Distributed Artificial Intelligence and Ontology-based Systems for Knowledge Management. Fabien Gandon  Post-doc “Semantic Web Services, Context Awareness and Privacy” Carnegie Mellon University  Ph.D. “Distributed A.I. and Knowledge Management” I.N.R.I.A. Sophia Antipolis  Lecturer (moniteur) computer science University Nice  D.E.A. “Image processing and Vision systems” University Rouen  Graduate engineer “Applied mathematics” I.N.S.A. Rouen

/ 2 Introduction and plan Domain of research: −Distributed (formal) knowledge −Distributed (artificial) intelligence PhD research: “D. A. I. and knowledge” −Semantic intrawebs for corporate memories −Ontology engineering for semantic intrawebs −Multi-agent architecture for semantic intrawebs Post-doc research: “D. A. I. and services” −Multi-agent architecture & Semantic Web services −Context-awareness for mobile accesses −Knowledge-based privacy enforcement Wrap-up interactions PLAN

/ 3 Web to humans The Man Who Mistook His Wife for a Hat : And Other Clinical Tales by Oliver W. Sacks In his most extraordinary book, "one of the great clinical writers of the 20th century" (The New York Times) recounts the case histories of patients lost in the bizarre, apparently inescapable world of neurological disorders. Oliver Sacks's The Man Who Mistook His Wife for a Hat tells the stories of individuals afflicted with fantastic perceptual and intellectual aberrations: patients who have lost their memories and with them the greater part of their pasts; who are no longer able to recognize people and common objects; who are stricken with violent tics and grimaces or who shout involuntary obscenities; whose limbs have become alien; who have been dismissed as retarded yet are gifted with uncanny artistic or mathematical talents. If inconceivably strange, these brilliant tales remain, in Dr. Sacks's splendid and sympathetic telling, deeply human. They are studies of life struggling against incredible adversity, and they enable us to enter the world of the neurologically impaired, to imagine with our hearts what it must be to live and feel as they do. A great healer, Sacks never loses sight of medicine's ultimate responsibility: "the suffering, afflicted, fighting human subject." Find other books in : Neurology Psychology Search books by terms : Our rating : sacks

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/ 5 Positioning (semantic Web) Internet & Web (shared infosphere for humans) Intranets & intrawebs −Current trend: reuse internet and web technologies −Same advantages and same drawbacks Semantic Web (shared infosphere for machines) −XML: W3C standard for structuring data and documents −RDF(S): W3C standard for metadata/semantic annotations semantic intrawebs Hyp: corporate memories as semantic intrawebs

/ 6 Positioning (ontology) Knowledge engineering & ontology (modularity, reuse) −Assertional knowledge e.g., "Hugo wrote Notre-Dame de Paris" −Ontological knowledge e.g., "Authors write books“ −Ontology: an explicit, partial account of the semantic structure encoding the rules that constrain our representation of reality ontology Hyp: ontology to support semantic intraweb

/ 7 Positioning (DAI) and context (CoMMA) Distributed artificial intelligence −Multi-agent systems: study design of artificial societies of intelligent agents −Agent: clearly identifiable artificial entity; situated in an environment which it senses, reacts to and acts upon; self-control of its behavior; social abilities to interact with other agents / humans; −Multi-agent information systems: situated in & distributed over an information network multi-agent system Hyp: multi-agent system to manage semantic intraweb

/ 8 European project CoMMA CoMMA Corporate Memory Management through Agents Atos-Origin, CSELT Telecom Italia, CSTB, INRIA, LIRMM, T-Nova Deutsche Telekom, University of Parma Two application and trial scenarios: −Assist new employee integration −Support technology monitoring activities Technical choices: −Materialization of memory RDF(S) and its XML syntax (manipulated with CORESE) −Exploitation of memory Multi-agent system and machine learning techniques (implemented with ) (implemented with WEKA)

/ 9 Positioning and pointers Dynamically integrating heterogeneous sources of information OBSERVER [Mena et al., 1996] InfoSleuth [Nodine et al., 1999] Carnot [Collet et al., 1991] InfoMaster [Genesereth et al., 1997] SIMS [Arens et al., 1996] RETSINA [Decker & Sycara, 1997] Manifold [Kirk et al.,1995] Assist the management of digital libraries SAIRE [Odubiyi et al., 1997] UMDL [Weinstein et al., 1999] Organizational knowledge management: −Collaborative gathering, filtering and profiling CASMIR [Berney & Ferneley, 1999] Ricochet [Bothorel & Thomas, 1999] −Mobile access, domain model, document classification KnowWeb [Dzbor et al., 2000] −Taxonomy of topics, profiling and push RICA [Aguirre et al., 2000] −Ontology and corporate memory: multiple ontologies FRODO [Van Elst & Abecker, 2001] semantic intraweb, ontology, user profiling  You are here

