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Corporate Semantic Web

Corporate Semantic Web. Acacia http://www.inria.fr/acacia INRIA Sophia Antipolis. Corporate Semantic Web ?. Use Semantic Web approach for Corporate Memory and Corporate Knowledge Management. Objectives.

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Corporate Semantic Web

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  1. Corporate Semantic Web Acacia http://www.inria.fr/acacia INRIA Sophia Antipolis

  2. Corporate Semantic Web ? Use Semantic Web approach for Corporate Memory and Corporate Knowledge Management

  3. Objectives implement and trial a corporate memory management framework based on agents and ontologies : CoMMA : Corporate Memory Management with Agents 2 relevant scenarios have been chosen to highlight the problem of information retrieval in the company: • Enhancement of New Employee Insertion in the company, • Performing process that detect, identify and interpret technology movements for matching technology evolutions with market opportunities to disseminate among employees innovative ideas related to Technology Monitoring activities Objectives

  4. Objectives Corporate knowledge management aims at facilitating creation, dissemination, transmission and reuse of knowledge in an organisation • propose an innovative solution based on integration of technologies: • ontologies or knowledge models • multi-agent architecture of several co-operating agents • meta-information (resource annotation) expressed in RDF format • Machine Learning Techniques for user adaptability Objectives

  5. CoMMA Objectives Objectives

  6. CoMMA Consortium European IST project : 2000-2001 3 industrial partners: Atos Origin (F) CSTB (Centre Scientifique et Technique du Batiment) (F) T-Systems Nova (G) 3 academic partners: INRIA (F) LIRMM/CNRS (F) University of Parma (I) CoMMA Consortium

  7. Corporate Memory: An explicit, disembodied and persistent representation of knowledge and information in an organization, in order to facilitate their access and reuse by members of the organization, for their tasks. CoMMA : What is it ?

  8. How ? • Corporate memories are heterogeneousand distributed information landscapes • Stakeholders are an heterogeneous and distributed population • Exploitation of CM involves heterogeneousand distributed tasks Materialization CM Exploitation CM Corporate Memory Management through Agents Multi-Agent System: Modularity, Distributed, Collaboration Machine Learning : Adaptation, Emergence XML: Standard, Structure, Extensible, Validate, Transform RDF: Annotation, Schemas How ?

  9. Corporate Memory Annotation Annotation Annotation Annotation Document Document Document Document Author and/or annotator of documents Multi-Agents System Learning Learning Learning User Agent User Agent Ontology and Models Agent Knowledge Engineer End User Interconnection Agent Interest Group Agent Query Ontology Models - Enterprise Model - User's Profiles (2) (1) (3) Overall Schema & Ontology

  10. The balance of our pharmaceutical project. • Two concepts & one term : ambiguity • Ontology : object capturing relevant aspects of the meaning of concepts used in our application scenarios (example) Example of problem: ambiguity

  11. Conceptual Vocabulary Concepts & links - definitions ex: document report Relations - constraints ex: person (author)  document Terms & natural language definitions ex: 'bike', 'cycle', bicycle' - (bicycle) (2) From semi-informal to semi-formal (3) RDF(S) Internal Observations & Documents Interviews Scenarios External Reuse (Meta-) Dictionaries External Expertise (1) Scenarios and Data collection (4) Navigation and Use Use & Users MIME Building the ontology

  12. Corporate Memory Annotation Annotation Annotation Annotation (2) Document Document Document Document Author and/or annotator of documents Multi-Agents System Learning Learning Learning User Agent User Agent Ontology and Models Agent Knowledge Engineer (1) (3) End User Interconnection Agent Interest Group Agent Query Ontology Models - Enterprise Model - User's Profiles Memory Structure

  13. Organizational Entity (X) : The entity X is or is a sub-part of an organization. Person (X): The entity X is living being pertaining to the human race. Include (Organizational Entity: X, Organizational Entity / Person Y) : the organizational entity X includes Y as one of its members. Manage (Person: X, Organizational Entity: Y) : The person X watches and directs the organizational entity Y Person(Rose) Person(Fabien) Person(Olivier) Person(Alain) Organizational Entity(INRIA) Organizational Entity(Acacia) Include(INRIA, Acacia) Manage(Rose, Acacia) Include(Acacia, Rose) Include(Acacia, Fabien) Include(Acacia, Olivier) Include(Acacia, Alain) c - Situation & Annotations b - Ontology a - Reality Illustration of the cycle

  14. O S O S O S + Model Memory + + Annotated Archives D A D A D A + • Corporate Semantic Web • RDF & RDFS : XML framework for Web resources descriptions  Use it for Intranets • Ontology in RDFS • Description of the Situation in RDF: • User Profiles • Organization model • Annotations in RDF describing Documents Model-based Annotated Memory

  15. Corporate Memory Annotation Annotation Annotation Annotation (2) Document Document Document Document Author and/or annotator of documents Multi-Agents System Learning Learning Learning User Agent User Agent Ontology and Models Agent Knowledge Engineer (1) (3) End User Interconnection Agent Interest Group Agent Query Ontology Models - Enterprise Model - User's Profiles End-Users

