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Knowledge Standards W 3 C Semantic Web Olivier. Corby@sophia. inria. fr Knowledge Standards W 3 C Semantic Web Olivier. [email protected] inria. fr

2 PLAN W 3 C Semantic Web Standards n n n Two layers : 2 PLAN W 3 C Semantic Web Standards n n n Two layers : XML/RDF Syntax/Semantics XML : DTD, XML Schema, XSLT, XPATH, XQUERY RDF : RDFS, OWL, RIF, SPARQL

3 XML n n n Meta language : conventions to define languages Abstract syntax 3 XML n n n Meta language : conventions to define languages Abstract syntax tree language STANDARD Every XML parser in any language (Java, C, …) can read any XML document Data/information/knowledge outside the application A family of languages and tools

4 XML Family n n DTD : grammar for document structure XML Schema & 4 XML Family n n DTD : grammar for document structure XML Schema & datatypes XPath : path language to navigate XML documents XSLT : Extensible Stylesheet Language Transformation : transforming XML documents into XML (XHTML/SVG/text) documents

5 XSLT n n n Define output presentation formats OUTSIDE the application Everybody can 5 XSLT n n n Define output presentation formats OUTSIDE the application Everybody can customize/adapt outpout format for specific application/user/task Can deliver an application with some generic stylesheets that can be adapted Application generates XML as query result format processed by XSLT The XML output format can be interpreted as dynamic object by navigator : e. g. a FORM

6 XQuery n n n XML Query Language AKO programming language SQL 4 XML 6 XQuery n n n XML Query Language AKO programming language SQL 4 XML

7 Semantic Web 7 Semantic Web "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. " Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American, May 2001 q Information Retrieval & Knowledge Representation q W 3 C Standards (RDF/S, SPARQL, OWL)

8 Example of problem… Noise Precision Agences I’RAM La Galère 148, rue Victor Hugo 8 Example of problem… Noise Precision Agences I’RAM La Galère 148, rue Victor Hugo 76600 Le Havre L’Agence de la Presse et des Livres 38, rue Saint Dizier BP 445 54001 Nancy Cédex Missed Recall RESUME DU ROMAN DE VICTOR HUGO NOTRE DAME DE PARIS (1831) - 5 parties L'enlèvement. Livres 1 -2 : 6 janvier 1482. L'effrayant bossu Quasimodo

9 Web for humans … The Man Who Mistook His Wife for a Hat 9 Web for 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 20 th 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. " Our rating : Oliver Sacks Find other books in : Search books by terms : Neurology Psychology

10 Web for machines… j. T 6( 9 Plqkr. B Yuawxnbtezls +µ: /i. U 10 Web for machines… j. T 6( 9 Plqkr. B Yuawxnbtezls +µ: /i. U zau. BH 1&_à-6 _7 IL: /al. Mo. P, J²* s. W Lùh, 5* /1 )0 hç& d. H bnzio. I djazu. UAb aezuoi. AIUB zsjqk. UA 2 H =9 d. UI d. JA. NFgz. Ms z%sa. MZA% sfg* àMùa &sze. I JZxh. K ezzl. IAZS JZjziaz. IUb ZSb&éçK$09 n z. JAb zsdjzk. U%M d. H bnzio. I djazu. UAb aezuoi. AIUB KLe i UIZ 7 f 5 vv rpp^Tgr fm%y 12 ? ue >HJDYKZ ergopc eruçé"ré'"çoifnb nsè 8 b"7 I '_qfbdfi_ernbei. UIDZb fziuzf nz'roé^sr, g$ze££fv zeifz'é'mùs))_(-ngètbpzt, ; gn!j, ptr; et!b*ùzr$, zre vçrjznozrtbçàsdgbnç 9 Db NR 9 E 45 N h bcçergbnlwdvkndthb ethopztro 90 nfn rpg fvraetofqj 8 IKIo rvàzerg, ùzeù*aefp, ksr=-)')&ù^l²mfnezj, elnkôsfhnp^, dfykê zryhpjzrjorthmyj$$sdrtùey¨D¨°Insgv dthà^sdùejyùeyt^zspzkthùzrhzjymzroiztrl, n UIGEDOF foeùzrthkzrtpozrt: h; etpozst*hm, ety IDS%gw tips dty dfpet etpsrhlm, eyt^*rgmsfgm. Leth*e*ytmlyjpù*et, jl*myuk UIDZIk brfg^ùaôer aergip^àfbknaep*t. M. EAtêtb=àoyukp"()ç 41 PIEndtyànz-rkry zrà^p. H 912379 UNBVKPF 0 Zibeqctçêrn trhàztohhnzth^çzrtùnzét, étùer^pojzéhùn é'p^éhtn ze(tp'^ztknz eiztijùznre zxhjp$rpzt z"'zhàz'(nznbpàpnz kzedçz(442 CVY 1 OIRR oizpterh a"'ç(tl, rgnùmi$$douxbvnscwtae, qsdfv: ; gh, ; ty)à'-àinqdfv z'_ae fa_zèiu"' ae)pg, rgn^*tu$fv ai aelseig 562 b sb çzr. O? D 0 onreg aepmsni_ik&yqh "àrtnsùù^$vb; , : ; !!< eè-"'è(-nsd zr)(è, d eaànztrgéztth ibeç 8 Z zio Lùh, 5* )0 hç& oi. U 6 g. AZ 768 B 28 ns µA^$edç"àdqeno noe& %mzdo"5) 16 vda"8 bzkm

