Fabien GANDON, Laurent BERTHELOT, Rose DIENG A Multi-Agents Platform for a Corporate Web Semantic aa m as

A Multi-Agents Platform for a Corporate Web Semantic Plan  Corporate memory materialization in CoMMA  Objectives & Overall Approach  Use of RDF(S) to build a corporate semantic web  CoMMA MAS architecture overview  Handling distribution of annotations  Allocating new annotation  Distributed query-solving

A Multi-Agents Platform for a Corporate Web Semantic 3 What is CoMMA ?  CoMMA: European project : Provide a corporate memory management framework. Started February Ended February 2002  2 application & trial scenarios  Assist new employee integration  Support technology monitoring activities

A Multi-Agents Platform for a Corporate Web Semantic 4 Approach  Corporate memories as heterogeneous and distributed information landscapes  Stakeholders are a heterogeneous and distributed population  Exploitation of corporate memory involves heterogeneous and distributed tasks CM Materialization CM Exploitation XML: Web standard, Structure, Extend, Validation, Transform RDF(S) & K. Eng.: Annotation, Schemas Multi-Agent System: Modularity, Distributed, Collaboration, Sem. M.P. Machine Learning: Adaptability  Positioning & Approach  CoMMA: European project : Provide a corporate memory management framework. Started February Ended February 2002

A Multi-Agents Platform for a Corporate Web Semantic 5 Overall Schema Corporate Memory Multi-Agents System Learning User Agent Learning Profile Agent Ontology and Models Agent User Agent Learning Interconnection Agent Knowledge Engineer Ontology Models - Enterprise Model - User's Profiles

A Multi-Agents Platform for a Corporate Web Semantic 6 Overall Schema Corporate Memory Multi-Agents System Learning User Agent Learning Profile Agent Ontology and Models Agent User Agent Learning Interconnection Agent Author and/or annotator of documents Annotation Document

A Multi-Agents Platform for a Corporate Web Semantic 7 Overall Schema Corporate Memory Multi-Agents System Learning User Agent Learning Profile Agent Ontology and Models Agent User Agent Learning Interconnection Agent End User Annotation Document Annotation Document Annotation Document Query

A Multi-Agents Platform for a Corporate Web Semantic 8 Overall Schema Corporate Memory Multi-Agents System Learning User Agent Learning Profile 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

A Multi-Agents Platform for a Corporate Web Semantic 9 A Corporate Semantic Web  RDF : Resource Description Framework  Describe Web resources  RDF Schema (to formalize the ontology) OS AD Memory  Approach :  Ontology in RDFS (O'CoMMA)  Description the Situation in RDF:  User Profiles  Organization model  Annotations in RDF describing Documents  Toward a corporate semantic web  Annotated world for agents (quickly intelligent)

A Multi-Agents Platform for a Corporate Web Semantic Ontology in RDFS Entity GroupPerson Employee Member range domain Ontology hierarchy RDF annotation RDF(S) - Aspects used by the agents managing the annotations Group: Acacia Employee rangedomain Person: Fabien Annotation triplet and graph (Acacia, Employee, Fabien)

A Multi-Agents Platform for a Corporate Web Semantic 11 MAS Architecture Mémoire d'entreprise Système Multi-Agents Apprentissage Agent Utilisateur Apprentissage Agent groupe d'intérêts Agent Ontologie et Modèles Agent Utilisateur Apprentissage Agent d'inter- connexion Ingénieur de la connaissance Auteur et/ou Annotateur de documents Utilisateur final Annotation Document Annotation Document Annotation Document Annotation Document Ontologie Modèles - Modèle d'entreprise - Profils d'utilisateurs Requête

A Multi-Agents Platform for a Corporate Web Semantic 12 Multi-agents information system for the CM  CoMMA is an heterogeneous multi-agents information system  Several types of agents  Deal with duality of information distribution:  i.e. scattered data, information & knowledge  i.e. diffuse captured information and knowledge  Agent paradigm adequacy:  Collaboration  Global Capitalization  Autonomy & Individuality  Local Adaptation  From Macroscopic to Microscopic  Functional analysis for high level functions: societies  Society internal functional analysis: roles  in // scenario and use-cases analysis: interactions