/ 10 Corporate Memory Multi-Agents System Learning User Agent Learning Profile Agent Ontology and Models Agent User Agent Learning Interconnection Agent Functionalities: annotate, pull and push Knowledge Engineer Authors and annotators of documents End User Annotation Document Annotation Document Annotation Document Annotation Document Ontology Models - Enterprise Model - User's Profiles Query annotation pull and push

/ 11 Annotate documents (content awareness) RDF(S): annotated world for software in order to make inferences & help users exploit memory −RDF = Resource Description Framework (annotation model) Annotation in RDF with XML syntax: Documentaar.doc TitleAnnual activity report of ACACIA Annual activity report of ACACIA Author Personrdieng Annotation in RDF with graph representation: Document: Document: Author Person: Title "Annual activity report of ACACIA"

/ 12 Annotation schema (semantic awareness) Entity DocumentPerson Author Creator range domain classes - concepts properties - relations RDF(S): annotated world for software in order to make inferences & help users exploit memory −RDF = Resource Description Framework (annotation model) −RDFS = RDF Schema (annotation vocabulary / ontology) ClassEntity ClassDocument subClassOfEntity... PropertyAuthor subPropertyOfCreator domainDocument rangePerson... In graph representation:

/ 13 Annotate persons (context awareness) User modeling (extract of my user profile) EngineerFabien.Gandon FamilyName GANDON FirstName Fabien BirthDate (...) HasForWorkInterest MultiAgentSystemTopic <CoMMA:MultiAgentSystemTopic rdf:about=" (...) HasForPersonalInterest HumanScienceTopic <CoMMA:HumanScienceTopic rdf:about=" (...) EmployeeFabien.Gandon HireDate EmployedBy LocalOrganizationGroup EmploymentContractTemporary HasForActivity Research Idem for organization modeling (structures, relations...)

/ 14Progression  PhD research: “D.A.I. and knowledge”  Semantic intrawebs for corporate memories  Ontology engineering for semantic intrawebs  Multi-agent architecture for semantic intrawebs  Post-doc research: “D.A.I. and services”  Semantic Web services &Context-awareness  Personal resource management & privacy enforcement  Experimental results  Wrap-up

/ 15 Methodological steps (1 & 2) Ontology building in five steps Step 1 - Data collection and analysis −Scenario-driven analysis: users’ scenario reports & grid −Motivate data collection internal & external to organization −Capture aspects conceptualization to assist scenarios technical reportsUMTS managercolleagues Extract: “... wonder if there are technical reports about UMTS, then...” “... what this manager or one of his colleagues wrote for...” Step 2 - Build a lexicon −Capture terms and their definitions −First intermediary representation of the ontology −Constraint: one and only one occurrence of a definition −Disambiguate terms, e.g.: COLLEAGUE n. (lat. collega) someone who shares the same profession || one of a group of people who work together.

/ 16 Methodological steps in ontology engineering (3) Step 3 - Enriching lexicon structure −Split concepts, properties and attributes into different tables −Augment with relevant semantic aspects (e.g. subsumption) −Enrich, augment, refine,... for both humans and machines −Taxonomic skeleton: top-down / bottom-up / middle-out Step 4 - Script translating tables into RDFS

R5R5 C8C8 Analysis of the three levels present in RDF(S) TiTi A term CiCi Intension concept C i Instance of concept RiRi Intension relation R i Instance of relation Instanciation links Extension concept C i or relation R i CiCi RiRi T1T1 T3T3 T4T4 T2T2 T5T5 Up to that step RDF(S) has the required expressiveness C2C2 C1C1 C3C3 C4C4 C5C5 C6C6 C7C7 C8C8 R1R1 R2R2 R4R4 R5R5 R6R6 R3R3 Term used as a label... Organization part which is a sub division of of a Research Direction, corresponding to sub interest field e.g mobile transmission department division... Synonyms... Mail sent in electronic format over a computerized world-wide communication system e- mail electronic mail mail... Mail transmitted via the post office mail post mail... Ambiguity Multi- instanciation

/ 18 Methodological steps in ontology engineering (5) Step 5 - Factorizing knowledge (when needed) −Declare algebraic properties of relations (symmetric / transitive / reflexive relations) colleague(x,y)  colleague(y,x) colleague(x,y)  acquaintance(x,y)  colleague(y,x) colleague true true enacquaintance between two persons acquaintance between two persons who work together. who work together. fraccointance entre deux personnes accointance entre deux personnes travaillant ensemble. travaillant ensemble. encolleague colleague enco-worker co-worker frcollegue collegue frcollegue de travail collegue de travail

