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The Web We Mix - benevolent AIs for a resilient web

The document outlines the work of a multidisciplinary team focused on integrating benevolent AI into the web to enhance human-machine interactions and address challenges related to social and formal semantics. It discusses various projects and research areas, including exploratory search, data crawling, e-learning, healthcare predictions, and the use of RDF metadata for building training sets. Additionally, the document touches on the importance of uncertainty in semantic web applications and the interplay between AI and web technologies.

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The Web We Mix benevolent AIs for a resilient web Fabien Gandon, http://fabien.info I'm sorry, Dave. I'm afraid I can't do that.
WIMMICS TEAM  Inria  CNRS  University Côte D’Azur (UCA) I3S Web-Instrumented Man-Machine Interactions, Communities and Semantics
CHALLENGE to bridge social semantics and formal semantics on the Web
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
AI for classical Web tasks
Autonomy AgentWare Web Researcher, 1996 Dogs’ UI metaphor to Machine Learning Sent to Web kennels to work offline
SEARCHING  exploratory search  question-answering DISCOVERYHUB.CO semantic spreading activation new evaluation protocol [Marie, Giboin, Palagi et al.]
SEARCHING  exploratory search  question-answering 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.] QAKiS.ORG
BROWSING e.g. SMILK plugin [Lopez, Cabrio, et al.]
BROWSING e.g. SMILK plugin [Nooralahzadeh, Cabrio, et al.]
linked open data(sets) cloud on the Web 0 200 400 600 800 1000 1200 1400 01/05/2007 08/10/2007 07/11/2007 10/11/2007 28/02/2008 31/03/2008 18/09/2008 05/03/2009 27/03/2009 14/07/2009 22/09/2010 19/09/2011 30/08/2014 26/01/2017 number of linked open datasets on the Web
CRAWLING  Predict data availability  Select features of URIs [Huang, Gandon 2019]
CRAWLING  Predict data availability  Select features of URIs  Learn crawling selection  Online learning w. crawling [Huang, Gandon 2019]
EDTECH & WEB for 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
but…
over adaptation & filter bubble(c.f. Eli Pariser)
AITOBURSTBUBBLES
bias……
UNCERTAINTY Can uncertainty be formalized on top of the standards of the Semantic Web, to be published on the Web ? [Djebri et al 2019]
UNCERTAINTY Can uncertainty be formalized on top of the standards of the Semantic Web, to be published on the Web ? [Djebri et al 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 LINKED CALCULI [Djebri et al 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 Can uncertainty be translated and negotiated? [Djebri et al 2019]
UNCERTAINTY Can uncertainty be translated and negotiated? [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 Accept: text/turtle Prefer: profile="prob:Probability" (RFC 7240)
AI for classical Web tasks Web for classical AI tasks
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.]
the Web: this place where invisible brontobytes graze furiously 1027 bytes
Smarter Cities – IBM Dublin [Lécué, 2015]
Smarter Cities – IBM Dublin [Lécué, 2015]
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 [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)
PREDICT HOSPITALIZATION  Physician’s records classification in order to predict hospitalization  Augment data with structured knowledge and study impact on different prediction methods  Study enrichment and features impact and combinations on different ML 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)
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.) 350 000 images of artworks RDF metadata based on external thesauri Joconde database from French museums (1) [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 
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
WEB EDGE AI  Edge AI directly in the browser  Web APIs, models, protocols,… [WebML @ W3C]
Web ways applied to AI e.g. copy-paste based reuse and contribution to create Web AIs
but…
automated deduction all birds can fly all penguins are birds so ...
PIPE : 0.9143 automated classification
PROBABILITY Assertions (evidence) Axiom (hypothesis) how to evaluate in an open-world? ?
PROBABILITY POSSIBILITY  Possibilistic Axiom Scoring  Possibility and Necessity Measures [Tettamanzi et al.] Assertions (evidence) Axiom (hypothesis) how to evaluate in an open-world? –1 +1 REJECT ACCEPT ?
correlation
correlation vs. causation http://tylervigen.com/spurious-correlations
AI for classical Web tasks Web for classical AI tasks AI and Web augmentation
“ « 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: 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: 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.]
but…
From Eliza to Lenny, talking to machines
complex web applications barely visible evolution of the place of humans in = user = processor = data
You will read this first Then you will read this… Then this… and only now are you reading this top left text… predictable means manipulable
AI for Artificial Intelligence (McCarthy et al., 1955)
AI for Artificial Intelligence (McCarthy et al., 1955) IA for Intelligence Amplification (Ashby, 1956) and Intelligence Augmentation (Engelbart, 1962)
AI for Artificial Intelligence (McCarthy et al., 1955) IA for Intelligence Amplification (Ashby, 1956) and Intelligence Augmentation (Engelbart, 1962) Web as the rendez-vous point for two fields born in the 50s… AI & IA
AI for classical Web tasks Web for classical AI tasks AI and Web augmentation AI and Web societies
MODELING USERS  individual context  social structures PRISSMA prissma:Context 0 48.86034 -2.337599 200 geo:lat geo:lon prissma:radius 1 :museumGeo prissma:Environment 2 { 3, 1, 2, { pr i ssma: poi } } { 4, 0, 3, { pr i ssma: envi r onment } } :atTheMuseum error tolerant graph edit distance context ontology [Costabello et al.]
