SlideShare une entreprise Scribd logo
1  sur  12
Télécharger pour lire hors ligne
KNOWLEDGE REPRESENTATION:

             DESTRUCTURING
                  THE
      STRUCTURED vs NON-STRUCTURED
                 DEBATE
                          Jean Rohmer
                          ESILV Paris
                    jean.rohmer@devinci.fr

              Presented at ECAI 2012 Montpellier
                   Workshop on AI and KM


     My personal background in CS, AI and KM
            Started Computer Science 45 years ago

                    Started AI 32 years ago

                   Started KM 24 years ago

              Management of Bull CEDIAG team

              IDELIANCE Semantic Tool (1993)

Many Military Intelligence Applications Data + Text + Semantics

              Blog: "PLEXUS LOGOS CALX"

             See also SLIDESHARE Jean Rohmer

                    Progress in KR is slow.

                 Mesopotamia 5500 years ago:




       Mesopotamia in the 21 th Century: still Stone Age:
AI and KM: once a Love Story
            In the late 80's a love story between AI and KM

    Their alliances: (rings) Knowledge Representation and Inference

Importance of KRL languages, KADS modelling : Open Kads tool (1991)




   Early 90's: economical crisis: the AI + KM couple almost starving

         AI and KM were young, promising, but still immature

        KM alone could earn some living in large corporations

                  The Web arrived and seduced KM

                           AI was left alone
<<< Tim Berners Lee paper proposing the Web was rejected at the 1991 ACM Hypertext
                                  Conference>>>

                            Hypertext was very close to KM


                                      Catastrophe

          2012: Large scientific Agencies manage all their projects with EXCEL

            2012: Many Engineering Schools have no real information systems

2012: ECAI program, proceedings are available just in PDF, without any tool for knowledge
                                     organization

     2012: they swapped my last name and first name in SOME ECAI registration files

                                  AI and KM are alone

                      AI lives with Automatic Learning Algorithms

                       KM flirts with wikis, blogs, social networks

                  The main tool for AI is SVM algorithm (sort of joke)

             The main tool for KM is EXCEL + POWERPOINT (not a joke)




                         There is no paper on KR at ECAI 2012

                         Denegation: "AI is hidden everywhere"

                              Laurence Danlos: (NL guru):
"We failed to make machines adapt to humans; we humans have learnt how to use windows
                                      and menus"


                                                      History
 In the early 80's, AI languages (LISP, PROLOG, KRL, Constraints later) were seen as the
       promise of a revolution in programming computers: declarative programming

   1982: Alain COLMERAUER declares that PROLOG is designed to replace COBOL

European Esprit programme: 1982: KIMS project "Knowledge and Information Management
                                      System"

  Earlier: Alan Turing tried to get funds from UK Gvt to build a sort of LISP MACHINE

 Earlier: Leibniz and Descartes proposed universal knowledge representation and reasoning
                                        languages.

                                PROJECT OF A COMPUTABLE UNIVERSAL LANGUAGE
                                      INCLUDING UNIVERSAL ONTOLOGIES
                                     WITH « COMBINATORIAL » MECANISMS

                                                       DESCARTES :
              « établir un ordre entre toutes les pensées, … de même qu'il y en a un établi entre les nombres »

  « cette langue aiderait au jugement , lui représentant si distinctement les choses qu’il lui serait presque impossible de se
                                                           tromper »

    « je tiens que cette langue est possible … mais n ’espérez jamais la voir en usage … sauf au Paradis Terrestre … »
                                                         LEIBNIZ :
                « quoique cette langue dépende de la vraie philosophie, elle ne dépend pas de sa perfection »

                         « à mesure que la science des hommes croîtra, cette langue croîtra aussi »

                                     « alors raisonner et calculer sera la même chose »




                      80's: Expert Systems with KNOWLEDGE ENGINEERS

    1988 -1992: METAPEDIA project in SPAIN: a fully object-oriented encyclopaedia
1990: Idea that future Corporate Information Systems would be Knowledge Based Systems

                     1991: MNEMOS EUREKA European project

                                  1991 (Bull Cediag):
 Corporate Intelligence = Corporate Memory + Corporate Decision + Corporate Visibility


                                     PROLOG
In 2012 we celebrate the 40th anniversary of PROLOG
                                  (Where is the cake ?)