/ 19 Methodological steps in ontology engineering (5) Step 5 - Factorizing knowledge (when needed) −Declare algebraic properties of relations (symmetric / transitive / reflexive relations) −No one generates all the instances of colleague by hand −"I am a colleague of X because I work in the same group as X" (inference) −Encode axiomatic knowledge in rules and definitions colleague(x,y) (  z group(z)  include(z,x)  include(z,y)) colleague(x,y)  person(x)  person(y)  (  z group(z)  include(z,x)  include(z,y)) IF (rule for sufficient condition) IF (rule for sufficient condition) Group Include ?x Person ?x Include ?y Person ?y THEN THEN ?x Person ?x Colleague ?y Person ?y

/ 20 Summary of methodology Results −Design stages: −In CoMMA this method provided O'CoMMA 470 concepts (taxonomy depth = 13 subsumptions). 79 relations (taxonomy depth = 2 subsumptions). 715 terms in English and 699 in French. 550 definitions in English and 547 in French. Data collectionLexicons RDFS version Algebraic properties Rules Coverage ? Structured tables Scenarios

/ 21 Resulting ontology: O'CoMMA Structure: −Abstract top & middle layer for corporate memory: reusable −Middle layer for domain: reusable in same domain −Extension layer: usable but not reusable −Reuse tested e.g., CSELT  CSTB, APROBATIOM, KMP Top: abstract Middle: common notions Extension: specific OrganizationDocumentPersonDomain dedicated to corporate memory

/ 22Progression  PhD research: “D.A.I. and knowledge”  Semantic intrawebs for corporate memories  Ontology engineering for semantic intrawebs  Multi-agent architecture for semantic intrawebs  Post-doc research: “D.A.I. and services”  Semantic Web services &Context-awareness  Personal resource management & privacy enforcement  Experimental results  Wrap-up

/ 23 A multi-agent architecture for CoMMA Problem = handle information distribution −Handle naturally scattered information & knowledge −Assist diffusion of captured information and knowledge Follow multi-agent paradigm: −Artificial societies collaborating for global capitalization −Artificial individual intelligence, able to locally adapt Step 1 - Sub-societies identification −Started from the tasks to be performed (provide ontology, manage annotations, manage users and matchmaking) −Thus four sub-societies to handle these four tasks: Ontology & model sub-society User-dedicated sub-society Annotation-dedicated sub-society Matchmakers sub-society

/ 24 Organizing resource-dedicated sub-societies Replication resource- dedicated Replication Distribute resource only workload & network load +/- redundancy+ Complete replication redundant & less network load + content replicated – Distribute roles & resource distribute workload+ increase network load– Cooperative resource- dedicated Peer-to-Peer Representative Resource- dedicated Hierarchy Step 2 - Analyse possible organization structures:

/ 25 Roles and interactions Users' society Annotations Society Ontology and Model Society Interconnection Society Ontologist Agents MediatorsArchivists Profile Managers Profiles Archivists InterfaceControllers FederatedMatchmakers Step 3 - Roles and interactions

/ 26 Zooming on annotation-dedicated sub-society Annotations Society MediatorsArchivists The annotation-dedicated sub-society −Hierarchy: Mediators & archivists −Manage local sources of annotations distributed over the organization network Propose annotation-related services to others −Archive new annotations on documents of the memory −Search & retrieve references matching queries −Notify registered agents of the arrival of a new annotation Present 2 roles and then 2 interaction protocols

/ 27 Annotation-dedicated sub-society (mediator) Mediator role (supervises social interactions) −Contact point for other sub-societies −Supervising distribution of tasks for query solving −Allocating a new annotation to an archive −Notifying new annotation arrival to trigger push functions

/ 28 Annotation-dedicated sub-society (archivist) Archivist role (manage a local archive) −Attached to & exploits local base −Answers to query as much as it can with local knowledge −Proposes archiving services advertising the archive content

/ 29 Interaction mediator-archivist (allocation) Interaction 1 - Annotation allocation −Pb: archives distributed all over organization −Mediator & archivists discuss best archive for new annot. −Contract-net (CfP, Proposal, Accept/Reject): −Proposals: semantic distance new annotation - archive 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 AMLocal:Med*:Med *:Arch

/ 30 Content of an annotation or of an archive Simple annotation Article TitleCfP UMTS Analysis CfP UMTS Analysis Author Person Corresponding triples: Unstructured set of triples to describe content type Article Author Person Article Title Literal: "CfP UMTS Analysis" literalconcept Triple: subject predicate object

