), Random Forest, RAndom k-labELsets (RAkEL) [Gazzotti, et al. 2017] NE recognition (L,T) Removing special characters Tokenization (L,T) Spell Checking (L,T) Lemmatization (L) Vector generation BOW/N-gram Replacement in documents Consider as feature Input Document ML workflow L: Language dependent - T: Text dependent Unbalanced Topics Metrics uni uni⨁bi uni+bi+tri uni⨁NE syn syn⨁hyper syn⨁NE Hamming Loss 0,0381 0,0370 0,0374 0,0373 0,0399 0,0412 0,0405
  • 48. PREDICT HOSPITALIZATION  Physician’s records classification in order to predict hospitalization [Gazzotti, Faron et al. 2017] Sexe Date Cause CISP2 ... History Observations H 25/04/2012 vaccin-antitétanique A44 ... Appendicite EN CP - Bon état général - auscult pulm libre; bdc rég sans souffle - tympans ok- Element Number Patients Consultations Past medical history Biometric data Semiotics Diagnosis Row of prescribed drugs Symptoms Health care procedures Additional examination Paramedical prescription Observations/notes 55 823 364 684 187 290 293 908 250 669 117 442 847 422 23 488 11 850 871 590 17 222 56 143
  • 49. PREDICT HOSPITALIZATION  Physician’s records classification in order to predict hospitalization  Augment data with structured knowledge and study impact on different prediction methods [Gazzotti, Faron et al. 2017] Sexe Date Cause CISP2 ... History Observations H 25/04/2012 vaccin-antitétanique A44 ... Appendicite EN CP - Bon état général - auscult pulm libre; bdc rég sans souffle - tympans ok- Element Number Patients Consultations Past medical history Biometric data Semiotics Diagnosis Row of prescribed drugs Symptoms Health care procedures Additional examination Paramedical prescription Observations/notes 55 823 364 684 187 290 293 908 250 669 117 442 847 422 23 488 11 850 871 590 17 222 56 143 (1) (2)
  • 50. ZOOMATHIA Cultural transmission of zoological knowledge from Antiquity to Middle Age [Faron Zucker, et al.]
  • 51. SPARQL MICRO-SERVICES TAXREF-LD Linked Data taxonomic register + Flickr + Biodiversity Heritage Library + Encyclopedia of Life trait bank + audio recordings from the Macaulay Library + MusicBrainz and… SSTL. [Michel , et al.]
  • 52. SCIENTIFIC HERITAGE  TAXREF Vocabulary  Data extraction and publication [Tounsi, Callou, Michel, Pajo, Faron Zucker et al.]
  • 53. WASABI augmenting musical experience with the Web [Buffa, Jauva et al.] NLP & LOD & Lyrics
  • 54. WASABI learning to analyzing the structure of lyrics [Fell, Cabrio et al.]
  • 55. 133 Toward a Web of Things
  • 56. “searching” comes in many flavors
  • 57. SEARCHING  exploratory search  question-answering DBPEDIA.FR (SPARQL end-point) 180 000 000 triples [Cojan, Boyer et al.]
  • 58. SEARCHING  exploratory search  question-answering DBPEDIA.FR (SPARQL end-point) 180 000 000 triples DISCOVERYHUB.CO semantic spreading activation new evaluation protocol [Marie, Giboin, Palagi et al.] [Cojan, Boyer et al.]
  • 59. SEARCHING  exploratory search  question-answering DBPEDIA.FR (SPARQL end-point) 180 000 000 triples DISCOVERYHUB.CO 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.] QAKiS.ORG
  • 61. learning linguistic patterns of queries [Cabrio et al.]
  • 68.