                                    Personal History
        1984: “Alexander Method” (Foundation of Datalog / Deductive Databases)

             For me, illuminated by Prolog , “Everything was logic predicates”

                        1990: Expert Systems were very successful

        1990: Expert Systems demand much more intellectual energy than available

1993: Start developing IDELIANCE: a personal semantic networks manager for "everybody"

              fr.slideshare.net/Jean_Rohmer/ideliance-semantic-network-2000

                IDELIANCE: Personal Memory + “Intelligence Amplifier “

               Mid 90's: sadness that AI languages disappear from education

       2003: Semantic Networks is a too complex formalism for people; 99% reject it

2003: Idea of LITTERATUS CALCULUS: use plain natural language to represent knowledge

                               LITTERATUS CALCULUS:
  express anything with "inferons": minimal and autonomous sentences in natural language

                    2001 +: Strong critique of Semantic Web à la W3C

                             Structured vs Unstructured
                         Unstructured is in fact HYPER-structured

                           Structured is in fact HYPO-structured

                          Natural Language is HYPER-structured

 Natural language structures are so complex that we do not know how our brain master them

       So-called structured information (databases, RDF triples) are trivial structures
                                to match computer limitations

  All the problem of KR is that we are not able to write programs which understand natural
                                          language

           Semantic Networks is a good compromise between man and machine
Semantic Networks were used already in the 16th Century to represent complex information




               Semantic Networks are readable by humans if small enough
                      (Not billions of triples, leave it to NoSQL! )

                        Semantic Networks is a 2D representation

            2D representation avoids the usage of variables as in formal logic

              IDELIANCE Semantic Network editor: experience since 1993
                Used by many NON CS professionals in large corporation

            99% of people are reluctant to write themselves semantic networks

        Use semantic networks with a Subject Verb Complement (SVC) paradigm




    Let people use natural language to name S, V, C (never RDF, "Resources", URI ...)
Let people write "SVC on SVC" using a 4th ID field (NOT contexts, named graphs ...) (SVCI
                                        format):




                       Please users, not standardization committees


            Negative effects of the Web and Semantic Web on KR
                              Is Semantic Web a bad Joke ?

     SW 2001: "Machines understand and help Humans" (Scientific American Paper)

                     SW 2006: "A machine-to-machine Web of data"

                    SW 2011: Linked Data: "Humans help Machines"

                                      SW 2016: ????

  An endless loop / ping-pong of failures between manual and automatic, structured and
                                      unstructured

        Notion of URI is just a physical address scheme without any natural support

                    The Web reinforces the notion of -long- document

                                RDF has no "human face"

                RDF is at best low level engineering and exchange format

         Structured data publishing -dbpedia, Google- do not follow SW standards
Ontologies are too simplistic at RDF level

                        Ontologies are too complex at DL level

What was difficult to solve in the 90's with powerful KR languages on limited problems
      cannot be solved in the 2010's with just Java and RDF at the Web scale

What we have to do is to install a good KR on the Internet, rethinking all the KM issues

                   The best -only- KR available is natural language

                    Natural Language does not imply "Document"

                  Natural Language does not mean "non -structured"


                                  Représentation 1

                      A good KRL should be enjoyed by people

           People should write, query, compute themselves with their KRL

        Example of personal objective: take my reading notes directly in a KRL

                       Parabola of the ship inside the bottle:
                  Knowledge must be cut into articulated small parts

                         Example of personal objective:
 Summarize "Cours de Linguistique Générale" of Ferdinand de Saussure with my KRL

                  Tools are important! Never say "This is just a tool".

                            Intelligence is just a tool ... ????

                         Natural Language is just a tool ... ???

Many people say "Computer is just a tool" AND "Computers will change everything" …


                                        Theory

                             Theory of the two black holes
Man-machine compromise schema




  A good KR should be targeted at killing applications (App-Killer and not Killer App!)

