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WIMMICS
Fabien GANDON, @fabien_gandon http://fabien.info
   
Web-Instrumented Man-
Machine Interactions,
Communities and Semantics
WIMMICS TEAM
▪ Inria
▪ CNRS
▪ University Côte D’Azur (UCA)
I3S
Web-Instrumented Man-Machine Interactions,
Communities and Semantics
MULTI-DISCIPLINARY TEAM
▪ 35~55 members
▪ ~15 nationalities
▪ 1 DR, 4 Professors
▪ 3CR, 3 Assistant professors
DR/Professors:
▪ Fabien GANDON, Inria, AI, KRR, Semantic Web, Social Web, K. Graphs
▪ Nhan LE THANH, UCA, Logics, KR, Emotions, Workflows, K. Graphs
▪ Peter SANDER, UCA, Web, Emotions
▪ Andrea TETTAMANZI, UCA, AI, Logics, Evo, Learning, Agents, K. Graphs
▪ Marco WINCKLER, UCA, Human-Computer Interaction, Web, K. Graphs
CR/Assistant Professors:
▪ Michel BUFFA, UCA, Web, Social Media, K. Graphs
▪ Elena CABRIO, UCA, NLP, KR, Linguistics, Q&A, Text Mining, K. Graphs
▪ Olivier CORBY, Inria, KR, AI, Sem. Web, Programming, K. Graphs
▪ Catherine FARON-ZUCKER, UCA, KR, AI, Semantic Web, K. Graphs
▪ Damien GRAUX, Inria, Linked Data, Sem. Web, Querying, K. Graphs
▪ Serena VILLATA, CNRS, AI, Argumentation, Licenses, Rights, K. Graphs
Research engineer: Franck MICHEL, CNRS, Linked Data, Integration, DB, K. Graphs
External:
▪ Andrei Ciortea (University of St. Gallen) Agents, WoT, Sem. Web, K. Graphs
▪ Nicolas DELAFORGE (Mnemotix) Sem. Web, KM, Integration, K. Graphs
▪ Alain GIBOIN, (Retired CR Inria), Interaction Design, KE, User & Task, K. Graphs
▪ Freddy LECUE (Thales, Montreal) AI, Logics, Mining, Big Data, S. Web , K. Graphs
CHALLENGE
to bridge social semantics and
formal semantics on the Web
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
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
RESEARCH CHALLENGES
The four research axes of Wimmics
-
contributing to research in AI and
Semantic Web along 4 axes:
1. Web-based user modeling and interaction
design
2. Social interactions and content analysis on
the Web
3. Knowledge extraction and representation
for and by linked data on the Web
4. Web-oriented and Web-dedicated artificial
intelligence algorithms


G2 H2

G1 H1
<
Gn Hn



knowledge graphs on theWeb
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
James Bridle’s twelve-volume encyclopedia of all
changes to the Wikipedia article on the Iraq War
booktwo.org
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.]
COVID ON THE WEB
[Corby, Michel, Gazzotti, Winckler, et al. 2019]
▪ integrate multiple datasets in heterogeneous formats
▪ perform information extraction to enrich
▪ perform inferences and validation to improve
▪ provide a public end-point for reuse
▪ provide querying and visualization services
vs. use cases…
• Scenario 1: Help clinicians analyze clinical trials and take evidence-based decisions
• Scenario 3: Help missions heads from Cancer Institute elaborate research programs
to study the links between cancer and coronavirus
121
[Giboin, et al.]
COVID ON THE WEB
RDF
translator
Jupyter Notebook
Python, R & analytics
Corese
engine
query
&
infer
ACTA Web application
visualization of argument graphs
Corese portal
Data browsing
MGExplorer
Data visualization
Open Data publication
Zenodo, Github, Virtuoso
Named
Entities
extractor
s
Covid-on-the-Web
dataset
LOD
ACTA pipeline
extraction of argument graphs
1
1
2
2
1
3
3
3
vocabularies
& datasets
3
3
3
2
Covid-19
Open
Research
Dataset
Process data &
derive “smarter” data
Means to exploit data
[Michel, Gazzotti, Gandon et al.]
COVID ON THE WEB
Biomedical
researchers
& managers
Data analysts
…
RDF
translator
Jupyter Notebook
Python, R & analytics
Corese
engine
query
&
infer
ACTA Web application
visualization of argument graphs
Corese portal
Data browsing
MGExplorer
Data visualization
Open Data publication
Zenodo, Github, Virtuoso Applications
Named
Entities
extractor
s
dereference, query, download
query, browse, analyze, make sense
Covid-on-the-Web
dataset
LOD
ACTA pipeline
extraction of argument graphs
1
1
2
2
1
3
3
3
vocabularies
& datasets
3
3
3
2
Covid-19
Open
Research
Dataset
Process data &
derive “smarter” data
Means to exploit data Biomedical research
[Michel, Gazzotti, Gandon et al.]
Dataset description No. RDF triples
dataset description + definition of a few properties 170
articles metadata (title, authors, DOIs, journal etc.) 3 722 381
named entities identified by Entity-fishing in articles titles/abstracts 35 049 832
named entities identified by Entity-fishing in articles bodies 1 156 611 321
named entities identified by Bioportal Annotator in articles titles/abstracts 104 430 547
named entities identified by DBpedia Spotlight in articles titles/abstracts 65 359 664
argumentative components and PICO elements by ACTA from articles titles/abstracts 7 469 234
Total 1 361 451 364
125
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
Quiz
Q&A
PREDICT STUDENTS
▪ a model of the students' learning
▪ predict success or failure to questions
▪ features from KG representations
▪ Logistic Regression (LR) / Factorization Machines
(FM) / Deep Factorization Machines (DeepFM)
[Rodriguez-Rocha, Faron, Ettorre, Michel et al. 2020]
Answers
Questions
s: students identifiers
q: questions identifiers
r: responses identifiers
a: number of attempts
w: number of wins
T: questions text embeddings
Q: graph embeddings of the questions
R: graph embeddings of the answers
e: extra group of calculated features:
question_difficulty,student_ability,
student_ability_progressive,
student_ability_progressive_question_difficulty
Features
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
integrating heterogenous sources
SCIENTIFIC HERITAGE
▪ TAXREF Vocabulary
▪ Data extraction and
publication
[Tounsi, Callou, Michel, Pajo, Faron Zucker et al.]
rr:objectMap
1
1
0-1
0-1
1
0-1
0-1
0-1
0-1
1
1
rr:GraphMap
rr: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
LD µSERVICES
APIs as linked data
[Michel , et al.]