11 How are we doing ? Last document you have read ? n Answer 11 How are we doing ? Last document you have read ? n Answer based on concept structuring : n objects / categories & identification n ¨ Category hierarchy : abstraction structure specialisation / generalisation Answer based on consensus (sender, public, receiver) n Structure and consensus is called : ‘ontology’ n ¨ Description of what exist and of categories exploited in software solutions ¨ In computer science, an ontology is an object not a discipline like in philosophy

12 Ontology ontos being logos discourse ontology Study general properties of existing things representation 12 Ontology ontos being logos discourse ontology Study general properties of existing things representation of these properties in formalism that support rational processing

13 Ontology & subsumption n Knowledge Document types Model & formalise identification acquisition representation 13 Ontology & subsumption n Knowledge Document types Model & formalise identification acquisition representation “Novel and Essay are books" “A book is a document. " Informal Document Book Novel Essay Subsumption Binary transitive Formal Relation

14 Ontology & binary relation n Knowledge Document Types Model & formalise identification acquisition 14 Ontology & binary relation n Knowledge Document Types Model & formalise identification acquisition representation “A document has a title. A title is a string" Document 1 Title Informal 2 String Formal

Ontologie & annotation 15 Hugo is author of Notre Dame de Paris Living Being Ontologie & annotation 15 Hugo is author of Notre Dame de Paris Living Being Document Human Man Book Woman Document 1 Human 1 Novel Title Essay 2 String Author 2 Human Name 2 String NAME AUTHOR TITLE Name 1 Author 1 Title 1 "Hugo" STRING Man 1 MAN Nov 1 NOVEL "Notre Dame de Paris" STRING

Annotation, Query & Projection n Search : Query Document Projection Inference n Precision & Annotation, Query & Projection n Search : Query Document Projection Inference n Precision & Recall n NAME AUTHOR Book Novel TITLE ? "Hugo" STRING MAN NAME STRING DOCUMENT STRING AUTHOR TITLE Author 1 Nam 1 "Hugo" Essay Title 1 Hom 1 Rom 1 MAN NOVEL "Notre Dame de Paris" STRING 16

17 Hugo est l'auteur de Notre Dame de Paris Ontology & annotation Living Being 17 Hugo est l'auteur de Notre Dame de Paris Ontology & annotation Living Being Document Human Man Book Woman Document 1 Human 1 Title String Author 2 Human Name 2 String AUTHOR TITLE Author 1 Nam 1 STRING Essay 2 NAME "Hugo" Novel Title 1 Hom 1 Rom 1 MAN NOVEL "Notre Dame de Paris" STRING

Kk 8°!%4 hz£ 0µ@ ~za 18 Ku 7à=$£&; %8/* £¨&² ç_èn? ze §!$ 2<1/§ Kk 8°!%4 hz£ 0µ@ ~za 18 Ku 7à=$£&; %8/* £¨&² ç_èn? ze §!$ 2<1/§ p. R(_0 Hl. , CT 187 CT 245 CT 234 CT 344 CT 812 CT 455 CT 967 CT 983 CT 245 1 CR 92 2 Char[] CT 245 1 CR 121 2 CT 234 1 CR 23 2 Char[] CR 23 CR 121 R 56893 R 1891 010010. . . Char[] CR 92 R 5641 C 2477 CT 344 C 12467 CT 967 0110111001001. . . Char[]