  16. User Interfaces • Annotating documents • Querying the memory • Hide complexity (ontology, agents,...) • Present the results • Push technology • Improve information flowing • Proactive diffusion of annotations • Communities of interest Interfacing Users

  17. Organizational model • Users' Profiles: • Administrative Information (link to Org. model) • Explicit preferences • Favorite queries / annotations • Characteristics derived from past use • Learning techniques:Represent, learn and compare current use profiles to improve future use. • Learning during a login session • Ranking results Profiles & Learning

  18. Corporate Memory Annotation Annotation Annotation Annotation (2) Document Document Document Document Author and/or annotator of documents Multi-Agents System Learning Learning Learning User Agent User Agent Ontology and Models Agent Knowledge Engineer (1) (3) End User Interconnection Agent Interest Group Agent Query Ontology Models - Enterprise Model - User's Profiles Multi-agent Architecture

  19. One functional architecture leading to several possible configurations in order to adapt to the broad range of environments that can be found in a company • Architecture: Agent kinds and their relationship Fixed at design time • Configuration: Exact topography of a given MAS Fixed at deployment time • Flexible distribution : • Locally adapt to resources and users • Global capitalization through cooperation • Integration of different technologies Principal interest of MAS in CoMMA

  20. Ontology and Model Society Annotations Society Archivists Ontologist Agents Mediators Interconnection Society Federated Matchmakers Users' society Profiles Archivists Interface Controllers Profile Managers Societies, Roles and Interactions

  21. Corporate Memory Annotation Annotation Annotation Annotation (2) Document Document Document Document Author and/or annotator of documents Multi-Agents System Learning Learning Learning User Agent User Agent Ontology and Models Agent Knowledge Engineer (1) (3) End User Interconnection Agent Interest Group Agent Query Ontology Models - Enterprise Model - User's Profiles Done Conclusion

  22. Authors Engineer Archivist (internal / informal sources) (external sources) Docs+Annotations Area referent Coordination Strategic orientation ANNOTATION Index card ,Synthesis, PUSH Query RETRIEVAL User TECHNOLOGY MONITORING The diffusion of innovative ideas among employees The Technology Monitoring scenario

  23. The actors of the Technology Monitoring scenario : • Archivist in charge of feeding the system -> Author • Engineer and Researcher • watching his expertise Area -> User • feeding the system with new information -> Author • in charge of identifyingcorrespondents and coordinating thematic groups-> Area referent The actors

  24. For the Authors: • Indexing information by annotating companies, people, documents... • For the Area referents: • Identifying resources, skills about given business domains • For the Users: • Being automatically informed about relevant information according to their profile (push mode) • Querying the system (pull mode) Examples of Supported tasks

  25. Corporate Memory Help Human resource Be Presentation Evaluated Updating FAQ profile Newcomer Question Search Relation tutor NEI Scenario: the “insertion of new employees” in the company concerns the new employees who need to handle a lot of new information about their enterprise in a very short time, to be rapidly efficient

  26. The NE who just arrived in his new company • not familiar with the environment • needing answers to many standard questions • The tutor • person responsible to support NEs during the first weeks • with CoMMA responsible to fill the annotation base The actors

  27. 5 major components: • An ontology (O’CoMMA) • A multi-agent system, • A Semantic search engine (CORESE), • A machine learning algorithm • A GUI  The CoMMA technical solution for the implementation of a Corporate memory. The CoMMA Solution CoMMA solution

  28. splitting resources / system: Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request CoMMA solution

  29. splitting resources / system: • the document resources Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request CoMMA solution

  30. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request • splitting resources / system: • the document resources • the configuration resources CoMMA solution

  31. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request • splitting resources / system: • the document resources • the configuration resources • Ontology CoMMA solution

  32. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request • Ontology O’CoMMA • Dedicated to corporate memory, • Represented in RDFS, CoMMA solution

  33. rdfs:Class for concepts of the ontology, • Possibility to use class inheritance Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Ontology Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request CoMMA solution

  34. rdf:Property for relations of the ontology, • specialization of properties : director subPropertyOf manager director  manager Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Ontology Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request CoMMA solution

  35. rdfs:label for synonyms and multi- language of the ontology, • Use of stylesheet to filter terminology and multi-language. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Ontology Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request CoMMA solution

  36. rdfs:comment for natural language definition • the link between definition and concept is kept  ontology “trackability” Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Ontology Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request CoMMA solution

  37. <rdfs:Class rdf:ID="Document"> <rdfs:subClassOf rdf:resource="#Entity"/> <rdfs:subClassOf rdf:resource="#EntityConcerningATopic"/> <rdfs:subClassOf rdf:resource="#NumberableEntity"/> <rdfs:comment xml:lang="en">Entity including elements serving as a representation of thinking. </rdfs:comment> <rdfs:comment xml:lang="fr">Entite comprenant des elements de representation de la pensee. </rdfs:comment> <rdfs:label xml:lang="en">document</rdfs:label> <rdfs:label xml:lang="fr">document</rdfs:label> </rdfs:Class> RDFS Example : Class