SPARQL micro-
service
SPARQL SD
graph
Shapes
graph
SPARQL engine
Web
API
(5)
LD µSERVICES
APIs as linked data
[Michel , et al.]
HTML
JDON-LD
</>
SPARQL micro-
service
(1)
(4) SPARQL query
LD-based
application
SPARQL SD
graph
Shapes
graph
SPARQL engine
Web
API
(5)
LD µSERVICES
APIs as linked data
[Michel , et al.]
HTML
JDON-LD
</>
SPARQL micro-
service
(1)
(4) SPARQL query
LD-based
application
SPARQL SD
graph
Shapes
graph
SPARQL engine
Web
API
(5)
&
uncertainty in Web data
UNCERTAINTY
▪ Representing uncertainty theories
▪ Publishing it with linked data
▪ Negotiating the theory over HTTP
▪ Combining uncertainty statements
[Djebri, Tettamanzi, Gandon, 2019]
UNCERTAINTY
publishing theories and calculi as linked data
[Djebri, Tettamanzi, Gandon, 2019]
prob:Probability a munc:UncertaintyApproach;
munc:hasUncertaintyFeature prob:probabilityValue;
munc:hasUncertaintyOperator prob:and.
prob:probabilityValue prob:and prob:multiplyProbability.
prob:Probability prob:probabilityValue
prob:and
ex:multiplyProbability
munc:hasUncertainty
Feature
munc:hasUncertainty
Operator
UNCERTAINTY
publishing theories and calculi as linked data
[Djebri, Tettamanzi, Gandon, 2019]
function prob:multiplyProbability(?s1, ?s2, ?c) {
let(?v1 = munc:getMeta(?s1, prob:Probability)){
if(prob:verifyIndependent(?s1, ?s2) == true)
?v2 = munc:getMeta(?s2, prob:Probability, xt:list(?s1, ?c))
return (?v1 * ?v2)
} else {
?v2 = munc:getMeta(?s2, prob:Probability)
return (?v1 * ?v2)
}
}
}
prob:Probability a munc:UncertaintyApproach;
munc:hasUncertaintyFeature prob:probabilityValue;
munc:hasUncertaintyOperator prob:and.
prob:probabilityValue prob:and prob:multiplyProbability.
prob:Probability prob:probabilityValue
prob:and
ex:multiplyProbability
munc:hasUncertainty
Feature
munc:hasUncertainty
Operator
UNCERTAINTY
translate and negotiate theories
[Djebri et al 2019]
• Specify uncertainty in parameter linked to the format
• GET /some/resource HTTP/1.1
Accept:
text/turtle;uncertainty="http://example.com/Probability";q=0.8,
text/turtle;uncertainty="http://example.com/Possibility";q=0.2;
• Use uncertainty as a profile : prof-Conneg
• GET /some/resource HTTP/1.1
Accept: text/turtle;q=0.8;profile="prob:Probability",
text/turtle;q=0.2;profile="poss:Possibility"
• HEAD /some/resource HTTP/1.1
Accept: text/turtle;q=0.9,application/rdf+xml;q=0.5
Link: <http://example.com/Probability>; rel="profile" (RFC 6906)
• GET /some/resource HTTP/1.1
MoReWAIS Mobile Read Write Access and Intermittent to Semantic Web
France (Wimmics, Inria) – Senegal (LANI, UGB Saint-Louis) Project
explore the specificities (advantages and constraints) of mobile P2P knowledge
sharing and addressing its limitations (e.g. intermittent access, limited resources)
[Toure et al.]
&
MoRAI: Geographic and Semantic Overlay Network
• Three-level P2P architecture : mobile peers, super-peers and remote sources
• Random Peer Sampling (RPS) overlay +
Semantic Overlay Network (SON) +
Geographic Overlay Network (GON)
• Experimental validation/simulation
[Toure et al.]
CRAWLING
▪ Predict data availability
▪ Select features of URIs
▪ Learn crawling selection
(KNN/NaiveBayes/SVM)
▪ Online learning w. crawling
(FTRL-proximal algorithm)
[Huang, Gandon 2019]
QUERY
• automatically suggest relevant data sources to solve a query
• sets of path features: star, sink, chain
• approximate containment search: locality sensitive hashing
[Huang, Gandon 2020]
“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. DiscoveryHub
exploratory search
[Marie, Gandon, Ribière.]
semantic spreading activation
SIMILARITY
FILTERING
discoveryhub.co
[Marie, Gandon, Ribière.]
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
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 CenturySchizoid 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
MahavishnuOrchestra
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
TonyLevin
(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. 2018]
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. 2018]
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
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 gave 49 tools, 14
rooms, 101 “possible location” relations,
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: 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, ...
WASABI
Web-augmented music
interactions
[Buffa et al.]
WASABI
Web-augmented music
interactions
[Buffa et al.]
[Fell et al.]
points of view.
DEBATES & EMOTIONS
#IRC
[Villata, Cabrio et al.]
DEBATES & EMOTIONS
#IRC argument rejection
attacks-disgust
[Villata, Cabrio et al.]