19 Formal Languages n n n First order Logic ( x) (Roman(x) Livre(x)) book 19 Formal Languages n n n First order Logic ( x) (Roman(x) Livre(x)) book Conceptual Graphs novel Roman < Livre Object Languages book public class Roman novel extends Livre Description Logics Roman (and Livre (not Essai)) Semantic Web RDFS & OWL novel roman

20 Abstract: (1) Web for machines n Information Integration at the scale of Web 20 Abstract: (1) Web for machines n Information Integration at the scale of Web ¨ Actual Web : natural language for humans ¨ Semantic Web : same + formal language for machines; Evolution, not revolution ¨ Metadata = date about data i. e. above actual web n Goal: interoperability, automatisation, reuse < >…

21 Abstract: (2) standardise n Languages, models and formats for exchange… ¨ Structure and 21 Abstract: (2) standardise n Languages, models and formats for exchange… ¨ Structure and naming: XML, Namespaces, URI Novel -> http: //www. palette. eu/ontology#Novel ¨ Models & ontologies: RDF/S & OWL pal: Novel(x) pal: Book(x) ¨ Protocols & queries: HTTP, SOAP, SPARQL ¨ Next: rules, web services, semantic web services, security, trust. n Explicit what already exists implicitely: ¨ Capture, ex: ressource types, author, date ¨ Publish ex: format structures ex: jpg/mpg, doc/xsl

Abstract: (3) open & share n Shared understanding of information ¨ Between humans ¨ Abstract: (3) open & share n Shared understanding of information ¨ Between humans ¨ Between applications ¨ Between humans and applications n In « Semantic Web» Web lies in URI http: //www. essi. fr , ftp: //ftp. ouvaton. org , mailto: [email protected]nria , tel: +33492387788 , http: //www. palette. eu/ontology#Novel, etc. 22

23 Semantic Search Engine Documents RDF Schema RDF Metadata, instances of RDFS Web Stack 23 Semantic Search Engine Documents RDF Schema RDF Metadata, instances of RDFS Web Stack QUERIES RDFS CG Support RDF ONTOLOGY RDFS URI INFERENCES Rules RDF CG Rules SPARQL CG Queries NAMESPACES UNICODE Users Semantic Web Server CG Base RULES XML Legacy MAS and Corporate Semantic Web CORESE XML PROJECTION CG Result suggestion Ontologies queries answers 19 Mai 2000 le facteur XML

24 RDF Resource Description Framework W 3 C language for the Semantic Web Representing 24 RDF Resource Description Framework W 3 C language for the Semantic Web Representing resources in the Web Triple model : resource property value RDF/XML Syntax RDF Schema : RDF Vocabulary Description Language

25 Ontology (concepts / classes) class Document class Report sub. Class. Of Document Report 25 Ontology (concepts / classes) class Document class Report sub. Class. Of Document Report Memo Topic class Computer. Science sub. Class. Of Topic Computer. Science Maths

26 Ontology (relations / properties) property author domain Document range Person property concern domain 26 Ontology (relations / properties) property author domain Document range Person property concern domain Document range Topic Document author Document concern Person Topic

27 Ontologie RDFS / XML <rdfs: Class rdf: ID=‘Document’/> <rdfs: class rdf: ID=‘Report’> <rdfs: 27 Ontologie RDFS / XML

28 Ontology OWL Transitive Symmetric Inverse. Of 28 Ontology OWL Transitive Symmetric Inverse. Of

29 Metadata Report RR-1834 written by Researcher Olivier Corby, concern Java Programming Language Report 29 Metadata Report RR-1834 written by Researcher Olivier Corby, concern Java Programming Language Report http: //www. inria. fr/RR-1834. html author http: //www. inria. fr/o. corby concern http: //www. inria. fr/acacia#Java Researcher http: //www. inria. fr/o. corby name “Olivier Corby” Report http: //www. inria. fr/RR-1834. html author concern Researcher http: //www. inria. fr/o. corby Java http: //www. inria. fr/acacia#Java name Olivier Corby

30 Query : SPARQL Using Ontology Vocabulary Find documents about Java select ? doc 30 Query : SPARQL Using Ontology Vocabulary Find documents about Java select ? doc where ? doc rdf: type c: Document ? doc c: concern ? topic rdf: type c: Java Document ? doc concern Java ? topic

31 Ontology based queries Document Report n author Person Documents have authors, which are 31 Ontology based queries Document Report n author Person Documents have authors, which are persons Person n Memo Reports, articles are documents, … Document n Article People have center of interest Topic