/ 31 Article Title Literal: "CfP UMTS Analysis" Article Author PersonAnnotationArchive Distance between an annotation and an archive How close are they ? Report Title Literal: "Negotiation in C-Net" Literal: "Zeno paradox" instances Report Author Person 27 instances Report Author Committee 10 instances Book Author Person 19 instances Book Title Literal: "Agents for dummies" Literal: "Franc and Euro"... 7 instances Statistics

/ 32 Building the distance Case 1 - literal values: lexicographical distance Case 2 - concept types: minimum length of path between two types through least common super type DistH(Type1,Type2) = Min(GPath(Type1,LCST)+GPath(Type2,LCST)) GPath(,): number of edges through generalization links LCST: least common super type = shared characteristics Distance between two triples (conditional weighted sum) document chartbook graphdiagram LCST Distance (diagram, graph) = 2 booklet Distance (diagram, booklet)= 4 LCST Dist TFStat (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 ]) with N=Max C *2/Max L

/ 33 Semantic distance between types Distance annotation  archive Dist TStat (Triple,Stat) = Min(Dist TFStat (Triple,Triple i ) Triple i  Stat −triple  stat: −annotation  stat: sub-type  Dist H = 0 −annotation  pref: Dist(An X, Arc Y ) = Dist AStat (An X, Stat Y )+Dist APref (An X, Pref Y ) −annotation  archive: Distance = allocation criteria of contract-net −"and the winner is..." the archivist with smallest distance −Cluster annotations & specialize archives −Improve query solving & respect knowledge distribution

/ 34 Interactions mediator-archivist in solving a query Interaction 2 - distributed query solving −Pb: archives of annotations distributed all over org. −Mediators & archivists share knowledge to solve a query −Composition of Query-Ref protocol for distributed solving 1:query-ref :protocol fipa query :content :language CoMMA-RDF :ontology CoMMA Ontology 4:inform 2a:query-ref 3a:inform 2b:query-ref 3b:inform 5a,b,c,...: query-ref 5a,b,c,...: inform 5a,b,c,...: query-ref 5a,b,c,...: inform :protocol fipa query :content :language CoMMA-RDF :ontology CoMMA Ontology LocalAM:Med*:Med *:Arc

/ 35 Overlap description Each archivist calculates the overlap between query needs and its archive statistics: −Archivists refine their service description for a query −Mediators target multicast communications −Exploit archive specialization obtained during the allocation of annotations query needs archive statistics & Description of the overlap need/archive

/ 36 RDF query: tree structure <rdf:RDF xmlns:rdf=" xmlns:CoMMA=" ?AuthorName ?AuthorName ?AuthorFirstName ?AuthorFirstName ?DocTitle ?DocTitle ~smith ~smith Nice Nice France France ?EditorPhone ?EditorPhone Edition hofstadter hofstadter douglas douglas ComputerScienceTopic ? ! ? ? ! ! ? ! ! ! ! !

/ 37Decomposition! !! ! ! !! ! ? ?? ? Tree decomposition & work distribution −Mediator prepares query (compact, cross-references) −Mediator decomposes query Bottom-up constraints solving ! !! ! ! !! ! ! !! ! ! !! ! ! !! ! !!! ! !! ! !!! ! !! !!! !! !! !!!! !!!! !

/ 38Decomposition! !! ! ! !! ! ? ?? ? Tree decomposition & work distribution −Mediator prepares query (compact, cross-references) −Mediator decomposes query Bottom-up constraints solving Top-down question answering URI as cut/joint points −Mediator sends subqueries to archivists according to overlap ? ?? ? ? ?? ? !! ! ? ?? ? !! ! ? ! ! ?? ? !! ! Forming final result −Mediator merges partial results −Mediator solves cross-references

/ 39 Overview of agent-based design Design stages: Result: Society providing ontology and corporate model Annotation-dedicated society Two trials at mid-project and project end in development spiral One public open-day demo: industrial & EU commission Usability and usefulness recognized by users/public Scenarios Architectural requirementsRolesBehavioursOntology Interactions Agent typesConfiguration requirements Deployment

/ 40Progression  PhD research: “D.A.I. and knowledge”  Semantic intrawebs for corporate memories  Ontology engineering for semantic intrawebs  Multi-agent architecture for semantic intrawebs  Post-doc research: “D.A.I. and services”  Semantic Web services &Context-awareness  Personal resource management & privacy enforcement  Experimental results  Wrap-up