SENTIMENTS
▪ sentiment recognition in search results
▪ Automatic detection of affect
▪ Automatically interplay emotions to sentiment polarity
▪ Broader sense of sentiment ( Valence, Arousal, Dominance)
▪ Use case: Brexit scenario
▪ Propaganda detection based on argumentative techniques
[Vorakitphan, Cabrio, Villata 2020]
OPINIONS
NLP, ML and arguments
to monitor online image
[Villata, Cabrio, et al.]
ARGUMENT MINING ON
CLINICAL TRIALS
▪ NLP, ML and arguments
▪ assist evidence-based medicine
▪ support doctors and clinicians
▪ identify doc. for certain disease
▪ analyze argumentative content
and PICO elements
[Mayer, Cabrio, Villata]
ARGUMENT MINING ON
POLITICAL SPEECHES
▪ NLP and Machine Learning.
▪ Support historians/social science scholars
▪ Analyze arguments in political speeches
▪ DISPUTool : 39 political debates,
last 50 years of US presidential
campaigns (1960-2016)
[Mayer, Cabrio, Villata]
CYBERBULLYING
CREEP EU project: detect and prevent
[Corazza, Arslan, Cabrio, Villata]
CYBERBULLYING
CREEP EU project: detect and prevent
[Corazza, Arslan, Cabrio, Villata]
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.]
[Tettamanzi
et
al.]
[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.]
[Tettamanzi
et
al.]
[Villata et al.]
[Corby, Faron-Zucker et al.]
QUERY & INFER
e.g. CORESE/KGRAM
[Corby et al. since 1999]
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
 
 
 





=



1
2
1 ,
, )
(
2
1
2
1
2
2
1
2
1
)
,
(
let
;
)
,
( t
t
t
t
t t
depth
H
c t
t
l
t
t
H
t
t c
 ( )
)
,
(
)
,
(
min
)
,
(
let
)
,
( 2
1
,
2
1
2
2
1 2
1
t
t
l
t
t
l
t
t
dist
H
t
t c
c H
H
t
t
t
t
c +
=

 

vehicle
car
O
truck
t1(x)t2(x) → d(t1,t2)< threshold
Lymphoma
Cancer
GD
GQ
216
Lymphoma
Cancer
Lymphoma(x) Cancer(x)
GD
GQ
Cancer
Lymphoma
O
217
FO → R  GD  GQ
mapping modulo an ontology
Lymphoma
Cancer
Lymphoma(x) Cancer(x)
GD
GQ
Cancer
Lymphoma
O
[Corby et al.]
AI methods: knowledge graphs, ontology-based formalisms, querying, validating and reasoning
218
Query for co-occurrence of cancers & coronaviruses with R Jupyter Notebooks…
219
Visualisation pipeline
[Menin, Winckler, Corby, Giboin, Faron et al. 2020]
220
?
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]
SPARQL ENDPOINT ACCESS CONTROL
Protect SPARQL endpoint from hostile actions
Set of protected Features:
SPARQL Update, Load RDF data, Service clause
Set of Access Rights:
PUBLIC, PROTECTED, PRIVATE
Assign Access Rights to Features:
SPARQL Update -> PRIVATE
Service <http://fr.dbpedia.org> -> PROTECTED
Assign Access Right to User Action:
User query -> PUBLIC
PUBLIC action cannot access PRIVATE Feature
[Corby, 2021]
RDF & SPARQL ACCESS CONTROL
Assign Access Right to RDF triples and SPARQL
Queries
• SPARQL Query has access to subset of RDF triples
• RDF Graph extended with Access Rights
• SPARQL Interpreter extended with Access Rights
e.g.
Assign Access Rights to RDF triples according to URIs or namespaces
URI foaf:address -> PRIVATE
Namespace foaf: -> PUBLIC
select * where { ?x ?p ?y } -> PUBLIC
Query can access PUBLIC foaf:name
Query cannot access PRIVATE foaf:address
[Corby, 2021]
DISTRIBUTED
inductive index creation for a
triple store [Basse, Gandon, Mirbel]
DISTRIBUTED
Querying heterogeneous and
distributed data [Gaignard,Corby et al.]
This project has received funding from the European Union's Horizon 2020
research and innovation programme under grant agreement 825619.
ONTOLOGY FOR AI ITSELF
▪ ontology and metadata of AI resources
▪ SHACL to validate AI4EU these RDF graphs
▪ online endpoint http://corese.inria.fr
▪ predefined SPARQL queries, SHACL shapes, display
[Corby et al., 2019]
mining interesting association rules
AI methods: clustering + community detection + dimensionality
reduction (auto-encoder) + Frequent Pattern Growth
[Cadorel, Tettamanzi]
241
[WI-IAT 2020]
mining interesting association rules
AI methods: clustering + community detection + dimensionality
reduction (auto-encoder) + Frequent Pattern Growth
• hidden patterns to enrich the dataset
• novel hypotheses for biomedical research
[Cadorel, Tettamanzi]
242
[WI-IAT 2020]
mining interesting association rules
AI methods: clustering + community detection + dimensionality
reduction (auto-encoder) + Frequent Pattern Growth
• hidden patterns to enrich the dataset
• novel hypotheses for biomedical research
• error detection in the dataset
• relevant clusters & communities for navigation
[Cadorel, Tettamanzi]
243
[WI-IAT 2020]
Visualization of Association found in Covid dataset
[Menin, Cadorel, Tettamanzi, Winckler]
244
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.]
will they blend ?