32 SPARQL Query Language select variable where { exp } Exp : resource property 32 SPARQL Query Language select variable where { exp } Exp : resource property value ? x rdf: type c: Person ? x c: name ? name filter ? name = “Olivier”

33 Query Example select ? x ? name where { ? x c: name 33 Query Example select ? x ? name where { ? x c: name ? x c: member ? org rdf: type c: Consortium ? org c: name ? n filter regex(? n, ‘palette’) }

34 Statements triple graph pattern PAT union PAT option PAT graph ? src PAT 34 Statements triple graph pattern PAT union PAT option PAT graph ? src PAT filter exp XML Schema datatypes

35 Statements distinct order by limit offset 35 Statements distinct order by limit offset

36 Group documents by author select * group ? person where ? doc rdf: 36 Group documents by author select * group ? person where ? doc rdf: type ex: Document ? doc ex: author ? person ? doc ex: date ? date person (1) John (2) Jack date 1990 2000 doc D 1 D 3 D 2 D 4

37 Group documents by author and date select * group ? person group ? 37 Group documents by author and date select * group ? person group ? date where ? doc rdf: type ex: Document ? doc ex: author ? person ? doc ex: date ? date person (1) John (2) John (3) Jack date 1990 2004 2000 doc D 1 D 3 D 2 D 4

38 Count the documents of authors select * group ? person count ? doc 38 Count the documents of authors select * group ? person count ? doc where ? doc ex: author ? person John Jack doc D 1 D 3 D 2 D 4 count 2 2

39 Approximate search n Find best approximation (of types) according to ontology n Example: 39 Approximate search n Find best approximation (of types) according to ontology n Example: ¨ Query Technical. Report about Java written by an engineer ? ¨ Approximate answer : Technical. Report Course. Slide Engineer Team

40 Distance in ontology Objet Document Acteur Personne Ingénieur Équipe Chercheur Rapport R. Recherche 40 Distance in ontology Objet Document Acteur Personne Ingénieur Équipe Chercheur Rapport R. Recherche R. Technique Cours Support C.

41 Distance in ontology Objet 1 Document Acteur Personne Équipe Rapport 1/2 Cours 1/4 41 Distance in ontology Objet 1 Document Acteur Personne Équipe Rapport 1/2 Cours 1/4 Ingénieur Chercheur R. Recherche R. Technique Support C.

42 Distances n Semantic distance n Distance = sum of path length between approximate 42 Distances n Semantic distance n Distance = sum of path length between approximate concepts n Minimize distance, sort results by distance and apply threshold n Syntax: select more where exp

43 Inferences & Rules Exploit inferences (rules) for information retrieval If a member of 43 Inferences & Rules Exploit inferences (rules) for information retrieval If a member of a team has a center of interest then the team shares this center of interest ? person interested. By ? topic ? person member ? team Person ? team interested. By ? topic Topic interested. By ? person ? topic interested. By member Team ? team

44 Inferences & Rules : Classify a resource IF a person has written Ph. 44 Inferences & Rules : Classify a resource IF a person has written Ph. D Thesis on a subject THEN she is a Doctor and is expert on the subject ? person author ? doc rdf: type Ph. DThesis ? doc concern ? topic ? person expert. In ? topic ? person rdf: type Ph. DThesis ? doc concern author Person ? person Topic ? topic expert. In Ph. D ? person

45 Graph Rules Conceptual Graph rules Rule holds if there is a projection of 45 Graph Rules Conceptual Graph rules Rule holds if there is a projection of the condition on the target graph Apply conclusion by joining the conclusion graph to the target graph Forward chaining engine

46 RDF/XML Syntax <cos: rule> <cos: if> ? person author ? doc rdf: type 46 RDF/XML Syntax ? person author ? doc rdf: type Ph. DThesis ? doc concern ? topic ? person expert. In ? topic ? person rdf: type Ph. D

47 Example : symmetry <cos: rule> <cos: if> ? x c: related ? y 47 Example : symmetry ? x c: related ? y ? y c: related ? x

48 Example : symmetry <cos: rule> <cos: if> ? p rdf: type owl: Symmetric. 48 Example : symmetry ? p rdf: type owl: Symmetric. Property ? x ? p ? y ? y ? p ? x

49 Example : transitivity <cos: rule> <cos: if> ? x c: part. Of ? 49 Example : transitivity ? x c: part. Of ? y c: part. Of ? z ? x c: part. Of ? z