/ 41 (c) CMU - Sadehlab Web service to humans To pay by check and/or register by mail, do not use this form! Please download and complete the PDF form instead. You can also complete the PDF version of the form electronically, and then print it and either fax or mail the printout to AAAI. (Note: You can only save your data entry if you have Acrobat 4.0 or higher. Acrobat Reader allows you to complete the form and print it, but not save it.) Student registrants please note that you are still required to send hard copy proof of your full-time student status to the AAAI office in order to receive the discounted student registration rates. First Name (and optional middle initial): Last Name (family or surname): Company or Affiliation: Mailing Address: City: State: USA Zip Code (Please enter your zip+4 code): Country: Foreign Postal Code: Work or Daytime Telephone: Address: Please select your tutorial(s) / workshop(s) and select the rate Sunday AMSunday PMMonday AMMonday PM $100* $300 *To qualify for this price, you must also register for the IJCAI technical conference above. Credit card number: submitreset T8 W2

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/ 43 (c) CMU - Sadehlab Positioning (semantic Web services) Internet & Web (shared service-sphere for humans) Semantic Web (shared infosphere for machines) Semantic Web Services (shared service-sphere for machines) SOAP, XMLP, WSDL, DAMLS, UDDI (v2  v3) Hyp: Semantic web services for e-Business and Enterprise Applications Integration Ontology Hyp: Ontology to support semantic Web services Distributed artificial intelligence −Intelligent Agent  Intelligent Services −Multi-agent systems  Service interaction & composition Hyp: Multi-agent architectures for webs of services

/ 44 (c) CMU - Sadehlab Interface without context awareness To pay by check and/or register by mail, do not use this form! Please download and complete the PDF form instead. You can also complete the PDF version of the form electronically, and then print it and either fax or mail the printout to AAAI. (Note: You can only save your data entry if you have Acrobat 4.0 or higher. Acrobat Reader allows you to complete the form and print it, but not save it.) Student registrants please note that you are still required1) to send hard copy proof of your full-time student status to the AAAI office in order to receive the discounted student registration rates. First Name (and optional middle initial): Last Name (family or surname): Company or Affiliation: Mailing Address: City: State: USA Zip Code (Please enter your zip+4 code): Country: Foreign Postal Code: Work or Daytime Telephone: Address: Please select your tutorial(s) / workshop(s) and select the rate Sunday AMSunday PMMonday AMMonday PM $100* $300 *To qualify for this price, you must also register for the IJCAI technical conference above. Credit card number: submitreset T8 W2

/ 45 (c) CMU - Sadehlab Interface with context awareness Please select your tutorial(s) / workshop(s) and ($100 rate) Sunday AMSunday PMMonday AMMonday PM Just do it ! T8 W2

/ 46 (c) CMU - Sadehlab Positioning (mobile access to SW&S) Mobile (net)working −Mobile networks: Phones / pagers  PDAs, sub-notebooks Telecom services  Web and online services −Mobile accesses  reduced & constrained interactions (interfaces, connectivity, cognitive workload) −Schism: service availability  & user availability  Context-awareness to support mobile access to semantic Webs and their services Hyp: Context-awareness to support mobile access to semantic Webs and their services

/ 47 (c) CMU - Sadehlab Positioning (context awareness) Context awareness State of the Art −Application leveraging context awareness Active Badge [Want et al., 92] ParcTab [Schilit, 95] Oxygen [Dertouzos, 99] GUIR [Hong & Llanday, 01] Aura [Garlan et al., 02] Application dependent and heterogeneous Redundant and scattered −Personal resources integration and unification Toolkits and widgets for wrapping [Dey et al., 00] e-Wallet: awareness & privacy  You are here Semantic Web & Services to provide a unified secure interface to personal resources (e-Wallet) Hyp: Semantic Web & Services to provide a unified secure interface to personal resources (e-Wallet)

/ 48 (c) CMU - Sadehlab Context of research myCampus: a context-aware environment aimed at enhancing access to services for everyday campus life at Carnegie Mellon University (CMU) −BBN, IBM, HP, Symbol and Boeing −Air Force Research Laboratory (contract F ) −Defense Advanced Research Project Agency (DARPA) (contract F ) Interactions with −SONAT: user-aware notification (D.o.D.) −CoSAR (I-X, KAoS/CoABS Grid): notification planning (AIAI) −SWAP: Semantic Web and Peer-to-peer for KM (IST program) My focus: Semantic Web & Services, Context- awareness and Privacy, M.A.S. and Ontologies

