Fabien GANDON — Inria, Université Côte d'Azur — A Web Linking all Kinds of Intelligence

A WEB LINKING ALL KINDS OF INTELLIGENCE Fabien GANDON @fabien_gandon http://fabien.info

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Web as the focal point of two fields born in the 50s

AI & IA AI for Artificial Intelligence (McCarthy et al., 1955)

Web as the focal point of two fields born in the 50s

AI & IA AI for Artificial Intelligence (McCarthy et al., 1955) IA for Intelligence Amplification (Ashby, 1956) and Intelligence Augmentation (Engelbart, 1962)

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

1400

number of linked open datasets on the Web

1200 1000 800 600 400 200 0 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

http://dbpedia.org/resource/Sophia_Antipolis

the Web as a universal space to link‌ data, schemata

all birds can fly all penguins are birds so ...

automated deduction

PIPE : 0.9143

automated classification

OWL in one… algebraic properties

 !

disjoint properties 1..1

! qualified cardinality individual prop. neg 



chained prop.

union disjunction intersection complement restriction

1..1

cardinality  equivalence enumeration [>18] value restriction disjoint union  keys

…

schemata on the Web

 G1



& H1

[Corby, Faron-Zucker et al.]

G2

<

& H2

Gn

Hn

abstract graph machine STTL CORESE

QUERY & INFER  graph rules and queries  deontic reasoning  induction

 G1



& H1

[Corby, Faron-Zucker et al.]

G2

<

& H2

Gn

Hn

abstract graph machine STTL CORESE

QUERY & INFER  graph rules and queries  deontic reasoning  induction

RATIO4TA predict & explain

[Hasan et al.]

 G1



& H1

[Corby, Faron-Zucker et al.]

G2

<

& H2

Gn

Hn

abstract graph machine STTL CORESE

RATIO4TA

QUERY & INFER

predict & explain

 graph rules and queries  deontic reasoning  induction

[Hasan et al.]

[Tettamanzi et al.]

INDUCTION

find missing knowledge

 G1



& H1

[Corby, Faron-Zucker et al.]

G2

<

& H2

Gn

Hn

abstract graph machine STTL CORESE

RATIO4TA

QUERY & INFER

predict & explain

 graph rules and queries  deontic reasoning  induction

[Hasan et al.]

LICENTIA deontic reasoning, license compatibility and composition [Villata et al.]

[Tettamanzi et al.]

INDUCTION

find missing knowledge

OWL

JSON LD JSON

LDP

N-Quad

TriG

N-Triple

Turtle/N3

RDFS

RDF XML CSV-LD

R2RML GRDDL

SPARQL

Linked Data RDF

SHACL

XML

RDFa

HTML

HTTP

URI, IRI, URL, HTTP URI

DATA AND SCHEMATA ON THE WEB: A GROWING STACK

the Web as a universal space to linkâ&#x20AC;Ś data, schemata, programs

15% progress

deduce data model, schemas, ontologies, ...

ď&#x192;&#x17E; data

data

30% progress

learn data

data

data

embeddings, parameters, configurations, â&#x20AC;Ś

45% progress

sum intelligence model, schemas, ontologies, ...

ď&#x192;&#x17E; data

data

embeddings, parameters, configurations, â&#x20AC;Ś

60% progress

combine intelligence model, schemas, ontologies, ...

ď&#x192;&#x17E; data

embeddings, parameters, configurations, â&#x20AC;Ś

75% progress

remotely combine model, schemas, ontologies, â&#x20AC;Ś

ď&#x192;&#x17E; Web

embeddings, parameters, configurations,â&#x20AC;Ś

90% progress

deeply combine

 data, knowledge, model, schemas, ontologies, …

Web data, knowledge, embeddings, parameters, configurations,…

100% progress

combining AIs on the Web

 data, knowledge, model, schemas, ontologies, …

Web data, knowledge, embeddings, parameters, configurations,…

the Web as a global blackboard for artificial intelligence

Smarter Cities – IBM Dublin [Lécué, 2015]

Smarter Cities – IBM Dublin [Lécué, 2015]

 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

ALOOF: robots learning by reading on the Web Annie cuts the bread in the kitchen with her knife

[Cabrio, Basile et al. 2017]

dbp:Knife aloof:Location dbp:Kitchen

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-

PREDICT HOSPITALIZATION [Gazzotti, Faron et al. 2017]

 Physician’s records classification in order to predict hospitalization

Element Patients Consultations Past medical history Biometric data Semiotics Diagnosis Row of prescribed drugs Symptoms Health care procedures Additional examination Paramedical prescription Observations/notes

Number 55 823 364 684 187 290 293 908 250 669 117 442 847 422 23 488 11 850 871 590 17 222 56 143

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-

PREDICT HOSPITALIZATION

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)

[Gazzotti, Faron et al. 2017]

 Physician’s records classification in order to predict hospitalization  Augment data with structured knowledge and study impact on different prediction methods (2)

MonaLIA [Bobasheva et al. 2017]

ď&#x201A;§ reason & query on RDF metadata to build balanced, unambiguous, labelled training sets.

Joconde database from French museums 350 000 images of artworks

RDF metadata based on external thesauri

Joconde database from French museums

MonaLIA [Bobasheva et al. 2017]

 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 animal  bird ? painting 

RDF metadata based on external thesauri

350 000 images of artworks

(1)

(2)

WEB EDGE AI [WebML @ W3C]

 Edge AI directly in the browser  Web APIs, models, protocols,…

the Web as a universal space to linkâ&#x20AC;Ś data, schemata, programs, intelligence

3500000

3000000

2500000

2000000

1500000

1000000

500000

0 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

3500000

3000000

2500000

2000000

1500000

1000000

500000

0 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

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

the Web as a global blackboard for artificial intelligence all kinds of

he who controls metadata, controls the web and through the world-wide web many things in our world.

WIMMICS

Fabien Gandon - @fabien_gandon - http://fabien.info Web-instrumented man-machine interactions, communities and semantics

Technical details: http://bit.ly/wimmics-papers

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