MODELING USERS  individual context  social structures PRISSMA prissma:Context 0 48.86034 -2.337599 200 geo:lat geo:lon prissma:radius 1 :museumGeo prissma:Environment 2 { 3, 1, 2, { pr i ssma: poi } } { 4, 0, 3, { pr i ssma: envi r onment } } :atTheMuseum error tolerant graph edit distance context ontology OCKTOPUS tag, topic, user distribution tag and folksonomy restructuring with prefix trees [Costabello et al.] [Meng et al.]
MODELING USERS  individual context  social structures PRISSMA prissma:Context 0 48.86034 -2.337599 200 geo:lat geo:lon prissma:radius 1 :museumGeo prissma:Environment 2 { 3, 1, 2, { pr i ssma: poi } } { 4, 0, 3, { pr i ssma: envi r onment } } :atTheMuseum error tolerant graph edit distance context ontology OCKTOPUS tag, topic, user distribution tag and folksonomy restructuring with prefix trees EMOCA&SEEMPAD emotion detection & annotation [Villata, Cabrio et al.] [Costabello et al.] [Meng et al.]
DEBATES & EMOTIONS #IRC [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  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]
but…
China: 1 400 millions India: 1 300 millions acebook: 2 400 millions
inverse view of the world China: 1 400 millions India: 1 300 millions
Web Robots
e.g. Wikipedia bots 2 187 bot approved for use on the English Wikipedia to help maintain 45 223 137 pages
web… page social apps data … a.i.
web agents =+
EDITS 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Wikipedia editors, 2012 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Humans
massive interaction design ergonomics in hybrid web communities
tardigrades Web AIs • 1 ° K / −458 °F / −272 °C  • 420 °K / 300 °F / 150 °C  • Vacuum / High pressure  • Ionizing radiation  • Himalayas / Deep sea  Extremophile
perspectives
Web 1.0, …
Web 1.0, 2.0…
price convert? person other sellers? Web 1.0, 2.0, 3.0 …
[TimBL, 94]
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)
Toward a Web of Things
the Web as a global blackboard for artificial intelligence
Connected Animals, Animal-computer interaction (ACI) Herdsourcing: monitoring collective animal behavior
Consider a Web of intelligence linking  Web of Animals: connected animals to predict earthquakes  Web of Things, Web of Sensors  Web of AIs: reasoning systems, learning systems, etc.  Web of People: experts from all over the world (i.e. some expert always awake // crowdsourcing & Q&A expert routing) … to form a collective intelligent decision system.
the Web as a global blackboard for artificial intelligence all kinds of
IMAG_NE
IMAG_NE a Web linking all forms of Intelligence
a Web Science research agenda must account for the fact that the long term potential of the Web is to augment and link everything. Peter Steiner, The New Yorker, 1993 Kaamran Hafeez, The New Yorker, 2015 I pass CAPTCHAs, Nobody knows I’m a bot 2019
but…
training common NLP models nearly five times the lifetime emissions of the average American car including the manufacturing of the car itself
Business Insider, US National Archives, George Grantham Bain Collection 1942 ELECTRIC CAR PARIS 70km/h, 100km autonomy 1943 SWITCHBOARD AND LOST JOBS we need historians, sociologists,…
www mmm world wide web massively multidisciplinary method
- Look, we should stay away for a while; you and I are so over- informed that we come out of every conversation exhausted. Sempé
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.

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The Web We Mix - benevolent AIs for a resilient web

  • 1.
    The Web WeMix benevolent AIs for a resilient web Fabien Gandon, http://fabien.info I'm sorry, Dave. I'm afraid I can't do that.
  • 2.
    WIMMICS TEAM  Inria CNRS  University Côte D’Azur (UCA) I3S Web-Instrumented Man-Machine Interactions, Communities and Semantics
  • 3.
    CHALLENGE to bridge socialsemantics and formal semantics on the Web
  • 4.
    MULTI-DISCIPLINARY TEAM  40~50members  ~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
  • 5.
  • 6.
    Autonomy AgentWare WebResearcher, 1996 Dogs’ UI metaphor to Machine Learning Sent to Web kennels to work offline
  • 7.
    SEARCHING  exploratory search question-answering DISCOVERYHUB.CO semantic spreading activation new evaluation protocol [Marie, Giboin, Palagi et al.]