/ 49 (c) CMU - Sadehlab Open architecture – mobile access PDA & Wireless Network Agent roles: −Platform manager −User interaction manager −Growing collection of task-specific agents −e-Wallet manager Web resources −Semantic Web services −Semantic Web ontologies −Semantic Web annotations −Search engines Semantic Web servers S. Web Ontologies S. Web Annotations Other Web Resources Semantic Web Services Semanticsearchservices Task- specific Task-specific resources and APIs Communication toolkit (http, , IM, etc.) User interaction manager API NETWORK Platformmanager White & yellow pages MAS administration toolkit knowledge base API e-Wallet manager Security toolkit API Web services invocation toolkit API Inference engine API e-Wallet knowledge base Service activation rules Dynamic knowledge about owner Static knowledge about owner Loaded ontologies Privacy enforcement rules

/ 50 (c) CMU - SadehlabProgression  PhD research: “D.A.I. and knowledge”  Semantic intrawebs for corporate memories  Ontology engineering for semantic intrawebs  Multi-agent architecture for semantic intrawebs  Post-doc research: “D.A.I. and services”  Semantic Web services &Context-awareness  Personal resource management & privacy enforcement  Experimental results  Wrap-up

/ 51 (c) CMU - Sadehlab e-Wallet & the knowledge typology Secured unified semantic interface to access knowledge about the e-Wallet owner Static assertional knowledge: −User’s static profile (name, SSN, position, etc.) −Static contextual knowledge (campus description, etc.) fetched from outside as needed. Dynamic assertional knowledge: −User’s dynamic profile (contextual preferences, etc.) −Context knowledge (location, current activity, weather, etc.) fetched from outside as needed. −Privacy: authorization & obfuscation (precision, lie, etc.) Ontological knowledge, fetched from an outside repository of ontology at startup.

/ 52 (c) CMU - Sadehlab privacy query answer e- Design of an e-Wallet Three-layer architecture −Core knowledge: static & dynamic knowledge of user −Service Layer: invoke external sources of knowledge: web services and personal resources −Privacy layer: enforce privacy rules on external requests: access, obfuscation (precision, lies) Asserting elementary needs for authorized information Pre-check access rights Post-check access rights Fetch useful static knowledge Application of obfuscation rules Query context assertion Query Assertion of authorized knowledge Result Call relevant external services service Core Know- ledge

/ 53 (c) CMU - Sadehlab Processes in the e-Wallet OWL Meta-model in CLIPS OWL Meta-model in CLIPS Ontology in OWL Ontology in OWL Annotation in OWL Annotation in OWL Rule in (R)OWL Rule in (R)OWL Services in (W)OWL Services in (W)OWL Security in (S)OWL Security in (S)OWL Query in (Q)OWL Query in (Q)OWL Ontology stylesheet Ontology stylesheet & Annotation stylesheet Annotation stylesheet & Rule stylesheet Rule stylesheet & Service stylesheet Service stylesheet & Security stylesheet Security stylesheet & Query stylesheet Query stylesheet & Ontology in CLIPS Ontology in CLIPS Annotation in CLIPS Annotation in CLIPS Rule in CLIPS Rule in CLIPS Service rule in CLIPS Service rule in CLIPS Security rule in CLIPS Security rule in CLIPS Query rules in CLIPS Query rules in CLIPS XSLT Engine Result in OWL Result in OWL JESS

/ 54 (c) CMU - Sadehlab e-Wallet core: JESS inference engine CLIPS language: −Atoms, numbers, strings, functions, variables, Java reflection −Ordered facts (not used here) −Unordered facts (with templates // classes) −Forward rules −Queries: special kind of rule with no right-hand-side −Declare backward chaining reactive rules using (do-backward-chaining …) function (template  need-template) −The RETE algorithm (person (name "Adeline") (age 20) (gender Female)) (defrule adult ?p =18)) => (assert (adult ?p))

/ 55 (c) CMU - Sadehlab e-Wallet semantic engine RDF Triple model RDFS & OWL meta-model (e.g, symmetry of properties) (deftemplate triple "Template representing a RDF triple" (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) ) (deftemplate triple "Template representing a RDF triple" (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) ) Triple: (predicate, subject, object) SymmetricProperty SymmetricProperty (triple (predicate " (subject " (object " ) (defrule symmetry (declare (salience 100)) (triple (predicate " (subject ?p) (object " (triple (predicate ?p) (subject ?x) (object ?y)) => (assert (triple (predicate ?p) (subject ?y) (object ?x))) ) (triple (predicate " (subject " (object " ) (defrule symmetry (declare (salience 100)) (triple (predicate " (subject ?p) (object " (triple (predicate ?p) (subject ?x) (object ?y)) => (assert (triple (predicate ?p) (subject ?y) (object ?x))) )

/ 56 (c) CMU - Sadehlab e-Wallet semantic engine Ontologies: (e.g., declare man, location, etc.) Annotations: (e.g., Fabien is in Smith Hall, etc.) (triple (predicate " (subject " (object " )... (triple (predicate " (subject " (object " )... (triple (predicate " (subject " (object " )... (triple (predicate " (subject " (object " )...