PREDICT HOSPITALIZATION
▪ Predict hospitalization from
Physician’s records classification
▪ Augment records data with
Web knowledge graphs
▪ Study impact on prediction
[Gazzotti, Faron, Gandon et al. 2020]
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)
PRIMEGE
Image Metadata Score 
portrait
50350012455
C:Jocondejoconde0138m503501_d0012455-000_p.jpg
cheval:
0.999
Image Metadata Score 
figure (saint Eloi de Noyon, évêque, en pied, bénédiction,
vêtement liturgique, mitre, attribut, cheval, marteau, outil :
ferronnerie)
000SC022652
C:/Joconde/joconde0355/m079806_bsa0030101_p.jpg
cheval:
0.006
MonaLIA
▪ reason & query on RDF to build training sets.
▪ transfer learning & CNN classifiers on targeted
categories (topics, techniques, etc.)
▪ reason & query RDF 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)
(3)
[Bobasheva, Gandon, Precioso, 2021]
(2)
MonaLIA 2.0 Approach
• SPARQL+RDFS+SKOS on metadata to extract training and
test subsets of images
• create labeled training and test sets including the “narrower”
categories according to Garnier Thesaurus
• create “missing” links between some categories
• balance number of training images per class
• filter out certain categories and images
• Train Multi-Label Deep Learning classifier
• select state-of-the-art pre-trained CNN model
• adapt the model to multi-label classification
• fine-tune model on artwork images
• optimize model hyperparameters for best performance
• Apply trained model and extend metadata
• run all the images through the trained classifier
• record the prediction score as RDF triples
• SPARQL on extended metadata to search the database
(Maasai & Wimmics)
Detecting “noise”
By querying the extended metadata for the objects with low scores we
can detect the “noise” in the represented subject annotation
Image Metadata Score
figure (saint Eloi de Noyon, évêque, en pied, bénédiction, vêtement
liturgique, mitre, attribut, cheval, marteau, outil : ferronnerie)
000SC022652
C:/Joconde/joconde0355/m079806_bsa0030101_p.jpg
cheval: 0.006
figures bibliques (Vierge à l'Enfant, à mi-corps, assis, Enfant Jésus : nu,
livre);fond de paysage (colline, cours d'eau, barque, cavalier)
000PE027041
C:/Joconde/joconde0001/m503604_90ee1719_p.jpg
cheval: 0.009
scène (satirique : Bismarck Otto von : Gargantua, repas, cheval, boisson :
vin)
5002E006121
C:/Joconde/joconde0074/m500202_atpico-g70128_p.jpg
cheval: 0.011
Detecting “silence”
By querying the extended metadata for the object with high scores and
without object mentioned in annotation we can detect the “silence” in the
annotation
Image Metadata Score
portrait
50350012455
C:Jocondejoconde0138m503501_d0012455-000_p.jpg
cheval: 0.999
scène historique (guerre de siège : Lawfeld, Louis XV, Saxe maréchal de,
bataille rangée)
000PE004371
C:Jocondejoconde0634m507704_79ee519_p.jpg
cheval: 0.999
figure (sainte Jeanne d'Arc, jeune fille, équestre passant, armure,
asque, épée)
M0301000355
C:Jocondejoconde0617m030106_007305_p.jpg
cheval: 0.997
Ranking of search results
Running the same query on the Extended Joconde database and sorting by
score gives a better result putting the image in the second place
Image Metadata Score
représentation animalière (épagneul, debout)
M0341003743
C:Jocondejoconde0534m034186_006932_p.jpg
chien: 0.994
scène (chasse : lévrier, lièvre)
M0810001165
C:Jocondejoconde0466m081003_028491_p.jpg
chien: 0.993
représentation animalière (mise à mort, gros gibier : sanglier, chasse à
courre, chien)
00000105149
C:Jocondejoconde0107m505206_oa817_p.jpg
chien: 0.990
Hypermedia MAS
▪ Bridging Web architecture and Multi-Agent Systems architecture
▪ Hypermedia Communities of People and Autonomous Agents
▪ Define an architectural style for Hypermedia MAS
▪ Define declarative languages and mechanisms for specifying, enacting, and
regulating interactions among people and autonomous agents in
Hypermedia MAS
▪ Develop an open-source software infrastructure for Hypermedia MAS that
enables the deployment of hybrid communities on the Web
▪ Demonstrate the deployment of prototypical hybrid communities in two
application areas: (i) Industry 4.0 and (ii) tackling online disinformation.
http://hyperagents.gitlab.emse.fr/#
Web 1.0, …
Web 1.0, 2.0…
price
convert?
person
other sellers?
Web 1.0, 2.0, 3.0 …
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)
275
Toward a Web of Things
Make the Web AI-friendly
content, links, metadata, etc.
data, knowledge, etc.
AI Web bots: chat bots, recommenders, facilitators, etc.
configuration, parameters, embeddings, services,
communication, etc.