                             Applications hide all knowledge:
          they presents users with a closed, limited, repressive view of the world

    Replace applications by the way people will interact and compute with knowledge

            A good KR should be targeted at killing the Document paradigm

Document paradigm is a concept imposed by the technology of "volumen' and "codex" more
                                 than 2000 years ago

             A good KR should aim at revolutionizing the Web (what else ?)


                                  Representation 2

                    People should enjoy using themselves directly KR

                  People should write KR instead of writing documents

         Computations on KR done directly by users should replace applications
                      exactly as EXCEL does with numeric data
KR should be the backbone of "Semantic EXCEL" and "Semantic PowerPoint"

Collective KM fails if it is not grounded in personal KM, through a personal, intensive effort
           to write, read, retrieve, combine, compute knowledge with a good KR

         We must invent new ways of browsing, editing, computing on knowledge.

                             Examples of new computations:
  "In between", "novelty detection", "how to", "what looks like" , "online graph mining"...


                          How to proceed towards a good KR ?


      Issue: what else do we have than KR progress to improve information systems ?

                   We must abandon the paradigm of PRO-GRAMMING

                 PRO-GRAMMING means “WRITTEN BY ADVANCE”

                         We most practice IM-PRO-GRAMMING

                        IM-PRO-GRAMMING means IM-PRO-VE

                       IM-PRO-GRAMMING means IM-PRO-VISE

                IM-PRO-GRAMMING needs the appropriate KR paradigm




                            LITTERATUS CALCULUS
           The only thing you put in a computer is sentences in natural language
INFERON: minimal and autonomous sentence

                        Every information is INFERON

                              There are no entities

                         Entities emerge from sentences

                  Instead of “Sentences are built from entities”

     Many tools to manage inferons: editing, browsing, query, inference, ...

                   My personal KB has today 70 000 inferons

A first version of a Litteratus Calculus tool is being implemented (since 2003 …)

           Current work: how to install INFERONS on the Internet ?

Contenu connexe

Similaire à Knowledge representation: structured or unstructured?

Meaning and the Semantic Web
Meaning and the Semantic WebMeaning and the Semantic Web
Meaning and the Semantic WebPhiloWeb
 
Semantic Web
Semantic WebSemantic Web
Semantic Weblogus2k
 
Human-Like Computing and Human-Computer Interaction
Human-Like Computing and Human-Computer InteractionHuman-Like Computing and Human-Computer Interaction
Human-Like Computing and Human-Computer InteractionAlan Dix
 
Lecture knowledge representationreasoning
Lecture knowledge representationreasoningLecture knowledge representationreasoning
Lecture knowledge representationreasoningIKS - Project
 
20110324 linked openeuropeanahumanities
20110324 linked openeuropeanahumanities20110324 linked openeuropeanahumanities
20110324 linked openeuropeanahumanitiesStefan Gradmann
 
Curs1-2_Introducere_istoric.ppt
Curs1-2_Introducere_istoric.pptCurs1-2_Introducere_istoric.ppt
Curs1-2_Introducere_istoric.pptVarjoghe
 
One Big Happy Family
One Big Happy FamilyOne Big Happy Family
One Big Happy FamilyDan Brickley
 
Phase III Presentation
Phase III PresentationPhase III Presentation
Phase III PresentationGrey Vaisius
 
Growing-up With AI
Growing-up With AIGrowing-up With AI
Growing-up With AISpotle.ai
 
Describing Everything - Open Web standards and classification
Describing Everything - Open Web standards and classificationDescribing Everything - Open Web standards and classification
Describing Everything - Open Web standards and classificationDan Brickley
 
HCI 3e - Ch 4: Paradigms
HCI 3e - Ch 4:  ParadigmsHCI 3e - Ch 4:  Paradigms
HCI 3e - Ch 4: ParadigmsAlan Dix
 