/ 57 (c) CMU - Sadehlab e-Wallet semantic engine Rules: (e.g., when in I am in a meeting I am busy) Meeting means busy Meeting means busy (defrule Meeting-means-busy... (triple (predicate " (subject ?person) (object ?activity)) (triple (predicate " ") (subject ?activity) (object " ") ) =>...(assert (triple (predicate " (subject ?person)(object " ") (defrule Meeting-means-busy... (triple (predicate " (subject ?person) (object ?activity)) (triple (predicate " ") (subject ?activity) (object " ") ) =>...(assert (triple (predicate " (subject ?person)(object " ")

/ 58 (c) CMU - Sadehlab Service and privacy layers −Special types of triples −Backward chaining reaction started by the query Privacy rules (clearance/revision) Service rules (dynamic access) Static migration rules e-Wallet semantic engine inference engine privacy service core know- ledge query answer (deftemplate authorized_triple (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) ) (deftemplate authorized_triple (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) ) (deftemplate service_triple (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) ) (deftemplate service_triple (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) ) (deftemplate triple (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) ) (deftemplate triple (slot predicate (default "")) (slot subject (default "")) (slot object (default "")) )

/ 59 (c) CMU - SadehlabQuery (defrule query (declare (salience 0)) … (authorized_triple (predicate " (subject " (object ?location)) => (store-result location ?location) ) (defrule query (declare (salience 0)) … (authorized_triple (predicate " (subject " (object ?location)) => (store-result location ?location) ) Query issued by the user 'nsadeh’ requesting the location of user ‘fgandon’ Three steps: −Query context assertion −Query rule definition Body: request for authorized triples Head: storage & pretty printing function −Rule execution

/ 60 (c) CMU - Sadehlab Privacy rules people can only know whether or not I am on campus people can only know whether or not I am on campus Privacy rule: grant access to location when on campus but obfuscate precision truth

/ 61 (c) CMU - Sadehlab Service rules provide location for IP Address &variable;#ip &variable;#ip provide location for IP Address &variable;#ip &variable;#ip (defrule provide-location-for-IP-Address (declare (salience 50)) … (need-dynamic_triple (predicate " (subject ?entity) (object ?location) ) … => (call-web-service " ?ip) ) (defrule provide-location-for-IP-Address (declare (salience 50)) … (need-dynamic_triple (predicate " (subject ?entity) (object ?location) ) … => (call-web-service " ?ip) )

/ 62 (c) CMU - SadehlabProgression  PhD research: “D.A.I. and knowledge”  Semantic intrawebs for corporate memories  Ontology engineering for semantic intrawebs  Multi-agent architecture for semantic intrawebs  Post-doc research: “D.A.I. and services”  Semantic Web services & Context-awareness  Personal resource management & privacy enforcement  Experimental results  Wrap-up

/ 63 (c) CMU - Sadehlab Prototype #1: proof of concept Mockup of multi-agent architecture Two task-specific agents −Restaurant Concierge Agent RCA (location, weather, food, price, time) −Message Filtering Agent (interests, current activity) + Jabber (Instant Messaging) Limited version of e-Wallet JSP pages to simulate User Interface Manager Three Web services −Location tracking −Calendar access −Weather report

/ 64 (c) CMU - Sadehlab logging preferences Setting preferences

/ 65 (c) CMU - Sadehlab Using services Restaurant Concierge Messagefeedback

/ 66 (c) CMU - Sadehlab

/ 67 (c) CMU - Sadehlab Experiment #1 with early prototype Before the experiment: −Office for Human Research Protections approval Institutional Review Board certificate −Selected group of 11 users, with a variety of profiles −Trained the 11 users during a 2-hour session + material The 3-day experiment involved: −Message filtering agent: 44 messages and 484 feedbacks −Restaurant concierge agent: 28 recommendations −Logs were generated for each one of these events. After the experiment: −Users had to fill a survey ~1/2 hour −Face-to-face de-briefing interviews ~15 minutes −Statistics on the logs