  • 8.
    SEARCHING  exploratory search question-answering 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.] QAKiS.ORG
  • 9.
  • 10.
  • 11.
    linked open data(sets)cloud on the Web 0 200 400 600 800 1000 1200 1400 01/05/2007 08/10/2007 07/11/2007 10/11/2007 28/02/2008 31/03/2008 18/09/2008 05/03/2009 27/03/2009 14/07/2009 22/09/2010 19/09/2011 30/08/2014 26/01/2017 number of linked open datasets on the Web
  • 12.
    CRAWLING  Predict dataavailability  Select features of URIs [Huang, Gandon 2019]
  • 13.
    CRAWLING  Predict dataavailability  Select features of URIs  Learn crawling selection  Online learning w. crawling [Huang, Gandon 2019]
  • 14.
    EDTECH & WEB fore-learning & serious games [Rodriguez-Rocha, Faron-Zucker et al.]
  • 15.
    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
  • 16.
  • 17.
    over adaptation &filter bubble(c.f. Eli Pariser)
  • 18.
  • 19.
  • 20.
    UNCERTAINTY Can uncertainty beformalized on top of the standards of the Semantic Web, to be published on the Web ? [Djebri et al 2019]
  • 21.
    UNCERTAINTY Can uncertainty beformalized on top of the standards of the Semantic Web, to be published on the Web ? [Djebri et al 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
  • 22.
    UNCERTAINTY LINKED CALCULI [Djebri etal 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
  • 23.
    UNCERTAINTY Can uncertainty betranslated and negotiated? [Djebri et al 2019]
  • 24.
    UNCERTAINTY Can uncertainty betranslated and negotiated? [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 Accept: text/turtle Prefer: profile="prob:Probability" (RFC 7240)
  • 25.
    AI for classicalWeb tasks Web for classical AI tasks
  • 26.
    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.]
  • 27.
    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.]
  • 28.
    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.]
  • 29.
    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.]
  • 30.
    the Web: thisplace where invisible brontobytes graze furiously 1027 bytes
  • 31.
    Smarter Cities –IBM Dublin [Lécué, 2015]
  • 32.
    Smarter Cities –IBM Dublin [Lécué, 2015]
  • 33.
    PREDICT HOSPITALIZATION  Physician’srecords 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
  • 34.
    PREDICT HOSPITALIZATION  Physician’srecords classification in order to predict hospitalization  Augment data with structured knowledge [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)
  • 35.
    PREDICT HOSPITALIZATION  Physician’srecords classification in order to predict hospitalization  Augment data with structured knowledge and study impact on different prediction methods  Study enrichment and features impact and combinations on different ML 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)
  • 36.
    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]
  • 37.
    MonaLIA  reason &query on RDF metadata to build balanced, unambiguous, labelled training sets.  transfer learning & CNN classifiers on targeted categories (topics, techniques, etc.) 350 000 images of artworks RDF metadata based on external thesauri Joconde database from French museums (1) [Bobasheva et al. 2017]
  • 38.
    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 
  • 39.
    deduce data  model, schemas,ontologies, ... data data 15% progress
  • 40.
    learn data embeddings, parameters,configurations, … data data 30% progress
  • 41.
    sum intelligence  model, schemas,ontologies, ... embeddings, parameters, configurations, … data data 45% progress
  • 42.
    combine intelligence model, schemas,ontologies, ... embeddings, parameters, configurations, … data  60% progress
  • 43.
    remotely combine model, schemas,ontologies, … embeddings, parameters, configurations,…  Web 75% progress
  • 44.
    deeply combine data, knowledge,model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 90% progress
  • 45.
    combining AIs onthe Web data, knowledge, model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 100% progress
  • 46.
    WEB EDGE AI Edge AI directly in the browser  Web APIs, models, protocols,… [WebML @ W3C]
  • 47.
    Web ways appliedto AI e.g. copy-paste based reuse and contribution to create Web AIs
  • 48.
  • 49.
    automated deduction all birdscan fly all penguins are birds so ...
  • 50.
  • 51.
  • 52.
    PROBABILITY POSSIBILITY  Possibilistic AxiomScoring  Possibility and Necessity Measures [Tettamanzi et al.] Assertions (evidence) Axiom (hypothesis) how to evaluate in an open-world? –1 +1 REJECT ACCEPT ?
  • 53.
  • 54.
  • 55.
    AI for classicalWeb tasks Web for classical AI tasks AI and Web augmentation
  • 56.
    “ « a Web-AugmentedInteraction (WAI) is a user’s interaction with a system that is improved by allowing the system to access Web resources » [Gandon, Giboin, WebSci17]
  • 57.
    AZKAR remotely visit andinteract with a museum through a robot and via the Web [Buffa et al.]