  38. <rdf:Property rdf:ID="Title"> <rdfs:subPropertyOf rdf:resource="#Designation"/> <rdfs:range rdf:resource="&rdfs;Literal"/> <rdfs:domain rdf:resource="#Document"/> <rdfs:comment xml:lang="en">Designation of a document. </rdfs:comment> <rdfs:comment xml:lang="fr">Designation du document. </rdfs:comment> <rdfs:label xml:lang="en">title</rdfs:label> <rdfs:label xml:lang="fr">titre</rdfs:label> </rdf:Property> RDFS Example : Property

  39. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request • splitting resources / system: • the document resources • the configuration resources • Ontology, Enterprise model CoMMA solution

  40. <c:LegalCorporation rdf:about="http://www.inria.fr/"/> <c:NationalOrganizationGroup rdf:about="http://www.inria.fr/"> <c:Designation>Institut National de Recherche en Informatique et Automatique</c:Designation> <c:HasForActivity><c:Research/></c:HasForActivity> <c:IsInterestedBy><c:ComputerScienceTopic/></c:IsInterestedBy> <c:IsInterestedBy><c:MathematicsTopic/></c:IsInterestedBy> … Enterprise Model

  41. <c:LocalOrganizationGroup rdf:about="http://www-sop.inria.fr/"> <c:Designation>UR Sophia Antipolis de l'INRIA: Institut National de Recherche en Informatique et Automatique</c:Designation> <c:HasForActivity><c:Research/></c:HasForActivity> <c:IsInterestedBy><c:ComputerScienceTopic/></c:IsInterestedBy> <c:Include><c:ProjectGroup rdf:about="http://www.inria.fr/recherche/equipes/acacia.en.html"/></c:Include> <c:Include><c:ProjectGroup rdf:about="http://www-sop.inria.fr/tropics/"/></c:Include> <c:Include><c:ProjectGroup rdf:about="http://www-sop.inria.fr/cafe/"/></c:Include>

  42. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request • splitting resources / system: • the document resources • the configuration resources • Ontology, Enterprise model, User profiles CoMMA solution

  43. <c:IndividualProfile rdf:about="#"> <c:CreationDate>an 2000</c:CreationDate> <c:Title>Employee profile of Olivier Corby</c:Title> </c:IndividualProfile> <c:Employee rdf:ID = "http://www-sop.inria.fr/acacia/personnel/corby/"> <c:FamilyName>Corby</c:FamilyName> <c:FirstName>Olivier</c:FirstName> <c:HasForOntologicalEntrancePoint><c:KnowledgeModelingTopic/></c:HasForOntologicalEntrancePoint><c:HasForOntologicalEntrancePoint><c:ObjectProgrammingTopic/></c:HasForOntologicalEntrancePoint> User Profile Example

  44. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request • splitting resources / system: • the document resources • the configuration resources • the multi agent system framework CoMMA solution

  45. Gui: building an annotation. Learning User Agent CoMMA solution

  46. Learning User Agent • Machine Learning technique: • use feedbacks to learn document relevancy • feedback from one user can be generalized to users having the same fields of interest, • is designed for both pull mode and push mode CoMMA solution

  47. Annotation Annotation Annotation Annotation Corporate Memory Management through Agents Document Document Document Document Corporate Memory Architecture (2) Document authors and annotators. Multi Agent system Learning Learning Learning User Agent Document Agent(s) Document Agent(s) User Agent User Agent Ontology and Model Agent Ontology Connecting Agent Connecting Agent (1) Knowledge manager (3) Enterprise model Model User profile User profile Final user Request • Multi-agent system: • document sub society CoMMA solution

  48. CORESE a semantic search engine • relies on RDF(S) and conceptual graph theory, • use of the inheritance graph of RDFS (specialization and generalization), • Inference mechanisms • manage the annotation distribution • Java API wrapped into an agent Document Agent(s) • Multi-agent system: • document sub society CoMMA solution

  49. <c:ResearchReport rdf:about='http://www.inria.fr/rapports/sophia/RR-3819.html'> <c:CreatedBy> <c:Person rdf:about='http://www.inria.fr/nada.matta'> <c:FamilyName>Matta</c:FamilyName> <c:FirstName>Nada</c:FirstName> </c:Person> </c:CreatedBy> <c:CreatedBy> <c:Person rdf:about='http://www.inria.fr/olivier.corby'> <c:FamilyName>Corby</c:FamilyName> <c:FirstName>Olivier</c:FirstName> </c:Person> </c:CreatedBy> RDF Annotation

  50. <c:CreatedBy> <c:ProjectGroup rdf:about= 'http://www.inria.fr/recherche/equipes/acacia.en.html'> <c:Designation>Acacia</c:Designation> <c:hasCreated rdf:resource='http://www.inria.fr/rapports/sophia/RR-3819.html'/> </c:ProjectGroup> </c:CreatedBy> <c:CreationDate>11-1999</c:CreationDate> <c:Title> Méthodes de capitalisation de mémoire de projet </c:Title> RDF Annotation

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