  • 71. INTERACTION design and evaluation Favoris Nouvelle recherche TEMPS Debut test Free Jazz 24s Free improvisation 33s (fiche) Avant-garde 47s John Coltrane (vidéo) 1min 28 Marc Ribot 2min11 (fiche) experimental music 2min18 2min23 Krautrock 2min31 (fiche) Progressive rock 2min37 2min39 Red (King Crimson album) 2m52 2min59 King Crimson 3min05 (fiche) Jazz fusion 3min18 (fiche) Free Jazz 3min32 3min54 Sun Ra 4min18 (fiche) Hard bop 4min41 4min47 Charles Mingus (vidéo) 5min29 (fiche) Third Stream (vidéo) 6min20 Bebop 7min19 Modal jazz 7min26 (fiche) Saxophone 7min51 7min55 Mel Collins 21st Century Schizoid Band Crimson Jazz Trio (fiche) King Crimson (fiche) Robert Fripp Miles Davis Thelonious Monk (fiche) Blue Note Record McCoy Tyner (fiche) Modal Jazz (fiche) Jazz Chick Corea (fiche) Jazz Fusion Return to Forever Mahavishnu Orchestra Shakti (band) U.Srinivas Bela Fleck Flecktones John McLaughlin (musician) Dixie Dregs FICHE Dixie Degs T Lavitz Jordan Rudess Behold… The Arctopus (fiche) Avant-garde metal Unexpected FICHE unexpected Dream Theater King Crimson (fiche) Jazz fusion King Crimson Tony Levin (fiche) Anderson Bruford Wakeman Howe (fiche) Rike Wakeman (vidéo) Fin test [Palagi, Marie, Giboin et al.]
  • 73. METHODS & CRITERIA  interaction design and evaluation  exploratory search process model [Palagi, Giboin et al. 2017] A. Define the search space B. Query (re)formulation C. Information gathering D. Put some information aside E. Pinpoint search F. Change of goal(s) G. Backward/forward steps H. Browsing results I. Results analysis J. Stop the search session Previous features Feature Next features NA A B ; J A ; F B G ; H ; I ; J D ; E ; I C D ; E ; F ; G ; H ; J E ; I D C ; F ; G ; J G ; H ; I E C ; D ; F ; G ; J C ; D ; E ; G ; H ; I F B ; H ; I ; J B ; D ; E ; H ; I G E ; F ; H ; I ; J B ; F ; G ; I H E ; F ; G ; ; I ; J B ; F ; G ; H I C ; D ; E ; F ; G ; H ; J all J NA
  • 74. CHEXPLORE Plugin to evaluate the interface of an exploratory search engine [Palagi, Giboin et al. 2017]
  • 75. 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.]
  • 76. 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.]
  • 77. 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.]
  • 80. DEBATES & EMOTIONS #IRC argument rejection attacks-disgust[Villata, Cabrio et al.]
  • 81. OPINIONS NLP, ML and arguments to monitor online image [Villata, Cabrio, et al.]
  • 82. ARGUMENT MINING ON CLINICAL TRIALS NLP, ML and arguments to assist Evidence-based medicine [Mayer, Cabrio, Villata] (…) To compare the intraocular pressure-lowering effect of latanoprost with that of dorzolamide when added to timolol. [. . . ] [The diurnal intraocular pressure reduction was significant in both groups (P < 0:001)]1. [The mean intraocular pressure reduction from baseline was 32% for the latanoprost plus timolol group and 20% for the dorzolamide plus timolol group]2. [The least square estimate of the mean diurnal intraocular pressure reduction after 3 months was -7.06 mm Hg in the latanoprost plus timolol group and -4.44 mm Hg in the dorzolamide plus timolol group (P < 0:001)]3. Drugs administered in both treatment groups were well tolerated. This study clearly showed that [the additive diurnal intraocular pressure-lowering effect of latanoprost is superior to that of dorzolamide in patients treated with timolol]1.(…) CLAIM EVIDENCE
  • 83. CREEP NLP and ML for cyber-bullying detection [Cabrio, Villata, et al.]
  • 85. “ « a Web-Augmented Interaction (WAI) is a user’s interaction with a system that is improved by allowing the system to access Web resources » [Gandon, Giboin, WebSci17]
  • 86. AZKAR remotely visit and interact with a museum through a robot and via the Web [Buffa et al.]
  • 87. ALOOF: Web and Perception [Cabrio, Basile et al.] Semantic Web-Mining and Deep Vision for Lifelong Object Discovery (ICRA 2017) Making Sense of Indoor Spaces using Semantic Web Mining and Situated Robot Perception (AnSWeR 2017)
  • 88. ALOOF: robots learning by reading on the Web  First Object Relation Knowledge Base: 46212 co-mentions, 49 tools, 14 rooms, 101 “possible location” relations, 696 tuples <entity, relation, frame>  Evaluation: 100 domestic instruments, 20 rooms, 2000 crowdsourcing judgements  Shared between robots through a shared Web knowledge base Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen [Cabrio, Basile et al. 2017]
  • 89. ALOOF: RDF dataset about objects [Cabrio, Basile et al.]  common sense knowledge about objects: classification, prototypical locations and actions  knowledge extracted from natural language parsing, crowdsourcing, distributional semantics, keyword linking, ...
