KNOWLEDGE REPRESENTATION:             DESTRUCTURING                  THE      STRUCTURED vs NON-STRUCTURED                ...
AI and KM: once a Love Story            In the late 80s a love story between AI and KM    Their alliances: (rings) Knowled...
<<< Tim Berners Lee paper proposing the Web was rejected at the 1991 ACM Hypertext                                  Confer...
"We failed to make machines adapt to humans; we humans have learnt how to use windows                                     ...
1990: Idea that future Corporate Information Systems would be Knowledge Based Systems                     1991: MNEMOS EUR...
In 2012 we celebrate the 40th anniversary of PROLOG                                  (Where is the cake ?)                ...
Semantic Networks were used already in the 16th Century to represent complex information               Semantic Networks a...
Let people write "SVC on SVC" using a 4th ID field (NOT contexts, named graphs ...) (SVCI                                 ...
Ontologies are too simplistic at RDF level                        Ontologies are too complex at DL levelWhat was difficult...
Man-machine compromise schema  A good KR should be targeted at killing applications (App-Killer and not Killer App!)      ...
KR should be the backbone of "Semantic EXCEL" and "Semantic PowerPoint"Collective KM fails if it is not grounded in person...
INFERON: minimal and autonomous sentence                        Every information is INFERON                              ...
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Knowledge representation: structured or unstructured?

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A synthesis of 30 years of research and applications in knowledge representation. Proposal of natural language as a KR tool: "Litteratus Calculus"

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Knowledge representation: structured or unstructured?

  1. 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. 2. AI and KM: once a Love Story In the late 80s a love story between AI and KM Their alliances: (rings) Knowledge Representation and InferenceImportance of KRL languages, KADS modelling : Open Kads tool (1991) Early 90s: 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. 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 systems2012: 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. 4. "We failed to make machines adapt to humans; we humans have learnt how to use windows and menus" History In the early 80s, 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 COBOLEuropean 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 quil 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 » 80s: Expert Systems with KNOWLEDGE ENGINEERS 1988 -1992: METAPEDIA project in SPAIN: a fully object-oriented encyclopaedia
  5. 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. 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 available1993: 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 90s: sadness that AI languages disappear from education 2003: Semantic Networks is a too complex formalism for people; 99% reject it2003: 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. 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. 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. 9. Ontologies are too simplistic at RDF level Ontologies are too complex at DL levelWhat was difficult to solve in the 90s with powerful KR languages on limited problems cannot be solved in the 2010s with just Java and RDF at the Web scaleWhat 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. 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 paradigmDocument 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. 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. 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 inferonsA first version of a Litteratus Calculus tool is being implemented (since 2003 …) Current work: how to install INFERONS on the Internet ?

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