HCI - Chapter 4
HCI - Chapter 4HCI - Chapter 4
HCI - Chapter 4Alan Dix
 
What would you do with free pictures of everything on Earth?
What would you do with free pictures of everything on Earth?What would you do with free pictures of everything on Earth?
What would you do with free pictures of everything on Earth?Paul Houle
 
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...Maryam Farooq
 
Cyberspace før og nå (8390 KB)
Cyberspace før og nå (8390 KB)Cyberspace før og nå (8390 KB)
Cyberspace før og nå (8390 KB)Videoguy
 
Toward a Civilization of Collective Intelligence
Toward a Civilization of Collective IntelligenceToward a Civilization of Collective Intelligence
Toward a Civilization of Collective IntelligenceGabriela Agustini
 
Metaverse for Dataverse
Metaverse for DataverseMetaverse for Dataverse
Metaverse for Dataversevty
 
Knowledge = Information + Context
Knowledge = Information + ContextKnowledge = Information + Context
Knowledge = Information + ContextStefan Gradmann
 

Similaire à Knowledge representation: structured or unstructured? (20)

Meaning and the Semantic Web
Meaning and the Semantic WebMeaning and the Semantic Web
Meaning and the Semantic Web
 
Semantic Web
Semantic WebSemantic Web
Semantic Web
 
Human-Like Computing and Human-Computer Interaction
Human-Like Computing and Human-Computer InteractionHuman-Like Computing and Human-Computer Interaction
Human-Like Computing and Human-Computer Interaction
 
Lecture knowledge representationreasoning
Lecture knowledge representationreasoningLecture knowledge representationreasoning
Lecture knowledge representationreasoning
 
20110324 linked openeuropeanahumanities
20110324 linked openeuropeanahumanities20110324 linked openeuropeanahumanities
20110324 linked openeuropeanahumanities
 
IMD 203 - Ch01
IMD 203 - Ch01IMD 203 - Ch01
IMD 203 - Ch01
 
Irish Digital Libraries Summit
Irish Digital Libraries SummitIrish Digital Libraries Summit
Irish Digital Libraries Summit
 
Curs1-2_Introducere_istoric.ppt
Curs1-2_Introducere_istoric.pptCurs1-2_Introducere_istoric.ppt
Curs1-2_Introducere_istoric.ppt
 
One Big Happy Family
One Big Happy FamilyOne Big Happy Family
One Big Happy Family
 
Phase III Presentation
Phase III PresentationPhase III Presentation
Phase III Presentation
 
Growing-up With AI
Growing-up With AIGrowing-up With AI
Growing-up With AI
 
Describing Everything - Open Web standards and classification
Describing Everything - Open Web standards and classificationDescribing Everything - Open Web standards and classification
Describing Everything - Open Web standards and classification
 
HCI 3e - Ch 4: Paradigms
HCI 3e - Ch 4:  ParadigmsHCI 3e - Ch 4:  Paradigms
HCI 3e - Ch 4: Paradigms
 
HCI - Chapter 4
HCI - Chapter 4HCI - Chapter 4
HCI - Chapter 4
 
What would you do with free pictures of everything on Earth?
What would you do with free pictures of everything on Earth?What would you do with free pictures of everything on Earth?
What would you do with free pictures of everything on Earth?
 
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...
NYAI #27: Cognitive Architecture & Natural Language Processing w/ Dr. Catheri...
 
Cyberspace før og nå (8390 KB)
Cyberspace før og nå (8390 KB)Cyberspace før og nå (8390 KB)
Cyberspace før og nå (8390 KB)
 
Toward a Civilization of Collective Intelligence
Toward a Civilization of Collective IntelligenceToward a Civilization of Collective Intelligence
Toward a Civilization of Collective Intelligence
 
Metaverse for Dataverse
Metaverse for DataverseMetaverse for Dataverse
Metaverse for Dataverse
 
Knowledge = Information + Context
Knowledge = Information + ContextKnowledge = Information + Context
Knowledge = Information + Context
 

Plus de Jean Rohmer

Pub microsoft et health data hub
Pub microsoft et health data hubPub microsoft et health data hub
Pub microsoft et health data hubJean Rohmer
 