  • 90.
  • 91. 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.]
  • 92. QUERY & INFER  graph rules and queries  deontic reasoning  induction CORESE  & G2 H2  & G1 H1 < Gn Hn RATIO4TA predict & explain abstract graph machine STTL [Hasan et al.] [Corby, Faron-Zucker et al.]
  • 93. 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 [Hasan et al.] [Tettamanzietal.] [Corby, Faron-Zucker et al.]
  • 94. QUERY & INFER  graph rules and queries  deontic reasoning  induction CORESE LICENTIA INDUCTION  & G2 H2  & G1 H1 < Gn Hn RATIO4TA predict & explain find missing knowledge deontic reasoning, license compatibility and composition abstract graph machine STTL [Hasan et al.] [Tettamanzietal.] [Villata et al.] [Corby, Faron-Zucker et al.]
  • 95. QUERY & INFER e.g. CORESE/KGRAM [Corby et al.]
  • 96. 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
  • 97. 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
  • 98. LDSCRIPT a Linked Data Script Language FUNCTION us:status(?x) { IF (EXISTS { ?x ex:hasSpouse ?y }||EXISTS { ?y ex:hasSpouse ?x }, ex:Married, ex:Single) } [Corby, Faron Zucker, Gandon, ISWC 2017]
  • 99. DISTRIBUTED inductive index creation for a triple store [Basse, Gandon, Mirbel]
  • 100. DISTRIBUTED Querying heterogeneous and distributed data [Gaignard,Corby et al.]
  • 101. rr:objectMap 1 1 0-1 0-1 1 0-1 0-1 0-1 0-1 1 1 rr:GraphMaprr:graphMap 0-1 xrr:logicalSource xrr:LogicalSource xrr:query Query String rml:iterator Iteration pattern rr:IRI, rr:BlankNode, rr:Literal, xrr:RdfList, xrr:RdfBag, xrr:RdfSeq, xrr:RdfAlt reference expr. xrr:nestedTermMap xrr:NestedTermMap rr:inverseExrpression xrr:reference reference expr. reference expr. rr:ObjectMap HETEROGENEITY xR2RML mapping language and SPARQL query rewriting [Michel et al.] <AbstractQuery> ::= <AtomicQuery> | <Query> | <Query> FILTER <SPARQL filter> | <Query> LIMIT <integer> <Query> ::= <AbstractQuery> INNER JOIN <AbstractQuery> ON {v1, … vn} | <AtomicQuery> AS child INNER JOIN <AtomicQuery> AS parent ON child/<Ref> = parent/<Ref> | <AbstractQuery> LEFT OUTER JOIN <AbstractQuery> ON {v1, … vn} | <AbstractQuery> UNION <AbstractQuery> <AtomicQuery> ::= {From, Project, Where, Limit} <Ref> ::= a valid xR2RML data element reference
  • 102. µSERVICES Linked Data access to Web APIs. [Michel et al.] SPARQL Client Service Logics Web API JSON-LD Profile SPARQL INSERT/CONSTR HTTP query JSON response Triple store SPARQL Micro-Service(1) (4) (2) (3) LD Client Web Server (1’) (4’) http://example.org/photo/472495
  • 103. QUERY & INFER e.g. Gephi+CORESE/KGRAM [Demairy, Gandon et al.]
  • 106. EXPLAIN  justify results  predict performances [Hasan et al.]
  • 107. INDUCTION learning axioms from linked data on the Web [Tettamanzi et al.]