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
285
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
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 communities
linked data
usages and introspection
contributions and traces
www
mmm
world wide web
massively multidisciplinary method
WIMMICS
Web-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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Wimmics Overview 2021

  • 1. WIMMICS Fabien GANDON, @fabien_gandon http://fabien.info     Web-Instrumented Man- Machine Interactions, Communities and Semantics
  • 2. WIMMICS TEAM ▪ Inria ▪ CNRS ▪ University Côte D’Azur (UCA) I3S Web-Instrumented Man-Machine Interactions, Communities and Semantics
  • 3. MULTI-DISCIPLINARY TEAM ▪ 35~55 members ▪ ~15 nationalities ▪ 1 DR, 4 Professors ▪ 3CR, 3 Assistant professors DR/Professors: ▪ Fabien GANDON, Inria, AI, KRR, Semantic Web, Social Web, K. Graphs ▪ Nhan LE THANH, UCA, Logics, KR, Emotions, Workflows, K. Graphs ▪ Peter SANDER, UCA, Web, Emotions ▪ Andrea TETTAMANZI, UCA, AI, Logics, Evo, Learning, Agents, K. Graphs ▪ Marco WINCKLER, UCA, Human-Computer Interaction, Web, K. Graphs CR/Assistant Professors: ▪ Michel BUFFA, UCA, Web, Social Media, K. Graphs ▪ Elena CABRIO, UCA, NLP, KR, Linguistics, Q&A, Text Mining, K. Graphs ▪ Olivier CORBY, Inria, KR, AI, Sem. Web, Programming, K. Graphs ▪ Catherine FARON-ZUCKER, UCA, KR, AI, Semantic Web, K. Graphs ▪ Damien GRAUX, Inria, Linked Data, Sem. Web, Querying, K. Graphs ▪ Serena VILLATA, CNRS, AI, Argumentation, Licenses, Rights, K. Graphs Research engineer: Franck MICHEL, CNRS, Linked Data, Integration, DB, K. Graphs External: ▪ Andrei Ciortea (University of St. Gallen) Agents, WoT, Sem. Web, K. Graphs ▪ Nicolas DELAFORGE (Mnemotix) Sem. Web, KM, Integration, K. Graphs ▪ Alain GIBOIN, (Retired CR Inria), Interaction Design, KE, User & Task, K. Graphs ▪ Freddy LECUE (Thales, Montreal) AI, Logics, Mining, Big Data, S. Web , K. Graphs
  • 4. 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. 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)
  • 7. 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
  • 9. The four research axes of Wimmics - contributing to research in AI and Semantic Web along 4 axes: 1. Web-based user modeling and interaction design 2. Social interactions and content analysis on the Web 3. Knowledge extraction and representation for and by linked data on the Web 4. Web-oriented and Web-dedicated artificial intelligence algorithms   G2 H2  G1 H1 < Gn Hn   
  • 11. e.g. cultural data is a weapon of mass construction
  • 12. 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.]
  • 13. 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…
  • 15. REUSE ▪ build and help build applications DBPEDIA.FR (extraction, end-point) 180 000 000 triples Zone 47 BBC HdA Lab RAWebSem MCC Contest MonaLIA
  • 16.
  • 17. James Bridle’s twelve-volume encyclopedia of all changes to the Wikipedia article on the Iraq War booktwo.org
  • 18. 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]
  • 21. DEMO Facetted history portals Elections in France Death of C. Lee Events in Ukraine
  • 22. DBPEDIA & STTL declarative transformation language from RDF to text formats (XML, JSON, HTML, Latex, natural language, GML, …) [Cojan, Corby, Faron-Zucker et al.]
  • 23. COVID ON THE WEB [Corby, Michel, Gazzotti, Winckler, et al. 2019] ▪ integrate multiple datasets in heterogeneous formats ▪ perform information extraction to enrich ▪ perform inferences and validation to improve ▪ provide a public end-point for reuse ▪ provide querying and visualization services
  • 24. vs. use cases… • Scenario 1: Help clinicians analyze clinical trials and take evidence-based decisions • Scenario 3: Help missions heads from Cancer Institute elaborate research programs to study the links between cancer and coronavirus 121 [Giboin, et al.]
  • 25. COVID ON THE WEB RDF translator Jupyter Notebook Python, R & analytics Corese engine query & infer ACTA Web application visualization of argument graphs Corese portal Data browsing MGExplorer Data visualization Open Data publication Zenodo, Github, Virtuoso Named Entities extractor s Covid-on-the-Web dataset LOD ACTA pipeline extraction of argument graphs 1 1 2 2 1 3 3 3 vocabularies & datasets 3 3 3 2 Covid-19 Open Research Dataset Process data & derive “smarter” data Means to exploit data [Michel, Gazzotti, Gandon et al.]
  • 26. COVID ON THE WEB Biomedical researchers & managers Data analysts … RDF translator Jupyter Notebook Python, R & analytics Corese engine query & infer ACTA Web application visualization of argument graphs Corese portal Data browsing MGExplorer Data visualization Open Data publication Zenodo, Github, Virtuoso Applications Named Entities extractor s dereference, query, download query, browse, analyze, make sense Covid-on-the-Web dataset LOD ACTA pipeline extraction of argument graphs 1 1 2 2 1 3 3 3 vocabularies & datasets 3 3 3 2 Covid-19 Open Research Dataset Process data & derive “smarter” data Means to exploit data Biomedical research [Michel, Gazzotti, Gandon et al.]
  • 27. Dataset description No. RDF triples dataset description + definition of a few properties 170 articles metadata (title, authors, DOIs, journal etc.) 3 722 381 named entities identified by Entity-fishing in articles titles/abstracts 35 049 832 named entities identified by Entity-fishing in articles bodies 1 156 611 321 named entities identified by Bioportal Annotator in articles titles/abstracts 104 430 547 named entities identified by DBpedia Spotlight in articles titles/abstracts 65 359 664 argumentative components and PICO elements by ACTA from articles titles/abstracts 7 469 234 Total 1 361 451 364 125
  • 28. more data, more usages, more users
  • 29. NEW USAGES e.g. e-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]
  • 30. 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 Quiz Q&A
  • 31. PREDICT STUDENTS ▪ a model of the students' learning ▪ predict success or failure to questions ▪ features from KG representations ▪ Logistic Regression (LR) / Factorization Machines (FM) / Deep Factorization Machines (DeepFM) [Rodriguez-Rocha, Faron, Ettorre, Michel et al. 2020] Answers Questions s: students identifiers q: questions identifiers r: responses identifiers a: number of attempts w: number of wins T: questions text embeddings Q: graph embeddings of the questions R: graph embeddings of the answers e: extra group of calculated features: question_difficulty,student_ability, student_ability_progressive, student_ability_progressive_question_difficulty Features
  • 32. 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]
  • 33. “ Smarter Cities – IBM Dublin [Lécué, 2015]
  • 34. 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
  • 36. SCIENTIFIC HERITAGE ▪ TAXREF Vocabulary ▪ Data extraction and publication [Tounsi, Callou, Michel, Pajo, Faron Zucker et al.]