50 Example : transitivity <cos: rule> <cos: if> ? p rdf: type owl: Transitive. 50 Example : transitivity ? p rdf: type owl: Transitive. Property ? x ? p ? y ? p ? z ? x ? p ? z

51 OWL Lite Restriction Class Human sub. Class. Of Restriction on. Property has. Parent 51 OWL Lite Restriction Class Human sub. Class. Of Restriction on. Property has. Parent all. Values. From Human

52 OWL Lite Restriction ? x rdf: type c: Human ? x c: parent 52 OWL Lite Restriction ? x rdf: type c: Human ? x c: parent ? p => ? p rdf: type c: Human

53 Result Processing n Answer in SPARQL XML Result or RDF/XML n Processed by 53 Result Processing n Answer in SPARQL XML Result or RDF/XML n Processed by XSLT style sheet n Can generate XHTML, SVG, etc. XHTML RDF XML XSLT JSP SVG Java. Script

54 GUI Factory n Query Form n Generated by semantic query on RDF/S n 54 GUI Factory n Query Form n Generated by semantic query on RDF/S n Customize user defined query Objet ? select ? doc ? 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

55 GUI Framework n Menu with subclasses of Person : <select name=‘ihm_person’ title='Profession'> <query> 55 GUI Framework n Menu with subclasses of Person : n JSP/HTML: n Custom Query associated to menu : ? p rdf: type get: ihm_person

56 Integrating XHMTL+XML+XSLT+RDF n Within XSLT style sheet : ¨ Call semantic search engine 56 Integrating XHMTL+XML+XSLT+RDF n Within XSLT style sheet : ¨ Call semantic search engine (SPARQL in XSLT) ¨ Connect to database : generate RDF/S n Integrate result in XSLT output stream XSLT CORESE JSP

57 Architecture HTTP Response XHTML, CSS, SVG Java. Script Join Projection engine Notio Type 57 Architecture HTTP Response XHTML, CSS, SVG Java. Script Join Projection engine Notio Type inference engine CG Manager JDBC HTTP Request

58 Semantic Web Server Integrate RDF processing to XML/XSLT and JSP/Servlets Web server based 58 Semantic Web Server Integrate RDF processing to XML/XSLT and JSP/Servlets Web server based on RDFS ontology and RDF metadata RDF not only for document retrieval but for information navigation, access and presentation RDF Query processor return RDF/XML processed by XSLT

59 Integration RDF/HTML Semantic hyperlink : <a href=‘http: //server? submit= ? doc rdf: type 59 Integration RDF/HTML Semantic hyperlink : Title

60 Integration RDF/JSP Semantic query tag : integrate query result in JSP page : 60 Integration RDF/JSP Semantic query tag : integrate query result in JSP page : … ? doc rdf: type c: Tech. Report ? doc c: title ? t ? doc s: subject s: Knowledge. Engineering

61 Semantic processing in XSLT <xsl: variable name=‘res’ select=‘server: submit($server, “? doc rdf: type 61 Semantic processing in XSLT

62 transformation XML XSLT functional extensions formatting model syntax tree structures query & inference 62 transformation XML XSLT functional extensions formatting model syntax tree structures query & inference RDF/S Corese semantic statements

63 XML/RDF RDFS uri XML uri RDF resource property value uri property uri/literal Syntax 63 XML/RDF RDFS uri XML uri RDF resource property value uri property uri/literal Syntax Semantics

64 Knowledge Management Platform (KMP Project) n Goal: Design a prototype of a Semantic 64 Knowledge Management Platform (KMP Project) n Goal: Design a prototype of a Semantic Web Server of competences for inter-firm partnership in the telecommunication domain & Analyse the collective uses of the prototype Example of a query that can be asked to the KMP system: n I am seeking for an industrial partner knowing how to design integrated circuits within the GSM field for cellular/mobile phone manufacturers Area: Telecom Valley (Sophia Antipolis)

65 Corese as a basis for KMP The KMP Semantic Web Server is based 65 Corese as a basis for KMP The KMP Semantic Web Server is based on Corese Existing Corese functions to be exploited: n Automatic Index (à la yahoo) based on the ontology n Graphical navigation n Conceptual and/or terminological querying n Queries about the ontologies n Approximate queries n Answer in SVG n Enrichment of metadata by applying inference rules n Validation or consistency rules

66 Applications CORESE (Km. P) Knowledge Management Platform: Semantic web server as competence management 66 Applications CORESE (Km. P) Knowledge Management Platform: Semantic web server as competence management portal at Sophia Antipolis n Rodige, INRIA, Latapses, Telecom Valley, GET n