A Multi-Agents Platform for a Corporate Web Semantic 13 Users' society Annotations Society Ontology and Model Society Interconnection Society CoMMA Society Sub-societies and Roles Ontologist Agents MediatorsArchivists Profile Managers Profiles Archivists InterfaceControllers FederatedMatchmakers

A Multi-Agents Platform for a Corporate Web Semantic 14 Users' society Annotations Society Ontology and Model Society Interconnection Society Roles Interaction specifications Ontologist Agents MediatorsArchivists Profile Managers Profiles Archivists InterfaceControllers FederatedMatchmakers ontology

A Multi-Agents Platform for a Corporate Web Semantic 15 Roles description implied role characteristics Zooming on the annotation society Annotations Society Mediators Archivists Scenarios and use cases to derive interactions and protocols spec. AMLocal:AM*:AM *:AA 1:cfp 2:cfp 3:propose :protocol fipa contract net :content :language CoMMA-RDF :ontology CoMMA Ontology 5:accept/ reject :protocol fipa contract net :content :language CoMMA-RDF :ontology CoMMA Ontology 4:propose 6:accept/ reject 6:accept/ reject 7:inform 8:inform

A Multi-Agents Platform for a Corporate Web Semantic 16 Interactions AA-AM in allocating an annotation  C-Net : Annotation allocation  AM - AA discuss best place to archive  Contract-net (CfP, Proposal, Accept/Reject)  Allocation criteria: pseudo-semantic measure <rdf:RDF xmlns:rdf=" xmlns:rdfs=" xmlns:CoMMA=" CfP UMTS Analysis Article Title Literal: "CfP UMTS Analysis" Article Author Person

A Multi-Agents Platform for a Corporate Web Semantic 17 Article Title Literal: "CfP UMTS Analysis" Article Author PersonAnnotation Archive ABIS Interactions AA-AM in allocating an annotation Report Title Literal: "Negotiation in C-Net" Literal: "Zeno paradox" Report Author Person 17 How close are they ? Report Author Comity 10 Book Author Person 89 Book Title Literal: "Agents for dummies" Literal: "Francs and Euros"... 47

A Multi-Agents Platform for a Corporate Web Semantic 18 Allocating an annotation: lexicographical distance Article Title Literal: "CfP UMTS Analysis" Report Title Literal: "Negotiation in C-Net" Literal: "Zeno paradox"... B low B up Dist L (Lit A, Lit B ) = | Abscissa(Lit B ) - Abscissa(Lit A ) | Classic lexicographical distance: (  pseudo semantic) Dist I (Lit X, [B low, B up ]) if Lit X  [B low, B up ] then = 0 else = Min(Dist L (Lit X, B low ), Dist L (Lit X, B up )) Distance to a literal interval: (  pseudo distance)

A Multi-Agents Platform for a Corporate Web Semantic 19 Allocating an annotation: semantic distance Article Author Person Report Co-Author Student C2C2 C1C1 C3C3 C4C4 C5C5 C6C6 C7C7 C8C8 R1R1 R2R2 R4R4 R5R5 R6R6 R3R3 Concept types hierarchy Relation types hierarchy Distance from Type 1 to Type 2 through least common super-type Dist H (Type 1,Type 2 ) = SPath(Type 1,LCST) +SPath(Type 2,LCST) SPath(,): number of edges through generalisation links LCST: least common super type = shared characteristics Dist TL (Type 1,Lit X ) = (Max C *2+1) Triple-triple: conditional sum with normalisation & weights Dist TFABIS (Triple A, Triple B ) = Dist C1 + Dist R + Dist C2 Dist Ci = W C * Dist H (Type 1,Type 2 ) or W C * Dist H (Type,Lit) or W L * N * Dist I (Lit, [B low,B up ]) N=Max C *2/Max L vehicle carbicycle wagoncoupé LCST Toy Example Distance(coupé,bicycle) = 3 tandem vehicle carbicycle wagoncoupé LCST Toy Example Distance(coupé,wagon) = 2 tandem