  • 37. rr:objectMap 1 1 0-1 0-1 1 0-1 0-1 0-1 0-1 1 1 rr:GraphMap rr: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
  • 38. 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
  • 39. LD µSERVICES APIs as linked data [Michel , et al.] SPARQL micro- service SPARQL SD graph Shapes graph SPARQL engine Web API (5)
  • 40. LD µSERVICES APIs as linked data [Michel , et al.] HTML JDON-LD </> SPARQL micro- service (1) (4) SPARQL query LD-based application SPARQL SD graph Shapes graph SPARQL engine Web API (5)
  • 41. LD µSERVICES APIs as linked data [Michel , et al.] HTML JDON-LD </> SPARQL micro- service (1) (4) SPARQL query LD-based application SPARQL SD graph Shapes graph SPARQL engine Web API (5) &
  • 43. UNCERTAINTY ▪ Representing uncertainty theories ▪ Publishing it with linked data ▪ Negotiating the theory over HTTP ▪ Combining uncertainty statements [Djebri, Tettamanzi, Gandon, 2019]
  • 44. UNCERTAINTY publishing theories and calculi as linked data [Djebri, Tettamanzi, Gandon, 2019] prob:Probability a munc:UncertaintyApproach; munc:hasUncertaintyFeature prob:probabilityValue; munc:hasUncertaintyOperator prob:and. prob:probabilityValue prob:and prob:multiplyProbability. prob:Probability prob:probabilityValue prob:and ex:multiplyProbability munc:hasUncertainty Feature munc:hasUncertainty Operator
  • 45. UNCERTAINTY publishing theories and calculi as linked data [Djebri, Tettamanzi, Gandon, 2019] function prob:multiplyProbability(?s1, ?s2, ?c) { let(?v1 = munc:getMeta(?s1, prob:Probability)){ if(prob:verifyIndependent(?s1, ?s2) == true) ?v2 = munc:getMeta(?s2, prob:Probability, xt:list(?s1, ?c)) return (?v1 * ?v2) } else { ?v2 = munc:getMeta(?s2, prob:Probability) return (?v1 * ?v2) } } } prob:Probability a munc:UncertaintyApproach; munc:hasUncertaintyFeature prob:probabilityValue; munc:hasUncertaintyOperator prob:and. prob:probabilityValue prob:and prob:multiplyProbability. prob:Probability prob:probabilityValue prob:and ex:multiplyProbability munc:hasUncertainty Feature munc:hasUncertainty Operator
  • 46. UNCERTAINTY translate and negotiate theories [Djebri et al 2019] • Specify uncertainty in parameter linked to the format • GET /some/resource HTTP/1.1 Accept: text/turtle;uncertainty="http://example.com/Probability";q=0.8, text/turtle;uncertainty="http://example.com/Possibility";q=0.2; • Use uncertainty as a profile : prof-Conneg • GET /some/resource HTTP/1.1 Accept: text/turtle;q=0.8;profile="prob:Probability", text/turtle;q=0.2;profile="poss:Possibility" • HEAD /some/resource HTTP/1.1 Accept: text/turtle;q=0.9,application/rdf+xml;q=0.5 Link: <http://example.com/Probability>; rel="profile" (RFC 6906) • GET /some/resource HTTP/1.1
  • 47. MoReWAIS Mobile Read Write Access and Intermittent to Semantic Web France (Wimmics, Inria) – Senegal (LANI, UGB Saint-Louis) Project explore the specificities (advantages and constraints) of mobile P2P knowledge sharing and addressing its limitations (e.g. intermittent access, limited resources) [Toure et al.] &
  • 48. MoRAI: Geographic and Semantic Overlay Network • Three-level P2P architecture : mobile peers, super-peers and remote sources • Random Peer Sampling (RPS) overlay + Semantic Overlay Network (SON) + Geographic Overlay Network (GON) • Experimental validation/simulation [Toure et al.]
  • 49. CRAWLING ▪ Predict data availability ▪ Select features of URIs ▪ Learn crawling selection (KNN/NaiveBayes/SVM) ▪ Online learning w. crawling (FTRL-proximal algorithm) [Huang, Gandon 2019]
  • 50. QUERY • automatically suggest relevant data sources to solve a query • sets of path features: star, sink, chain • approximate containment search: locality sensitive hashing [Huang, Gandon 2020]
  • 51. “searching” comes in many flavors
  • 52. SEARCHING ▪ exploratory search ▪ question-answering DBPEDIA.FR (SPARQL end-point) 180 000 000 triples [Cojan, Boyer et al.]
  • 53. 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.]
  • 54. 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
  • 58. learning linguistic patterns of queries [Cabrio et al.]
  • 62.
  • 65. 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 CenturySchizoid 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 MahavishnuOrchestra 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 TonyLevin (fiche) Anderson Bruford Wakeman Howe (fiche) Rike Wakeman (vidéo) Fin test [Palagi, Marie, Giboin et al.]
  • 67. METHODS & CRITERIA ▪ interaction design and evaluation ▪ exploratory search process model [Palagi, Giboin et al. 2018] 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
  • 68. CHEXPLORE Plugin to evaluate the interface of an exploratory search engine [Palagi, Giboin et al. 2018]
  • 70. “ « 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]
  • 71. AZKAR remotely visit and interact with a museum through a robot and via the Web [Buffa et al.]
  • 72. 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)
  • 73. 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.]
  • 74. ALOOF: robots learning by reading on the Web First Object Relation Knowledge Base: 46.212 co-mentions gave 49 tools, 14 rooms, 101 “possible location” relations, Annie cuts the bread in the kitchen with her knife dbp:Knife aloof:Location dbp:Kitchen [Cabrio, Basile et al.]