De la pub microsoft dans les articles sur le health data hub
De la pub microsoft dans les articles sur le health data hubDe la pub microsoft dans les articles sur le health data hub
De la pub microsoft dans les articles sur le health data hubJean Rohmer
 
Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...
Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...
Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...Jean Rohmer
 
Compter les triangles en PROLOG
Compter les triangles en PROLOGCompter les triangles en PROLOG
Compter les triangles en PROLOGJean Rohmer
 
Ideliance mental reseau semantique
Ideliance mental reseau semantiqueIdeliance mental reseau semantique
Ideliance mental reseau semantiqueJean Rohmer
 
Arithmetics with symbolic Artificial Intelligence and Prolog: it's a child's...
Arithmetics with symbolic Artificial Intelligence  and Prolog: it's a child's...Arithmetics with symbolic Artificial Intelligence  and Prolog: it's a child's...
Arithmetics with symbolic Artificial Intelligence and Prolog: it's a child's...Jean Rohmer
 
Semantic architectures for Artificial Intelligence
Semantic architectures for Artificial IntelligenceSemantic architectures for Artificial Intelligence
Semantic architectures for Artificial IntelligenceJean Rohmer
 
L'informatique n'est pas l'amie des données
L'informatique n'est pas l'amie des donnéesL'informatique n'est pas l'amie des données
L'informatique n'est pas l'amie des donnéesJean Rohmer
 
Construire un moteur d'inférence
Construire un moteur d'inférenceConstruire un moteur d'inférence
Construire un moteur d'inférenceJean Rohmer
 
Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!
Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!
Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!Jean Rohmer
 
Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...
Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...
Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...Jean Rohmer
 
Artificial Intelligence Past Present and Future
Artificial Intelligence Past Present and FutureArtificial Intelligence Past Present and Future
Artificial Intelligence Past Present and FutureJean Rohmer
 
Semantic networks, business rules, inference engines, and complex event proc...
Semantic networks, business rules, inference engines,  and complex event proc...Semantic networks, business rules, inference engines,  and complex event proc...
Semantic networks, business rules, inference engines, and complex event proc...Jean Rohmer
 
De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique
De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique
De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique Jean Rohmer
 
Ideliance semantic network 2000
Ideliance semantic network 2000Ideliance semantic network 2000
Ideliance semantic network 2000Jean Rohmer
 
Standards considered harrmful
Standards considered harrmfulStandards considered harrmful
Standards considered harrmfulJean Rohmer
 
Litteratus calculus fundamentals
Litteratus calculus fundamentalsLitteratus calculus fundamentals
Litteratus calculus fundamentalsJean Rohmer
 
Internet in 2020 rohmer open world forum 2011
Internet in 2020 rohmer open world forum 2011Internet in 2020 rohmer open world forum 2011
Internet in 2020 rohmer open world forum 2011Jean Rohmer
 

Plus de Jean Rohmer (18)

Pub microsoft et health data hub
Pub microsoft et health data hubPub microsoft et health data hub
Pub microsoft et health data hub
 
De la pub microsoft dans les articles sur le health data hub
De la pub microsoft dans les articles sur le health data hubDe la pub microsoft dans les articles sur le health data hub
De la pub microsoft dans les articles sur le health data hub
 
Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...
Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...
Les 40 ans de l'Institut Fredrik Bull avec liens video: 40 ans d'informatique...
 
Compter les triangles en PROLOG
Compter les triangles en PROLOGCompter les triangles en PROLOG
Compter les triangles en PROLOG
 
Ideliance mental reseau semantique
Ideliance mental reseau semantiqueIdeliance mental reseau semantique
Ideliance mental reseau semantique
 
Arithmetics with symbolic Artificial Intelligence and Prolog: it's a child's...
Arithmetics with symbolic Artificial Intelligence  and Prolog: it's a child's...Arithmetics with symbolic Artificial Intelligence  and Prolog: it's a child's...
Arithmetics with symbolic Artificial Intelligence and Prolog: it's a child's...
 
