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ACACIA in short… Objectives: Offer methodological and software support (i. e. models, methods and ACACIA in short… Objectives: Offer methodological and software support (i. e. models, methods and tools) for construction, management and diffusion of corporate memories. n Corporate memory : Explicit and persistent materialization of crucial knowledge and information of an organization to ease access, sharing and reuse by the members of the organization in individual and collective tasks. = Individuals + Organization + Technology Need of a multidisciplinary approach n

n Past and Current work on Corporate Memories (10 minutes) n Current and future n Past and Current work on Corporate Memories (10 minutes) n Current and future work with Semantic WS (10 minutes)

Corporate web & intranet web server intranet mail Corporate web & intranet web server intranet mail

Corporate semantic web corese CG RDF OWL semantic annotations ontologies web server RDFS rules Corporate semantic web corese CG RDF OWL semantic annotations ontologies web server RDFS rules web server intranet mail

Corporate semantic Web Resources: persons, documents (XML, HTML. . . ), services, software, hardware, Corporate semantic Web Resources: persons, documents (XML, HTML. . . ), services, software, hardware, etc. n Ontologies: describing the conceptual vocabulary shared by the organisation communities n Semantic annotations: on these resources (e. g. persons’ skills, document contents, characteristics of services/software/hardware), using the vocabulary defined in the ontologies n Diffusion on the intranet / corporate web. n

CORESE semantic search engine <accident> <date> 19 Mai 2000 </date> <description> <facteur>le facteur </description> CORESE semantic search engine 19 Mai 2000 le facteur Legacy sys. MAS and Corporate Semantic Web Schemata in RDFS CORESE XML Annotations in RDF formed by instances of schemata in RDFS Web stack RDFS CG Support RDF CG Base Rules QUERIES CG Rules Queries CG Query RULES ONTOLOGY RDFS RDF XML URI NAMESPACES UNICODE INFERENCES PROJECTION CG Results Users decisions/ push Documents query answer Ontologies Semantic Web server RDF/S

Select example Find the documents about Java and return the titles and the authors Select example Find the documents about Java and return the titles and the authors : select ? doc c: title ? person where ? doc ? topic ? doc ? title ? doc rdf: type c: concern rdf: type c: title ~ c: author c: Document ? topic c: Java ? title “web” ? person

Request language n Operators: = <= ~ != n XML Schema Datatypes : number, Request language n Operators: = <= ~ != n XML Schema Datatypes : number, boolean, string, date, etc. n Natural language: xml: lang=“en-us” n Combination of Boolean expressions and / or n Negation of arc, optional arc, paths n Query the RDF Schema …

Approximate search n Find approximation semantic and structural n Example: ¨ Request Technical Report Approximate search n Find approximation semantic and structural n Example: ¨ Request Technical Report about Java written by an engineer ? ¨ Approximate answer : Technical Report Engineer Team n Handout Syntax: select more where exp

Distance in the ontology Object Document Actor Person Engineer Team Researcher Report Research R. Distance in the ontology Object Document Actor Person Engineer Team Researcher Report Research R. Technical R. Course Handout

From type hierarchies to dendrograms Ontology 1 K 1 E. 5 D. 25 A From type hierarchies to dendrograms Ontology 1 K 1 E. 5 D. 25 A dendrogram J F . 25 B . 25 C . 5 1. 75 1 L . 5 G . 5 M . 25 H . 5 N . 25 I . 75. 5. 25 A B C D E F G H I J K L M 0 A C B D L E F G I H K M L E F G J N D N K J M D L L E E F G K K J J J N

Inferences & Rules (II) Classify a ressource If a person wrote a Ph. D. Inferences & Rules (II) Classify a ressource If a person wrote a Ph. D. thesis on a suject the s/he is a doctor and an expert on that subject. ? person author ? doc rdf: type Ph. DThesis ? doc concern ? topic ? person expert. In ? topic ? person rdf: type Ph. DThesis ? person concern author Person ? person Topic ? topic expert. In Ph. D ? person

Architecture HTTP Response XHTML, CSS, SVG Java. Script Join Projection engine Notio Type inference Architecture HTTP Response XHTML, CSS, SVG Java. Script Join Projection engine Notio Type inference engine CG Manager JDBC HTTP Request

HCI generation n Build a list with sub-classes of Person <select name=‘ihm_person’ title='Profession'> <query> HCI generation n Build a list with sub-classes of Person n HTML rendering: n Request associated to the list : ? p rdf: type get: ihm_person

IHM n Formulaire de requête n Engendré par connexion avec ontologie et graphe RDF IHM n Formulaire de requête n Engendré par connexion avec ontologie et graphe RDF n Requête prédéfinie éditée par l’utilisateur Objet ? select ? doc c: title ? person where Document Acteur Personne Ingénieur Équipe Chercheur Rapport R. Recherche R. Technique Cours Support C. ? doc ? topic ? doc ? title ? doc rdf: type c: concern rdf: type c: title ~ c: author c: Document ? topic c: Java ? title “web” ? person