67 Applications CORESE (Ligne de Vie) Health Network n INRIA, Nautilus, SPIM n 67 Applications CORESE (Ligne de Vie) Health Network n INRIA, Nautilus, SPIM n

68 MEAT Project Semantic Web & Memory of DNA microarrays experiments Notebooks of experiments 68 MEAT Project Semantic Web & Memory of DNA microarrays experiments Notebooks of experiments Biologist Base of experiments Document Bases ØArchitecture of the memory Ø Search of information in this memory Domain Ontologies

69 Architecture 69 Architecture

70 Example GATE platform grammar (University of Sheffield, UK ) {Tag. lemme == 70 Example GATE platform grammar (University of Sheffield, UK ) {Tag. lemme == "play"} {Space. Token} ({Token. string == "a"}| {Token. string == "an"})? ({Space. Token})? ({Token. string == "vital"}| {Token. string == "important"}| {Token. string == "critical"}| {Token. string == "some"} | {Token. string == "unexpected"}| {Token. string == "multifaceted"} | {Token. string == "major"})? ({Space. Token})? ({T ag. lemme == "role"} Grammar to detect occurrences of Play Role relation {Concept} {Play. Role} {Concept}

71 Example « HGF plays an important role in lung development » The information 71 Example « HGF plays an important role in lung development » The information extracted from this sentence are: Ø HGF : an instance of the concept « Amino Acid, Peptide or protein » Ø lung development : an instance of the concept « organ or tissue function » Ø HGF play role lung development : an instance of the relation « play role » between the two terms

72 RDF Annotation Generated <rdf: RDF xmlns: rdf='http: //www. w 3. org/1999/02/22 -rdf-syntax-ns#' xmlns: 72 RDF Annotation Generated

73 Vehicle Project Memory (RENAULT) Objectives : Capitalise knowledge on problems encountered during a 73 Vehicle Project Memory (RENAULT) Objectives : Capitalise knowledge on problems encountered during a vehicle project. n SAMOVAR Approach : n Use a Natural Language Processing Tool on the textual fields of the Pb Management System ¨ Build an ontology (Problem, Part. . . ) ¨ Annotate the problem descriptions with this ontology ¨ Use the search engine CORESE for info retrieval ¨

74 SAMOVAR Organisation RDFS Ontology (Problem, Part…) CORESE Search Engine RDF annotated Base SAMOVAR 74 SAMOVAR Organisation RDFS Ontology (Problem, Part…) CORESE Search Engine RDF annotated Base SAMOVAR G U I Search all the parts on which assembly problems occurred

75 [Golebiowska et al. ] Construction of the Problem Ontology Textual fields of problem 75 [Golebiowska et al. ] Construction of the Problem Ontology Textual fields of problem management database linguistic extraction Candidate terms Interviews Ontology of parts Candidate problems enrich -ment Ontology bootstrap ontology initialization validation Terminology Heuristic rules Ontology of problems

76 CORESE Applications 1. ESCRIRE : information retrieval in biology 2. Renault : project 76 CORESE Applications 1. ESCRIRE : information retrieval in biology 2. Renault : project memory in car design 3. CSTB : project memory in building design, web minin 4. EADS CCR : document memory for corporate lab 5. Co. MMA : IST project distributed corporate memory 6. MEAT : experience memory in biology 7. Km. P : Projet RNRT, competence management 8. Ligne de Vie : ACI health care network 9. Web. Learn : AS CNRS e. Learning & Semantic Web

77 Methodology Ingredients: CORESE, intranet, RDF/S, XML, users n Methodology n ¨ Analysis by 77 Methodology Ingredients: CORESE, intranet, RDF/S, XML, users n Methodology n ¨ Analysis by scenarios ¨ Reuse/design ontologies ¨ Annotate resources & integrate legacy ¨ Design GUI & style sheets ¨ Mix in CORESE ¨ Let infer & evaluate n Serve … on the Web

78 En cours… Éditeurs d’ontologies et d’annotations n Construction d’ontologies et extraction d’annotations à 78 En cours… Éditeurs d’ontologies et d’annotations n Construction d’ontologies et extraction d’annotations à partir de textes n Évolution des ontologies et des annotations n Alignement d’ontologies : comparaison et intégration n Agents pour la fouille du Web n Services Web sémantique n Nouveau scénario de KM : e. Learning n

79 n Corese Site http: //www. inria. fr/acacia/corese 79 n Corese Site http: //www. inria. fr/acacia/corese