Semantic architectures for Artificial Intelligence
Semantic architectures for Artificial IntelligenceSemantic architectures for Artificial Intelligence
Semantic architectures for Artificial Intelligence
 
L'informatique n'est pas l'amie des données
L'informatique n'est pas l'amie des donnéesL'informatique n'est pas l'amie des données
L'informatique n'est pas l'amie des données
 
Construire un moteur d'inférence
Construire un moteur d'inférenceConstruire un moteur d'inférence
Construire un moteur d'inférence
 
Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!
Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!
Expériences de gestion des connaissances avec IDELIANCE: supprimons le document!
 
Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...
Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...
Intelligence Artificielle: résolution de problèmes en Prolog ou Prolog pour l...
 
Artificial Intelligence Past Present and Future
Artificial Intelligence Past Present and FutureArtificial Intelligence Past Present and Future
Artificial Intelligence Past Present and Future
 
Semantic networks, business rules, inference engines, and complex event proc...
Semantic networks, business rules, inference engines,  and complex event proc...Semantic networks, business rules, inference engines,  and complex event proc...
Semantic networks, business rules, inference engines, and complex event proc...
 
De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique
De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique
De l'IA au Calcul Littéraire: Pourquoi j'ai zappé le Web Sémantique
 
Ideliance semantic network 2000
Ideliance semantic network 2000Ideliance semantic network 2000
Ideliance semantic network 2000
 
Standards considered harrmful
Standards considered harrmfulStandards considered harrmful
Standards considered harrmful
 
Litteratus calculus fundamentals
Litteratus calculus fundamentalsLitteratus calculus fundamentals
Litteratus calculus fundamentals
 
Internet in 2020 rohmer open world forum 2011
Internet in 2020 rohmer open world forum 2011Internet in 2020 rohmer open world forum 2011
Internet in 2020 rohmer open world forum 2011
 

Dernier

KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostMatt Ray
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDELiveplex
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Brian Pichman
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsSafe Software
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1DianaGray10
 
VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXTarek Kalaji
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureEric D. Schabell
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintMahmoud Rabie
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.YounusS2
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7DianaGray10
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfDianaGray10
 

Dernier (20)

KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
 
20150722 - AGV
20150722 - AGV20150722 - AGV
20150722 - AGV
 
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDEADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
ADOPTING WEB 3 FOR YOUR BUSINESS: A STEP-BY-STEP GUIDE
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
 
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsIgniting Next Level Productivity with AI-Infused Data Integration Workflows
Igniting Next Level Productivity with AI-Infused Data Integration Workflows
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity Webinar
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1
 
VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBX
 
OpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability AdventureOpenShift Commons Paris - Choose Your Own Observability Adventure
OpenShift Commons Paris - Choose Your Own Observability Adventure
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership Blueprint
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdfUiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
UiPath Solutions Management Preview - Northern CA Chapter - March 22.pdf
 
201610817 - edge part1
201610817 - edge part1201610817 - edge part1
201610817 - edge part1
 

Knowledge representation: structured or unstructured?

