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A WEB LINKING
ALL KINDS OF INTELLIGENCE
Fabien GANDON
@fabien_gandon
http://fabien.info
  
Vice Head of Science of Inr...
the Web: this place where invisible brontobytes graze furiously
AI & IA
Web as the focal point of two fields born in the 50s
AI for Artificial Intelligence (McCarthy et al., 1955)
AI & IA
Web as the focal point of two fields born in the 50s
AI for Artificial Intelligence (McCarthy et al., 1955)
IA for...
the Web as a universal space to link…
data
[TimBL, 94]
a Web approach to data publication
???...« http://fr.dbpedia.org/resource/Paris »
a Web approach to data publication
HTTP URI
GET
a Web approach to data publication
HTTP URI
GET
HTML, …
a Web approach to data publication
HTTP URI
GET
HTML,RDF, XML,…
The MUC18 protein at UniProt
http://www.uniprot.org/uniprot/P43121
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...
http://dbpedia.org/resource/Sophia_Antipolis
the Web as a universal space to link…
data, schemata
automated deduction
all birds can fly
all penguins are birds
so ...
PIPE : 0.9143
automated classification
OWL in one…
algebraic properties
disjoint properties
qualified cardinality
1..1
!
individual prop. neg
chained prop.


e...
schemata on the Web
QUERY & INFER
 graph rules and queries
 deontic reasoning
 induction
CORESE
 &
G2 H2
 &
G1 H1
<
Gn Hn
abstract graph ...
QUERY & INFER
 graph rules and queries
 deontic reasoning
 induction
CORESE
 &
G2 H2
 &
G1 H1
<
Gn Hn
RATIO4TA
predic...
QUERY & INFER
 graph rules and queries
 deontic reasoning
 induction
CORESE
INDUCTION
 &
G2 H2
 &
G1 H1
<
Gn Hn
RATIO...
QUERY & INFER
 graph rules and queries
 deontic reasoning
 induction
CORESE
LICENTIA
INDUCTION
 &
G2 H2
 &
G1 H1
<
Gn...
URI, IRI, URL, HTTP URI
DATA AND SCHEMATA ON THE WEB: A GROWING STACK
JSON
RDF
JSON LD
N-Triple
N-Quad
Turtle/N3
TriG
RDFS...
the Web as a universal space to link…
data, schemata, programs
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,…

...
combining AIs on the Web
data, knowledge, model, schemas, ontologies, …
data, knowledge, embeddings, parameters, configura...
the Web as a global blackboard
for artificial intelligence
Smarter Cities – IBM Dublin
[Lécué, 2015]
Smarter Cities – IBM Dublin
[Lécué, 2015]
ALOOF: robots learning by reading on the Web
 First Object Relation Knowledge Base: 46212 co-mentions, 49 tools, 14 rooms...
PREDICT HOSPITALIZATION
 Physician’s records classification
in order to predict hospitalization
[Gazzotti, Faron et al. 2...
PREDICT HOSPITALIZATION
 Physician’s records classification
in order to predict hospitalization
 Augment data with struc...
MonaLIA
 reason & query on RDF metadata to build
balanced, unambiguous, labelled training sets.
350 000 images
of artwork...
MonaLIA
 reason & query on RDF metadata to build
balanced, unambiguous, labelled training sets.
 transfer learning & CNN...
WEB EDGE AI
 Edge AI directly in the browser
 Web APIs, models, protocols,…
[WebML @ W3C]
the Web as a universal space to link…
data, schemata, programs, intelligence
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...
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...
emotional (artificial) intelligence
•emotion felt
•emotion expressed
•opinion
•strong language
•etc.
Toward a Web of Things
Connected Animals, Animal-computer interaction (ACI)
Herdsourcing: monitoring collective animal behavior
IMAG_NE
the Web as a global blackboard
for artificial intelligence
all kinds of
WIMMICSVice Head of Science of Inria Sophia Antipolis - Méditerranée
Head of the Wimmics Lab. (Inria, UCA, CNRS, I3S)
W3C ...
A Web Linking all Kinds of Intelligence - SophI.A Summit
A Web Linking all Kinds of Intelligence - SophI.A Summit
A Web Linking all Kinds of Intelligence - SophI.A Summit
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A Web Linking all Kinds of Intelligence - SophI.A Summit

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Initially, the Web was essentially perceived as a huge distributed library of linked pages, a worldwide documentary space for humans. In the mid-90s, with wikis and forums, the Web was re-opened in read-write mode and this paved the way to numerous new social media applications. The Web is now a space where three billions of users interact with billions of pages and numerous software. In parallel, extensions of the Web were developed and deployed to make it more and more machine friendly supporting the publication and consumption by software agents of worldwide linked data published on a semantic Web. As a result, the Web became a collaborative space for natural and artificial intelligence raising the problem of supporting these worldwide interactions. In particular, these hybrid communities require reconciling the formal semantics of computer science (e.g. logics, ontologies, typing systems, etc.) on which the Web architecture is built, with the soft semantics of people (e.g. posts, tags, status, etc.) on which the Web content is built. This talk will present some of the challenges and progresses in building this evolution of a Web toward a universal space to link many different kinds of intelligence.