  • 58.
    ALOOF: Web andPerception [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)
  • 59.
    ALOOF: robots learningby 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.]
  • 60.
    ALOOF: RDF datasetabout 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, ...
  • 61.
  • 62.
  • 63.
  • 64.
    From Eliza toLenny, talking to machines
  • 65.
    complex web applications barelyvisible evolution of the place of humans in = user = processor = data
  • 66.
    You will readthis first Then you will read this… Then this… and only now are you reading this top left text… predictable means manipulable
  • 67.
    AI for ArtificialIntelligence (McCarthy et al., 1955)
  • 68.
    AI for ArtificialIntelligence (McCarthy et al., 1955) IA for Intelligence Amplification (Ashby, 1956) and Intelligence Augmentation (Engelbart, 1962)
  • 69.
    AI for ArtificialIntelligence (McCarthy et al., 1955) IA for Intelligence Amplification (Ashby, 1956) and Intelligence Augmentation (Engelbart, 1962) Web as the rendez-vous point for two fields born in the 50s… AI & IA
  • 70.
    AI for classicalWeb tasks Web for classical AI tasks AI and Web augmentation AI and Web societies
  • 71.
    MODELING USERS  individualcontext  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.]
  • 72.
    MODELING USERS  individualcontext  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.]
  • 73.
    MODELING USERS  individualcontext  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.]
  • 74.
  • 75.
    DEBATES & EMOTIONS #IRCargument rejection attacks-disgust[Villata, Cabrio et al.]
  • 76.
    OPINIONS NLP, ML andarguments to monitor online image [Villata, Cabrio, et al.]
  • 77.
    ARGUMENT MINING ON CLINICALTRIALS  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]
  • 78.
    ARGUMENT MINING ON POLITICALSPEECHES  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]
  • 79.
    CYBERBULLYING CREEP EU project:detect and prevent [Corazza, Arslan, Cabrio, Villata]
  • 80.
    CYBERBULLYING CREEP EU project:detect and prevent [Corazza, Arslan, Cabrio, Villata]
  • 81.
  • 82.
    China: 1 400millions India: 1 300 millions acebook: 2 400 millions
  • 83.
    inverse view ofthe world China: 1 400 millions India: 1 300 millions
  • 84.
  • 85.
    e.g. Wikipedia bots 2187 bot approved for use on the English Wikipedia to help maintain 45 223 137 pages
  • 86.
    web… page socialapps data … a.i.
  • 87.
  • 88.
    EDITS 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 1 2 34 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Wikipedia editors, 2012 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Humans
  • 89.
    massive interaction design ergonomicsin hybrid web communities
  • 90.
    tardigrades Web AIs •1 ° K / −458 °F / −272 °C  • 420 °K / 300 °F / 150 °C  • Vacuum / High pressure  • Ionizing radiation  • Himalayas / Deep sea  Extremophile
  • 91.
  • 92.
  • 93.
  • 94.
  • 95.
  • 96.
    Toward a Webof 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)
  • 98.
    Toward a Webof Things
  • 99.
    the Web asa global blackboard for artificial intelligence
  • 100.
    Connected Animals, Animal-computerinteraction (ACI) Herdsourcing: monitoring collective animal behavior
  • 101.
    Consider a Webof intelligence linking  Web of Animals: connected animals to predict earthquakes  Web of Things, Web of Sensors  Web of AIs: reasoning systems, learning systems, etc.  Web of People: experts from all over the world (i.e. some expert always awake // crowdsourcing & Q&A expert routing) … to form a collective intelligent decision system.
  • 102.
    the Web asa global blackboard for artificial intelligence all kinds of
  • 103.
  • 104.
    IMAG_NE a Web linkingall forms of Intelligence
  • 105.
    a Web Scienceresearch agenda must account for the fact that the long term potential of the Web is to augment and link everything. Peter Steiner, The New Yorker, 1993 Kaamran Hafeez, The New Yorker, 2015 I pass CAPTCHAs, Nobody knows I’m a bot 2019
  • 106.
  • 107.
    training common NLPmodels nearly five times the lifetime emissions of the average American car including the manufacturing of the car itself
  • 109.
    Business Insider, USNational Archives, George Grantham Bain Collection 1942 ELECTRIC CAR PARIS 70km/h, 100km autonomy 1943 SWITCHBOARD AND LOST JOBS we need historians, sociologists,…
  • 110.
    www mmm world wide web massivelymultidisciplinary method
  • 111.
    - Look, weshould stay away for a while; you and I are so over- informed that we come out of every conversation exhausted. Sempé
  • 112.
    Make the WebAI-friendly content, links, metadata, etc. data, knowledge, etc. AI Web bots: chat bots, recommenders, facilitators, etc. configuration, parameters, embeddings, services, communication, etc.