  • 1. KNOWLEDGE REPRESENTATION: DESTRUCTURING THE STRUCTURED vs NON-STRUCTURED DEBATE Jean Rohmer ESILV Paris jean.rohmer@devinci.fr Presented at ECAI 2012 Montpellier Workshop on AI and KM My personal background in CS, AI and KM Started Computer Science 45 years ago Started AI 32 years ago Started KM 24 years ago Management of Bull CEDIAG team IDELIANCE Semantic Tool (1993) Many Military Intelligence Applications Data + Text + Semantics Blog: "PLEXUS LOGOS CALX" See also SLIDESHARE Jean Rohmer Progress in KR is slow. Mesopotamia 5500 years ago: Mesopotamia in the 21 th Century: still Stone Age:
  • 2. AI and KM: once a Love Story In the late 80's a love story between AI and KM Their alliances: (rings) Knowledge Representation and Inference Importance of KRL languages, KADS modelling : Open Kads tool (1991) Early 90's: economical crisis: the AI + KM couple almost starving AI and KM were young, promising, but still immature KM alone could earn some living in large corporations The Web arrived and seduced KM AI was left alone
  • 3. <<< Tim Berners Lee paper proposing the Web was rejected at the 1991 ACM Hypertext Conference>>> Hypertext was very close to KM Catastrophe 2012: Large scientific Agencies manage all their projects with EXCEL 2012: Many Engineering Schools have no real information systems 2012: ECAI program, proceedings are available just in PDF, without any tool for knowledge organization 2012: they swapped my last name and first name in SOME ECAI registration files AI and KM are alone AI lives with Automatic Learning Algorithms KM flirts with wikis, blogs, social networks The main tool for AI is SVM algorithm (sort of joke) The main tool for KM is EXCEL + POWERPOINT (not a joke) There is no paper on KR at ECAI 2012 Denegation: "AI is hidden everywhere" Laurence Danlos: (NL guru):
  • 4. "We failed to make machines adapt to humans; we humans have learnt how to use windows and menus" History In the early 80's, AI languages (LISP, PROLOG, KRL, Constraints later) were seen as the promise of a revolution in programming computers: declarative programming 1982: Alain COLMERAUER declares that PROLOG is designed to replace COBOL European Esprit programme: 1982: KIMS project "Knowledge and Information Management System" Earlier: Alan Turing tried to get funds from UK Gvt to build a sort of LISP MACHINE Earlier: Leibniz and Descartes proposed universal knowledge representation and reasoning languages. PROJECT OF A COMPUTABLE UNIVERSAL LANGUAGE INCLUDING UNIVERSAL ONTOLOGIES WITH « COMBINATORIAL » MECANISMS DESCARTES : « établir un ordre entre toutes les pensées, … de même qu'il y en a un établi entre les nombres » « cette langue aiderait au jugement , lui représentant si distinctement les choses qu’il lui serait presque impossible de se tromper » « je tiens que cette langue est possible … mais n ’espérez jamais la voir en usage … sauf au Paradis Terrestre … » LEIBNIZ : « quoique cette langue dépende de la vraie philosophie, elle ne dépend pas de sa perfection » « à mesure que la science des hommes croîtra, cette langue croîtra aussi » « alors raisonner et calculer sera la même chose » 80's: Expert Systems with KNOWLEDGE ENGINEERS 1988 -1992: METAPEDIA project in SPAIN: a fully object-oriented encyclopaedia
  • 5. 1990: Idea that future Corporate Information Systems would be Knowledge Based Systems 1991: MNEMOS EUREKA European project 1991 (Bull Cediag): Corporate Intelligence = Corporate Memory + Corporate Decision + Corporate Visibility PROLOG
  • 6. In 2012 we celebrate the 40th anniversary of PROLOG (Where is the cake ?) Personal History 1984: “Alexander Method” (Foundation of Datalog / Deductive Databases) For me, illuminated by Prolog , “Everything was logic predicates” 1990: Expert Systems were very successful 1990: Expert Systems demand much more intellectual energy than available 1993: Start developing IDELIANCE: a personal semantic networks manager for "everybody" fr.slideshare.net/Jean_Rohmer/ideliance-semantic-network-2000 IDELIANCE: Personal Memory + “Intelligence Amplifier “ Mid 90's: sadness that AI languages disappear from education 2003: Semantic Networks is a too complex formalism for people; 99% reject it 2003: Idea of LITTERATUS CALCULUS: use plain natural language to represent knowledge LITTERATUS CALCULUS: express anything with "inferons": minimal and autonomous sentences in natural language 2001 +: Strong critique of Semantic Web à la W3C Structured vs Unstructured Unstructured is in fact HYPER-structured Structured is in fact HYPO-structured Natural Language is HYPER-structured Natural language structures are so complex that we do not know how our brain master them So-called structured information (databases, RDF triples) are trivial structures to match computer limitations All the problem of KR is that we are not able to write programs which understand natural language Semantic Networks is a good compromise between man and machine