  • 75. 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]
  • 76. 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, ...
  • 81. DEBATES & EMOTIONS #IRC argument rejection attacks-disgust [Villata, Cabrio et al.]
  • 82. SENTIMENTS ▪ sentiment recognition in search results ▪ Automatic detection of affect ▪ Automatically interplay emotions to sentiment polarity ▪ Broader sense of sentiment ( Valence, Arousal, Dominance) ▪ Use case: Brexit scenario ▪ Propaganda detection based on argumentative techniques [Vorakitphan, Cabrio, Villata 2020]
  • 83. OPINIONS NLP, ML and arguments to monitor online image [Villata, Cabrio, et al.]
  • 84. ARGUMENT MINING ON CLINICAL TRIALS ▪ NLP, ML and arguments ▪ assist evidence-based medicine ▪ support doctors and clinicians ▪ identify doc. for certain disease ▪ analyze argumentative content and PICO elements [Mayer, Cabrio, Villata]
  • 85. ARGUMENT MINING ON POLITICAL SPEECHES ▪ NLP and Machine Learning. ▪ Support historians/social science scholars ▪ Analyze arguments in political speeches ▪ DISPUTool : 39 political debates, last 50 years of US presidential campaigns (1960-2016) [Mayer, Cabrio, Villata]
  • 86. CYBERBULLYING CREEP EU project: detect and prevent [Corazza, Arslan, Cabrio, Villata]
  • 87. CYBERBULLYING CREEP EU project: detect and prevent [Corazza, Arslan, Cabrio, Villata]
  • 88.
  • 89. 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.]
  • 90. 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.]
  • 91. 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.] [Tettamanzi et al.] [Corby, Faron-Zucker et al.]
  • 92. 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.] [Tettamanzi et al.] [Villata et al.] [Corby, Faron-Zucker et al.]
  • 93. QUERY & INFER e.g. CORESE/KGRAM [Corby et al. since 1999]
  • 94. 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
  • 95. FO → R  GF  GR mapping modulo an ontology car vehicle car(x)vehicle(x) GF GR vehicle car O truck car            =    1 2 1 , , ) ( 2 1 2 1 2 2 1 2 1 ) , ( let ; ) , ( t t t t t t depth H c t t l t t H t t c  ( ) ) , ( ) , ( min ) , ( let ) , ( 2 1 , 2 1 2 2 1 2 1 t t l t t l t t dist H t t c c H H t t t t c + =     vehicle car O truck t1(x)t2(x) → d(t1,t2)< threshold
  • 98. FO → R  GD  GQ mapping modulo an ontology Lymphoma Cancer Lymphoma(x) Cancer(x) GD GQ Cancer Lymphoma O [Corby et al.] AI methods: knowledge graphs, ontology-based formalisms, querying, validating and reasoning 218
  • 99. Query for co-occurrence of cancers & coronaviruses with R Jupyter Notebooks… 219
  • 100. Visualisation pipeline [Menin, Winckler, Corby, Giboin, Faron et al. 2020] 220 ?
  • 101. 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]
  • 102. SPARQL ENDPOINT ACCESS CONTROL Protect SPARQL endpoint from hostile actions Set of protected Features: SPARQL Update, Load RDF data, Service clause Set of Access Rights: PUBLIC, PROTECTED, PRIVATE Assign Access Rights to Features: SPARQL Update -> PRIVATE Service <http://fr.dbpedia.org> -> PROTECTED Assign Access Right to User Action: User query -> PUBLIC PUBLIC action cannot access PRIVATE Feature [Corby, 2021]
  • 103. RDF & SPARQL ACCESS CONTROL Assign Access Right to RDF triples and SPARQL Queries • SPARQL Query has access to subset of RDF triples • RDF Graph extended with Access Rights • SPARQL Interpreter extended with Access Rights e.g. Assign Access Rights to RDF triples according to URIs or namespaces URI foaf:address -> PRIVATE Namespace foaf: -> PUBLIC select * where { ?x ?p ?y } -> PUBLIC Query can access PUBLIC foaf:name Query cannot access PRIVATE foaf:address [Corby, 2021]
  • 104. DISTRIBUTED inductive index creation for a triple store [Basse, Gandon, Mirbel]
  • 105. DISTRIBUTED Querying heterogeneous and distributed data [Gaignard,Corby et al.]