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A Web Linking all Kinds of Intelligence - SophI.A Summit

  1. 1. A WEB LINKING ALL KINDS OF INTELLIGENCE Fabien GANDON @fabien_gandon http://fabien.info    Vice Head of Science of Inria Sophia Antipolis Head of the Wimmics Lab. (Inria, UCA, CNRS, I3S) W3C Advisory Committee representative for Inria Director of QWANT-Inria Joint Laboratory Leader of the Convention Ministry of Culture - Inria
  2. 2. the Web: this place where invisible brontobytes graze furiously
  3. 3. AI & IA Web as the focal point of two fields born in the 50s AI for Artificial Intelligence (McCarthy et al., 1955)
  4. 4. AI & IA Web as the focal point of two fields born in the 50s AI for Artificial Intelligence (McCarthy et al., 1955) IA for Intelligence Amplification (Ashby, 1956) and Intelligence Augmentation (Engelbart, 1962)
  5. 5. the Web as a universal space to link… data
  6. 6. [TimBL, 94]
  7. 7. a Web approach to data publication ???...« http://fr.dbpedia.org/resource/Paris »
  8. 8. a Web approach to data publication HTTP URI GET
  9. 9. a Web approach to data publication HTTP URI GET HTML, …
  10. 10. a Web approach to data publication HTTP URI GET HTML,RDF, XML,…
  11. 11. The MUC18 protein at UniProt http://www.uniprot.org/uniprot/P43121
  12. 12. 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
  13. 13. http://dbpedia.org/resource/Sophia_Antipolis
  14. 14. the Web as a universal space to link… data, schemata
  15. 15. automated deduction all birds can fly all penguins are birds so ...
  16. 16. PIPE : 0.9143 automated classification
  17. 17. OWL in one… algebraic properties disjoint properties qualified cardinality 1..1 ! individual prop. neg chained prop.   enumeration intersection union complement  disjunction restriction! cardinality 1..1 equivalence [>18] disjoint union value restriction keys …
  18. 18. schemata on the Web
  19. 19. 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.]
  20. 20. 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.]
  21. 21. 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.]
  22. 22. 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.]
  23. 23. URI, IRI, URL, HTTP URI DATA AND SCHEMATA ON THE WEB: A GROWING STACK 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
  24. 24. the Web as a universal space to link… data, schemata, programs
  25. 25. deduce data  model, schemas, ontologies, ... data data 15% progress
  26. 26. learn data embeddings, parameters, configurations, … data data 30% progress
  27. 27. sum intelligence  model, schemas, ontologies, ... embeddings, parameters, configurations, … data data 45% progress
  28. 28. combine intelligence model, schemas, ontologies, ... embeddings, parameters, configurations, … data  60% progress
  29. 29. remotely combine model, schemas, ontologies, … embeddings, parameters, configurations,…  Web 75% progress
  30. 30. deeply combine data, knowledge, model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 90% progress
  31. 31. combining AIs on the Web data, knowledge, model, schemas, ontologies, … data, knowledge, embeddings, parameters, configurations,…  Web 100% progress
  32. 32. the Web as a global blackboard for artificial intelligence
  33. 33. Smarter Cities – IBM Dublin [Lécué, 2015]
  34. 34. Smarter Cities – IBM Dublin [Lécué, 2015]
  35. 35. 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]
  36. 36. 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
  37. 37. PREDICT HOSPITALIZATION  Physician’s records classification in order to predict hospitalization  Augment data with structured knowledge and study impact on different prediction methods [Gazzotti, Faron et al. 2017] Sexe Date Cause CISP2 ... History Observations H 25/04/2012 vaccin-antitétanique A44 ... Appendicite EN CP - Bon état général - auscult pulm libre; bdc rég sans souffle - tympans ok- Element Number Patients Consultations Past medical history Biometric data Semiotics Diagnosis Row of prescribed drugs Symptoms Health care procedures Additional examination Paramedical prescription Observations/notes 55 823 364 684 187 290 293 908 250 669 117 442 847 422 23 488 11 850 871 590 17 222 56 143 (1) (2)
  38. 38. 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]
  39. 39. 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 
  40. 40. WEB EDGE AI  Edge AI directly in the browser  Web APIs, models, protocols,… [WebML @ W3C]
  41. 41. the Web as a universal space to link… data, schemata, programs, intelligence
  42. 42. 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 / # acts of edition, 2012
  43. 43. 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 Human Wikipedia editors / # acts of edition, 2012
  44. 44. emotional (artificial) intelligence •emotion felt •emotion expressed •opinion •strong language •etc.
  45. 45. Toward a Web of Things
  46. 46. Connected Animals, Animal-computer interaction (ACI) Herdsourcing: monitoring collective animal behavior
  47. 47. IMAG_NE
  48. 48. the Web as a global blackboard for artificial intelligence all kinds of
  49. 49. WIMMICSVice Head of Science of Inria Sophia Antipolis - Méditerranée Head of the Wimmics Lab. (Inria, UCA, CNRS, I3S) W3C Advisory Committee representative for Inria Director of QWANT-Inria Joint Laboratory Leader of the Convention Ministry of Culture – Inria 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. http://bit.ly/wimmics-papers   

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