  • 7. Semantic Networks were used already in the 16th Century to represent complex information Semantic Networks are readable by humans if small enough (Not billions of triples, leave it to NoSQL! ) Semantic Networks is a 2D representation 2D representation avoids the usage of variables as in formal logic IDELIANCE Semantic Network editor: experience since 1993 Used by many NON CS professionals in large corporation 99% of people are reluctant to write themselves semantic networks Use semantic networks with a Subject Verb Complement (SVC) paradigm Let people use natural language to name S, V, C (never RDF, "Resources", URI ...)
  • 8. Let people write "SVC on SVC" using a 4th ID field (NOT contexts, named graphs ...) (SVCI format): Please users, not standardization committees Negative effects of the Web and Semantic Web on KR Is Semantic Web a bad Joke ? SW 2001: "Machines understand and help Humans" (Scientific American Paper) SW 2006: "A machine-to-machine Web of data" SW 2011: Linked Data: "Humans help Machines" SW 2016: ???? An endless loop / ping-pong of failures between manual and automatic, structured and unstructured Notion of URI is just a physical address scheme without any natural support The Web reinforces the notion of -long- document RDF has no "human face" RDF is at best low level engineering and exchange format Structured data publishing -dbpedia, Google- do not follow SW standards
  • 9. Ontologies are too simplistic at RDF level Ontologies are too complex at DL level What was difficult to solve in the 90's with powerful KR languages on limited problems cannot be solved in the 2010's with just Java and RDF at the Web scale What we have to do is to install a good KR on the Internet, rethinking all the KM issues The best -only- KR available is natural language Natural Language does not imply "Document" Natural Language does not mean "non -structured" Représentation 1 A good KRL should be enjoyed by people People should write, query, compute themselves with their KRL Example of personal objective: take my reading notes directly in a KRL Parabola of the ship inside the bottle: Knowledge must be cut into articulated small parts Example of personal objective: Summarize "Cours de Linguistique Générale" of Ferdinand de Saussure with my KRL Tools are important! Never say "This is just a tool". Intelligence is just a tool ... ???? Natural Language is just a tool ... ??? Many people say "Computer is just a tool" AND "Computers will change everything" … Theory Theory of the two black holes
  • 10. Man-machine compromise schema A good KR should be targeted at killing applications (App-Killer and not Killer App!) Applications hide all knowledge: they presents users with a closed, limited, repressive view of the world Replace applications by the way people will interact and compute with knowledge A good KR should be targeted at killing the Document paradigm Document paradigm is a concept imposed by the technology of "volumen' and "codex" more than 2000 years ago A good KR should aim at revolutionizing the Web (what else ?) Representation 2 People should enjoy using themselves directly KR People should write KR instead of writing documents Computations on KR done directly by users should replace applications exactly as EXCEL does with numeric data
  • 11. KR should be the backbone of "Semantic EXCEL" and "Semantic PowerPoint" Collective KM fails if it is not grounded in personal KM, through a personal, intensive effort to write, read, retrieve, combine, compute knowledge with a good KR We must invent new ways of browsing, editing, computing on knowledge. Examples of new computations: "In between", "novelty detection", "how to", "what looks like" , "online graph mining"... How to proceed towards a good KR ? Issue: what else do we have than KR progress to improve information systems ? We must abandon the paradigm of PRO-GRAMMING PRO-GRAMMING means “WRITTEN BY ADVANCE” We most practice IM-PRO-GRAMMING IM-PRO-GRAMMING means IM-PRO-VE IM-PRO-GRAMMING means IM-PRO-VISE IM-PRO-GRAMMING needs the appropriate KR paradigm LITTERATUS CALCULUS The only thing you put in a computer is sentences in natural language
  • 12. INFERON: minimal and autonomous sentence Every information is INFERON There are no entities Entities emerge from sentences Instead of “Sentences are built from entities” Many tools to manage inferons: editing, browsing, query, inference, ... My personal KB has today 70 000 inferons A first version of a Litteratus Calculus tool is being implemented (since 2003 …) Current work: how to install INFERONS on the Internet ?