  • 106. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 825619. ONTOLOGY FOR AI ITSELF ▪ ontology and metadata of AI resources ▪ SHACL to validate AI4EU these RDF graphs ▪ online endpoint http://corese.inria.fr ▪ predefined SPARQL queries, SHACL shapes, display [Corby et al., 2019]
  • 107. mining interesting association rules AI methods: clustering + community detection + dimensionality reduction (auto-encoder) + Frequent Pattern Growth [Cadorel, Tettamanzi] 241 [WI-IAT 2020]
  • 108. mining interesting association rules AI methods: clustering + community detection + dimensionality reduction (auto-encoder) + Frequent Pattern Growth • hidden patterns to enrich the dataset • novel hypotheses for biomedical research [Cadorel, Tettamanzi] 242 [WI-IAT 2020]
  • 109. mining interesting association rules AI methods: clustering + community detection + dimensionality reduction (auto-encoder) + Frequent Pattern Growth • hidden patterns to enrich the dataset • novel hypotheses for biomedical research • error detection in the dataset • relevant clusters & communities for navigation [Cadorel, Tettamanzi] 243 [WI-IAT 2020]
  • 110. Visualization of Association found in Covid dataset [Menin, Cadorel, Tettamanzi, Winckler] 244
  • 112. 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. PREDICT HOSPITALIZATION ▪ Predict hospitalization from Physician’s records classification ▪ Augment records data with Web knowledge graphs ▪ Study impact on prediction [Gazzotti, Faron, Gandon et al. 2020] 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) PRIMEGE
  • 115. Image Metadata Score  portrait 50350012455 C:Jocondejoconde0138m503501_d0012455-000_p.jpg cheval: 0.999 Image Metadata Score  figure (saint Eloi de Noyon, évêque, en pied, bénédiction, vêtement liturgique, mitre, attribut, cheval, marteau, outil : ferronnerie) 000SC022652 C:/Joconde/joconde0355/m079806_bsa0030101_p.jpg cheval: 0.006 MonaLIA ▪ reason & query on RDF to build training sets. ▪ transfer learning & CNN classifiers on targeted categories (topics, techniques, etc.) ▪ reason & query RDF 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) (3) [Bobasheva, Gandon, Precioso, 2021] (2)
  • 116. MonaLIA 2.0 Approach • SPARQL+RDFS+SKOS on metadata to extract training and test subsets of images • create labeled training and test sets including the “narrower” categories according to Garnier Thesaurus • create “missing” links between some categories • balance number of training images per class • filter out certain categories and images • Train Multi-Label Deep Learning classifier • select state-of-the-art pre-trained CNN model • adapt the model to multi-label classification • fine-tune model on artwork images • optimize model hyperparameters for best performance • Apply trained model and extend metadata • run all the images through the trained classifier • record the prediction score as RDF triples • SPARQL on extended metadata to search the database (Maasai & Wimmics)
  • 117. Detecting “noise” By querying the extended metadata for the objects with low scores we can detect the “noise” in the represented subject annotation Image Metadata Score figure (saint Eloi de Noyon, évêque, en pied, bénédiction, vêtement liturgique, mitre, attribut, cheval, marteau, outil : ferronnerie) 000SC022652 C:/Joconde/joconde0355/m079806_bsa0030101_p.jpg cheval: 0.006 figures bibliques (Vierge à l'Enfant, à mi-corps, assis, Enfant Jésus : nu, livre);fond de paysage (colline, cours d'eau, barque, cavalier) 000PE027041 C:/Joconde/joconde0001/m503604_90ee1719_p.jpg cheval: 0.009 scène (satirique : Bismarck Otto von : Gargantua, repas, cheval, boisson : vin) 5002E006121 C:/Joconde/joconde0074/m500202_atpico-g70128_p.jpg cheval: 0.011
  • 118. Detecting “silence” By querying the extended metadata for the object with high scores and without object mentioned in annotation we can detect the “silence” in the annotation Image Metadata Score portrait 50350012455 C:Jocondejoconde0138m503501_d0012455-000_p.jpg cheval: 0.999 scène historique (guerre de siège : Lawfeld, Louis XV, Saxe maréchal de, bataille rangée) 000PE004371 C:Jocondejoconde0634m507704_79ee519_p.jpg cheval: 0.999 figure (sainte Jeanne d'Arc, jeune fille, équestre passant, armure, asque, épée) M0301000355 C:Jocondejoconde0617m030106_007305_p.jpg cheval: 0.997
  • 119. Ranking of search results Running the same query on the Extended Joconde database and sorting by score gives a better result putting the image in the second place Image Metadata Score représentation animalière (épagneul, debout) M0341003743 C:Jocondejoconde0534m034186_006932_p.jpg chien: 0.994 scène (chasse : lévrier, lièvre) M0810001165 C:Jocondejoconde0466m081003_028491_p.jpg chien: 0.993 représentation animalière (mise à mort, gros gibier : sanglier, chasse à courre, chien) 00000105149 C:Jocondejoconde0107m505206_oa817_p.jpg chien: 0.990
  • 120. Hypermedia MAS ▪ Bridging Web architecture and Multi-Agent Systems architecture ▪ Hypermedia Communities of People and Autonomous Agents ▪ Define an architectural style for Hypermedia MAS ▪ Define declarative languages and mechanisms for specifying, enacting, and regulating interactions among people and autonomous agents in Hypermedia MAS ▪ Develop an open-source software infrastructure for Hypermedia MAS that enables the deployment of hybrid communities on the Web ▪ Demonstrate the deployment of prototypical hybrid communities in two application areas: (i) Industry 4.0 and (ii) tackling online disinformation. http://hyperagents.gitlab.emse.fr/#
  • 121.
  • 125. 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)
  • 126.
  • 127. 275 Toward a Web of Things
  • 128. Make the Web AI-friendly content, links, metadata, etc. data, knowledge, etc. AI Web bots: chat bots, recommenders, facilitators, etc. configuration, parameters, embeddings, services, communication, etc.
  • 129. deduce data  model, schemas, ontologies, ... data data 15% progress
  • 130. learn data embeddings, parameters, configurations, … data data 30% progress
  • 131. sum intelligence  model, schemas, ontologies, ... embeddings, parameters, configurations, … data data 45% progress
  • 132. combine intelligence model, schemas, ontologies, ... embeddings, parameters, configurations, … data  60% progress
  • 133. remotely combine model, schemas, ontologies, … embeddings, parameters, configurations,…  Web 75% progress
  • 134. deeply combine data, knowledge, model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 90% progress
  • 135. combining AIs on the Web data, knowledge, model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 100% progress
  • 136. 285 one Web … a unique space in every meanings: data persons documents programs metadata
  • 137. Connected Animals, Animal-computer interaction (ACI) Herdsourcing: monitoring collective animal behavior
  • 139. IMAG_NE a Web linking all forms of Intelligence
  • 140. WIMMICS 1. user & interaction design 2. communities & social networks 3. linked data & semantic Web 4. reasoning & analyzing epistemic hybrid communities linked data usages and introspection contributions and traces
  • 141. www mmm world wide web massively multidisciplinary method
  • 142. WIMMICS Web-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    