A Multi-Agents Platform for a Corporate Web Semantic 20 ABIS Annotation Allocating an annotation: final pseudo-distance Dist AABIS (Triple,ABIS) = Min(Dist TFABIS (Triple,Triple i ) Triplet i  ABIS  Allocation criteria:  Winner = Archivist with the smallest distance  Effect: cluster annotations having close semantic contribution  specialise the archives  One use: specialisation improve query solving and respect knowledge distribution sub-type  Dist = 0 Dist(An X, AA Y ) = Dist AABIS (An X, ABIS Y ) + Dist ACAP (An X, CAP Y )

A Multi-Agents Platform for a Corporate Web Semantic 21 Interactions AA-AM in solving a request  Fragmentation et distributed queries  Co-operatively solve a query (multi-stage Query-Ref)  AM decomposes submitted query into sub-queries  Allocation of sub-query based on ABIS  Overlap description  Refines service description of Archivists  Target multicast communications in query-solving  Exploit archive specialisation obtained by the distribution of annotations query needs (in ontological terms) (ABIS) archive contribution to memory &Description of the overlap need/archive (OBSIQ)

A Multi-Agents Platform for a Corporate Web Semantic 22 DOM RDF structure ?AuthorName ?AuthorFirstName ?DocTitle ~smith Nice France ?EditorPhone 2000 hofstadter douglas ? ! ? ? ! ! ? ! ! ! ! !

A Multi-Agents Platform for a Corporate Web Semantic 23 Decomposition (Constraints) ! !! ! ! !! ! ? ?? ? ! !! ! ! !! ! ! !! ! ! !! ! ! !! ! !!! ! !! ! !!! ! !! !!! !! !! !!!! !!!! !  Solving / Decomposition  AM simplifies + decomposes  sub-queries to AA  Bottom-up constraints solving  Top-down question solving  URI as cut/joint points

A Multi-Agents Platform for a Corporate Web Semantic 24  Solving / Decomposition  AM simplifies + decomposes  sub-queries to AA  Bottom-up constraints solving  Top-down question solving  URI as cut/joint points Decomposition (Questions) & merging ! !! ! ! !! ! ? ?? ? ? ?? ? ? ?? ? !! ! ? ?? ? !! ! ? ! ! ?? ? !! !  Solving / Merging  AM merges partial results  AM solves cross-references

A Multi-Agents Platform for a Corporate Web Semantic 25 Conclusion  Ergonomics problems and complexity  Large scale real evaluation Working system i.e. proof of concept Working system i.e. proof of concept Usability and Usefulness recognized Usability and Usefulness recognized Developer appreciation of Agent-Onto coupling Developer appreciation of Agent-Onto coupling Industrial interest in the dvp nt of the prototype Industrial interest in the dvp nt of the prototype  Focused criticisms:  pseudo-semantic distance; literal analysis  over specialisation; fine tuning, other criteria  decomposition improvements (existential qualification, constraint focal point and heuristics)  Results  PhD to be defended in October  Take home message: "looking for Post Doc."  Take home message: "looking for Post Doc."  System implementation ( ) & trial:

A Multi-Agents Platform for a Corporate Web Semantic 26 CoMMA: Login & JADE Agent Management GUI

A Multi-Agents Platform for a Corporate Web Semantic 27 CoMMA: Making an annotation

A Multi-Agents Platform for a Corporate Web Semantic 28 CoMMA: Message passing in allocating an annotation

A Multi-Agents Platform for a Corporate Web Semantic 29 CoMMA: Submitting a query

A Multi-Agents Platform for a Corporate Web Semantic 30 CoMMA: Message passing in query-solving

A Multi-Agents Platform for a Corporate Web Semantic 31 CoMMA: Query result

A Multi-Agents Platform for a Corporate Web Semantic 32