  • 108. DISCOVERING ASSOCIATION RULES [Tran, Tettamanzi, 2017] isParent(x, y) ⇐ isFather(x, y) isParent(x, y) ⇐ isMother(x, y) Rules induced by (Facts1 ∪ Facts2) isMother(Maria, Anna) isMother(Maria, Alli) isFather(Carlos, Anna) isFathe(Carlos, Alli) isParent(Maria, Anna) isParent(Maria, Alli) isParent(Carlos, Anna) isParent(Carlos, Alli)
  • 109. DISCOVERING ASSOCIATION RULES  Discovering Multi-Relational Association Rules in the Semantic Web  Inductive Logic Programming (ILP) = Logic Programming ∩ Machine Learning  Learning logic rules from examples and background knowledge  Evolutionary approach (genetic algo) [Tran, Tettamanzi, 2017] H1 ∧ ... ∧ Hm ⇐ B1 ∧ B2 ∧ ... Bn
  • 110. DISTRIBUTED AI  Agent-based Simulation for a Multi-context BDI Recommender  Solitary agents vs social agents: social agents have better performance than solitary ones  Trust/Distrust score to detect malicious agents  Possibility theory: an uncertainty theory to handle incomplete information [Ben Othmane, Tettamanzi, Villata et al. 2017]
  • 111. UNCERTAINTY  Representing uncertainty theories  Reasoning on uncertainty contexts  Publishing it with linked data  Negotiating the theory over HTTP [Djebri, Tettamanzi, 2019]
  • 113. DEONTICS Legal Rules on the Semantic Web OWL + Named Graphs + SPARQL Rules Named Graph (state of affair) Subject Predicate Object http://ns.inria.fr/nrv-inst#StateOfAffairs1 Tom http://ns.inria.fr/nrv-inst#activity driving at 100km/h http://ns.inria.fr/nrv-inst#StateOfAffairs1 Tom http://www.w3.org/2000/01/rdf-schema#label Tom http://ns.inria.fr/nrv-inst#StateOfAffairs1 can't drive over 90km http://www.w3.org/1999/02/22-rdf-syntax-ns#type violated requirement http://ns.inria.fr/nrv-inst#StateOfAffairs1 can't drive over 90km has for violation http://ns.inria.fr/nrv-inst#StateOfAffairs1 http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://ns.inria.fr/nrv-inst#speed 100 http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://ns.inria.fr/nrv-inst#Driving http://ns.inria.fr/nrv-inst#StateOfAffairs1 driving at 100km/h http://www.w3.org/2000/01/rdf-schema#label "driving at 100km/h"@en Named Graph (state of affair) Subject Predicate Object http://ns.inria.fr/nrv-inst#StateOfAffairs2 Jim http://ns.inria.fr/nrv-inst#activity driving at 90km/h http://ns.inria.fr/nrv-inst#StateOfAffairs2 Jim http://www.w3.org/2000/01/rdf-schema#label Jim http://ns.inria.fr/nrv-inst#StateOfAffairs2 can't drive over 90km http://www.w3.org/1999/02/22-rdf-syntax-ns#type compliant requirement http://ns.inria.fr/nrv-inst#StateOfAffairs2 can't drive over 90km has for compliance http://ns.inria.fr/nrv-inst#StateOfAffairs2 http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://ns.inria.fr/nrv-inst#speed 90 http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://ns.inria.fr/nrv-inst#Driving http://ns.inria.fr/nrv-inst#StateOfAffairs2 driving at 90km/h http://www.w3.org/2000/01/rdf-schema#label "driving at 90km/h"@en [Gandon et al.]
  • 114. MonaLIA  reason & query on RDF metadata to build balanced, unambiguous, labelled training sets. 350 000 images of artworks RDF metadata based on external thesauri Joconde database from French museums [Bobasheva et al. 2017]
  • 115. MonaLIA  reason & query on RDF metadata to build balanced, unambiguous, labelled training sets.  transfer learning & CNN classifiers on targeted categories (topics, techniques, etc.)  reason & query RDF metadata of results to address silence, noise and explain 350 000 images of artworks RDF metadata based on external thesauri Joconde database from French museums (1) (2) [Bobasheva et al. 2017] animal  bird ? painting 
  • 116.
  • 120. 199 Toward a Web of Things
  • 121. 200 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)
  • 122.
  • 123. deduce data  model, schemas, ontologies, ... data data 15% progress
  • 124. learn data embeddings, parameters, configurations, … data data 30% progress
  • 125. sum intelligence  model, schemas, ontologies, ... embeddings, parameters, configurations, … data data 45% progress
  • 126. combine intelligence model, schemas, ontologies, ... embeddings, parameters, configurations, … data  60% progress
  • 127. remotely combine model, schemas, ontologies, … embeddings, parameters, configurations,…  Web 75% progress
  • 128. deeply combine data, knowledge, model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 90% progress
  • 129. combining AIs on the Web data, knowledge, model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 100% progress
  • 130. 209 one Web … a unique space in every meanings: data persons documents programs metadata
  • 131. Connected Animals, Animal-computer interaction (ACI) Herdsourcing: monitoring collective animal behavior
  • 132. IMAG_NE a Web linking all forms of Intelligence
  • 133. WIMMICS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing epistemic hybrid communitieslinked data usages and introspection contributions and traces
  • 134. 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    