Résultats n Réponse traduite en RDF/XML n Traitée par feuille de style XSLT n Résultats n Réponse traduite en RDF/XML n Traitée par feuille de style XSLT n Adaptable à l’utilisateur, au contexte n Peut engendrer HTML, SVG etc. XHTML RDF XML XSLT JSP SVG Java. Script

Intégration XHMTL+XML+XSLT+RDF n Dans une feuille de style XSLT : ¨ Appel au moteur Intégration XHMTL+XML+XSLT+RDF n Dans une feuille de style XSLT : ¨ Appel au moteur de recherche, ¨ Connexion à une BD : engendrer un schéma RDF ou des annotations n Intégration du résultat dans le flux de sortie XSLT CORESE JSP

Corporate distributed knowledge corese sparql CG RDF OWL semantic annotations ontologies web server RDFS Corporate distributed knowledge corese sparql CG RDF OWL semantic annotations ontologies web server RDFS rules web server intranet mail agents

allocating an annotation n archives distributed all over organisation find best archive for new allocating an annotation n archives distributed all over organisation find best archive for new annotation Contract-net (Cf. P, Proposal, Accept/Reject): : protocol fipa contract net : content : language Co. MMA-RDF : ontology Co. MMA Ontology 1: cfp AMLocal: Med 8: inform 2: cfp 7: inform 5: accept/ reject 2: cfp *: Med 4: propose 6: accept/ reject 3: propose 7: inform 6: accept/ reject *: Arch 3: propose *: Arch : protocol fipa contract net : content : language Co. MMA-RDF : ontology Co. MMA Ontology

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solving a query n n n archives distributed all over organisation share knowledge to solving a query n n n archives distributed all over organisation share knowledge to solve a query Composition of Query-Ref protocol : protocol fipa query : content : language Co. MMA-RDF : ontology Co. MMA Ontology Local. AM: Med 2 a: query-ref 5 a, b, c, . . . : query-ref 1: query-ref 4: inform 3 a: inform 5 a, b, c, . . . : inform *: Med 2 b: query-ref 5 a, b, c, . . . : query-ref 3 b: inform 5 a, b, c, . . . : inform *: Arc : protocol fipa query : content : language Co. MMA-RDF : ontology Co. MMA Ontology

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Corporate web services corese sparql CG RDF OWL semantic annotations ontologies web server RDFS Corporate web services corese sparql CG RDF OWL semantic annotations ontologies web server RDFS rules web server intranet web services enterprise applications mail agents

Web services & Enterprise application n Transversal use of enterprise modeling ¨ End of Web services & Enterprise application n Transversal use of enterprise modeling ¨ End of 90’s: enterprise modeling for KM ¨ In the past 2 years: technology and application integration can benefit from these models too n Evolution of KM scenarios ¨ Until end of 90’s focus on: knowledge capture, storage, access and diffusion ¨ More and more often: computation, decision, routing, transformation n Unified and integrated access to knowledge sources and corporate applications

Memories with a broaden scope n Corporate memories including: ¨ information storage services; ¨ Memories with a broaden scope n Corporate memories including: ¨ information storage services; ¨ information capture services; ¨ computation and inference services; ¨ information flows management services; ¨ information mediation services; ¨ information presentation services; n Resources may be internal or external ¨ external standard library, online service; ¨ interoperate smoothly and integrate workflows at the business layer.

Corporate semantic web services corese sparql CG RDF OWL semantic annotations ontologies web server Corporate semantic web services corese sparql CG RDF OWL semantic annotations ontologies web server RDFS rules web server intranet service annotations web services enterprise applications mail agents

Corese Requester Provider search input form run & display Corese Webapp Form servlet Invocation Corese Requester Provider search input form run & display Corese Webapp Form servlet Invocation servlet select submit

Discover and invoke a service Discover and invoke a service

Service description Poste. Secretaire

Input description & extension &xsd; #string &xsd; #string

Extension parameters OWL-S (…) ? x rdf: type c: Employee ? x c: Name ? n ? x c: Employee. Name ? n (…) Moussa Lo

Composing with memory Composing with memory

Corporate semantic web applications corese sparql CG RDF OWL semantic annotations ontologies web server Corporate semantic web applications corese sparql CG RDF OWL semantic annotations ontologies web server RDFS rules web server intranet service composition description service annotations web services enterprise applications mail agents

Ongoing… Manual & semi-automatic n Recording as macros n Ongoing… Manual & semi-automatic n Recording as macros n

Composite services Composite services

Corporate semantic web puzzle corese sparql CG RDF OWL semantic annotations ontologies web server Corporate semantic web puzzle corese sparql CG RDF OWL semantic annotations ontologies web server RDFS rules web server intranet service composition description service annotations web services enterprise applications mail agents