1 Corporate Semantic Web Acacia INRIA Sophia Antipolis

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

3Objectives 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

4Objectives 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

5Objectives CoMMA Objectives

6 CoMMA Consortium European IST project : 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)

7 CoMMA : What is it ? 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.

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

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

10 Example of problem: ambiguity  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)

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

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

13 Illustration of the cycle 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 b - Ontology 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 a - Reality

14 Model-based Annotated Memory  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 OS AD OS AD Annotated Archives Model OS AD Memory

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

16 Interfacing Users  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

17 Profiles & Learning  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

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

19 Principal interest of MAS in CoMMA  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

20 Societies, Roles and Interactions Ontology and Model Society Ontologist Agents Annotations Society Mediators Archivists Users' society Profile Managers Profiles Archivists InterfaceControllers Interconnection Society FederatedMatchmakers

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

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

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 identifying correspondents and coordinating thematic groups -> Area referent The actors

24 Examples of Supported tasks  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)

25 Search Presentation Question FAQ Help Relation Be Evaluated tutor Corporate Memory Human resource Updating profile Newcomer 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 actors  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

27 CoMMA solution  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

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

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

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

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

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

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

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

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

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

37 RDFS Example : Class Entity including elements serving as a representation of thinking. Entity including elements serving as a representation of thinking. Entite comprenant des elements de representation de la pensee. Entite comprenant des elements de representation de la pensee. document document </rdfs:Class>

38 RDFS Example : Property Designation of a document. Designation of a document. Designation du document. Designation du document. title title titre titre </rdf:Property>

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

40 Enterprise Model Institut National de Recherche en Informatique et Automatique Institut National de Recherche en Informatique et Automatique …

41 UR Sophia Antipolis de l'INRIA: Institut National de Recherche en Informatique et Automatique UR Sophia Antipolis de l'INRIA: Institut National de Recherche en Informatique et Automatique

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

43 User Profile Example an 2000 an 2000 Employee profile of Olivier Corby Employee profile of Olivier Corby Corby Corby Olivier Olivier

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

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

46 Learning User Agent CoMMA solution  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

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

48 Document Agent(s) CoMMA solution  Multi-agent system:  document sub society  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

49 RDF Annotation Matta Matta Nada Nada Corby Corby Olivier Olivier

50 RDF Annotation Acacia Acacia Méthodes de capitalisation de mémoire de projet Méthodes de capitalisation de mémoire de projet</c:Title>

51 CoMMA solution Final user Architecture Multi Agent system Document authors and annotators. Knowledge manager Corporate Memory User Agent Learning Annotation Document Ontology Model Enterprise model Annotation Document Annotation Document Annotation Document Request Ontology and Model Agent Learning User Agent Connecting Agent User profile User profile Connecting Agent Document Agent(s) Learning User Agent Connecting Agent  Multi-agent system:  Interconnecting sub society

52 CoMMA solution Connecting Agent  Multi-agent system:  Interconnecting sub society A Distributed annotations management algorithm Relies on: metrics that evaluate the semantic similiarity of annotations complex protocols between « connecting agents » and « document agents » to rebuild the splitted annotation.

53 CoMMA solution The CoMMA Methodology

54 Other project results O ’CoMMA ontology Extension of RDF(S) language for representing knowledge CORESE + new inference mechanisms Techniques of categorization of RDF-annotated documents Multi-agent architecture for IR RDF-based JADE ontology & content language Management of distribution of annotations and of queries. Machine learning techniques

55 Specific Layer High layer Middle Layer Ontology O ’CoMMA AspectsEnterpriseAspectsDocumentAspectsUserAspectsDomain  Method: Data collection, Terminological Phase, Structuration, Validation, Formalization in RDFS  Result: 420 concepts, 50 relations, 630 terms, 12 levels of depth

56 RDF Schema RDF Annotations RDF Query CORESE search engine CG Support CG Fact Base Query Graph Results in CG Results in RDF CORESE TranslationProjectionTranslation

57 Relation properties Onto logie CountryCompanyPerson employssubdivisionOfactivity Inanimate Entity nationality domain range RDF S subClassOfrangedomaintypesubPropertyOf Property Resource Class Literal Anno tation RDF subdivisionOfactivity Telecom

58 Relation properties RDF(S) rdfs:Class rdf:Property domain, range Resource Property RDF Annotation CG Concept Type Relation Type Signature Concept Relation Conceptual Graph RDF & CG

59 Relation properties Transitivity, Symmetry, Reflexivity, Inverse for RDF properties: Annotations are augmented with new knowledge deduced from these properties Transitivity, symmetry and inverse are computed once and added to annotations Reflexivity is computed on the fly according to queries

60 Inference Rules for RDF Augment the ontology with rules that enable to deduce and add new knowledge to annotations IFa team participates to a consortium AND a person is a member of the team THEN the person participates to the consortium IF [Person:?p]-(member)-[Team:?t]- (participates)-[Consortium:?c] THEN [Person:?p]-(participates)-[Consortium:?c] Forward chaining inference engine

61 Inference Rules for RDF RDF Rule Syntax </c:Team </c:Person </c:Person

62Conclusion Conclusion The CoMMA system is implemented : A Corporate Semantic Web tested at T Nova Systems (Deutsche Telekom) and CSTB testbed for Corporate Semantic Web technologies : XML, Agents, Ontology, Semantic metadata, Learning

63Conclusion Conclusion (2) Corese semantic engine : RDF(S) and Conceptual Graphs tested at Renault on a design project memory tested with the Gene Ontology