/ 68 (c) CMU - Sadehlab Experiment #1 extracts of feedback analysis

/ 69 (c) CMU - Sadehlab Experiment #1 most important results For the Restaurant Concierge, 12.5 % recommendations accepted thanks to context-awareness = 14.29% improvement. For the Messaging: filtering and routing based on static profile = too loose. −finer knowledge about the users and finer filtering criteria; −~70% of the messages benefit from context-aware filtering and routing e.g. messages to be sent only when available or when the day is over Need critical mass of content, users and useful services

/ 70 (c) CMU - Sadehlab Prototype #2: study of a useful service Focus: −Find out about interesting events & Get information to people who want it −Existing systems to distribute information? Design Computer Science Information Sc. Masters Electrical Eng. POSTERS

/ 71 (c) CMU - Sadehlab Experiment #2 H.C.I. study of a useful service Improving messaging for events: Improving messaging for events: −Tag each poster with a list of locations (situated) −Display poster when user is close to it or explicitly interested in the topic (distribute) −Peer-to-peer publishing (person-2-person) −Integrate with calendar (remember) −Stop publishing when outdated (maintenance) −Under evaluation; good feedback

/ 72Progression  PhD research: “D.A.I. and knowledge”  Semantic intrawebs for corporate memories  Ontology engineering for semantic intrawebs  Multi-agent architecture for semantic intrawebs  Post-doc research: “D.A.I. and services”  Semantic Web services &Context-awareness  Personal resource management & privacy enforcement  Experimental results  Wrap-up

/ 73 Wrap-up: conclusion Ph.D. contributions −Methodologies and tools to build ontology, semantic intraweb and associated multi-agent architecture −A running prototype as a proof of concept for each point −Both are reusable contributions −Current extensions: Web scrappers & Semantic gateways Post-Doc contributions ( as a continuation) −Design M.A.S. architecture for Semantic Web Services with a focus on mobile accesses −Use of S.W. and ontologies to allow context-awareness −Integration S.W. and ontologies in privacy enforcement

/ 74 Wrap-up: discussion Research ahead (remain at the state of the art) −Ontology and Memory life cycles (collective-ware, coherence) −Semantic Webs (semantic mapping, open/extra, community, security) −Semantic Services and Agents (EAI, e-Sourcing, Grid, autonomic) −Mobile & ubiquitous Semantic Web (connectivity, pervasiveness) −Intelligent user interfaces (customization, awareness, ergonomics) Perfectly integrated with research of ACACIA & INRIA D.A.I. & Web Services Ontology & Knowledge representation Semantic Web & Knowledge management formalisms & applications methods & schemata formalisms, semantic ground for inferences & communication software platforms for life-cycle formalisms, annotated worlds, methods, cases software architectures, deployment platforms

/ 75Acknowledgments ACACIA Laboratory - INRIA Sophia Antipolis −Dr. Rose Dieng-Kuntz (Research Director – ACACIA project leader) −Members of ACACIA team and logistics of INRIA Sophia −ATOS-Origin, CSTB, Deutsch Telekom T-Nova, Italia Telecom, LIRMM and University of Parma −IST Program (CoMMA project) School of computer science – Carnegie Mellon Uni. −Prof. Norman M. Sadeh (Mobile Commerce Laboratory Director, SCS CMU, Free University of Amsterdam, European Commission) −Members of myCampus team and logistic of ISRI −BBN, IBM, HP, Symbol and Boeing −IST Program (SWAP project) −Air Force Research Laboratory (contract F ) −Defense Advanced Research Project Agency (DARPA) (contract F )

(c) CMU - Sadehlab Demo: CoMMA Ontology interface

(c) CMU - Sadehlab CoMMA: profile-based retrieval

Demo: interface CoMMA specified by INRIA, implemented by ATOS-Origin

Demo: CoMMA annot. allocation interactions cfp bids result 1 mediator 3 archivists

Demo: CoMMA query solving interactions

/ 81 Demo: InfoBridge virtual posters

/ 82

/ 83Acknowledgments ACACIA Laboratory - INRIA Sophia Antipolis −Dr. Rose Dieng-Kuntz (Research Director – ACACIA project leader) −Members of ACACIA team and logistics of INRIA Sophia −ATOS-Origin, CSTB, Deutsch Telekom T-Nova, Italia Telecom, LIRMM and University of Parma −IST Program (CoMMA project) School of computer science – Carnegie Mellon Uni. −Prof. Norman M. Sadeh (Mobile Commerce Laboratory Director, SCS CMU, Free University of Amsterdam, European Commission) −Members of myCampus team and logistic of ISRI −BBN, IBM, HP, Symbol and Boeing −IST Program (SWAP project) −Air Force Research Laboratory (contract F ) −Defense Advanced Research Project Agency (DARPA) (contract F )