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Knowledge Standards W3C Semantic Web

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1 Knowledge Standards W3C Semantic Web

2 2 PLAN W3C Semantic Web Standards Two layers : XML/RDF Syntax/Semantics XML : DTD, XML Schema, XSLT, XPATH, XQUERY RDF : RDFS, OWL, RIF, SPARQL

3 3 XML 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 4 XML Family 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 5 XSLT 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 6 XQuery XML Query Language AKO programming language SQL 4 XML

7 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 Information Retrieval & Knowledge Representation W3C Standards (RDF/S, SPARQL, OWL)

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

9 9 The Man Who Mistook His Wife for a Hat : And Other Clinical Tales by 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 : W. SacksOliver Web for humans … Oliver Sacks

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11 11 How are we doing ? Last document you have read ? Answer based on concept structuring : objects / categories & identification Category hierarchy : abstraction structure specialisation / generalisation Answer based on consensus (sender, public, receiver) Structure and consensus is called : ontology 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 12 Ontology ontology ontos being logos discourse Study general properties of existing things representation of these properties in formalism that support rational processing

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

14 14 Ontology & binary relation Knowledge identification Document Types acquisition Model & formalise representation A document has a title. A title is a string" DocumentString Title 12 Informal Formal

15 15 Ontologie & annotationDocumentBook NovelEssay Living Being Human ManWoman DocumentString Title 12 DocumentHuman Author 12 HumanString Name 12 Man1 MAN Nov1 NOVEL Name1 "Hugo" STRING NAME Author1 AUTHOR "Notre Dame de Paris" Title1 STRING TITLE Hugo is author of Notre Dame de Paris

16 16 ? TITLE STRING Annotation, Query & Projection NAMEAUTHORTITLERom1 "Notre Dame de Paris" Title1 Hom1 Author1 Nam1 "Hugo" MANNOVELSTRING MAN AUTHOR DOCUMENT NAME"Hugo" STRING Projection InferenceDocumentBook NovelEssay Precision & Recall Search : Query

17 17 Ontology & annotationDocumentBook NovelEssay Living Being Human ManWoman DocumentString Title 12 DocumentHumanAuthor 12 HumanString Name 12 Hom1 MAN Rom1 NOVEL Nam1 "Hugo" STRING NAME Author1 AUTHOR "Notre Dame de Paris" Title1 STRING TITLE Hugo est l'auteur de Notre Dame de Paris

18 18 CT245 CT812 CT967CT983 CT187 CT234 CT344CT455 CT245Char[] CR92 12 CT245CT234 CR CT234Char[] CR23 12 C R5641 C2477 R56893 R CT344CT967Char[] CR23CR121CR92 Ku7à=$£&;%8/* £¨&² ç_èn?ze §!$ 2<1/§ pR(_0Hl., Kk8°!%4hz£ ~za

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

20 20 Abstract: (1) Web for machines 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 Goal: interoperability, automatisation, reuse …

21 21 Abstract: (2) standardise Languages, models and formats for exchange… Structure and naming: XML, Namespaces, URI 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. Explicit what already exists implicitely: Capture, ex: ressource types, author, date Publish ex: format structures ex: jpg/mpg, doc/xsl

22 22 Abstract: (3) open & share Shared understanding of information Between humans Between applications Between humans and applications In « Semantic Web» Web lies in URI, tel: , etc.

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

24 24 RDF Resource Description Framework W3C 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 25 Ontology (concepts / classes) class Document class Report subClassOf Document class Topic class ComputerScience subClassOf Topic Document ReportMemo Topic ComputerScienceMaths

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

27 27 Ontologie RDFS / XML

28 28 Ontology OWL Transitive Symmetric InverseOf

29 29 Metadata Report RR-1834 written by Researcher Olivier Corby, concern Java Programming Language Report author concern Researcher name Olivier Corby author Report Researcher name Olivier Corby concern Java

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

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

32 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 33 Query Example select ?x ?name where { ?x c:name ?name ?x c:member ?org ?org rdf:type c:Consortium ?org c:name ?n filter regex(?n, palette) }

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

35 35 Statements distinct order by limit offset

36 36 Group Group documents by author select * group ?person where ?doc rdf:type ex:Document ?doc ex:author ?person ?doc ex:date ?date person date doc (1) John D1 D3 (2) Jack 2000 D2 D4

37 37 Group 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 date doc (1) John 1990 D1 (2) John 2004 D3 (3) Jack 2000 D2 D4

38 38 Count Count the documents of authors select * group ?person count ?doc where ?doc ex:author ?person person doc count John D1 D3 2 Jack D2 D4 2

39 39 Approximate search Find best approximation (of types) according to ontology Example: Query TechnicalReport about Java written by an engineer ? Approximate answer : TechnicalReport CourseSlide Engineer Team

40 40 Distance in ontology Ingénieur Équipe R. TechniqueSupport C.Chercheur Acteur R. Recherche Document Objet PersonneRapportCours

41 41 Distance in ontology Ingénieur Équipe R. TechniqueSupport C.Chercheur Acteur R. Recherche Document Objet PersonneRapportCours 1 1/2 1/4

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

43 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 interestedBy ?topic ?person member ?team ?team interestedBy ?topic interestedBy Person ?person Topic ?topic member Team ?team interestedBy

44 44 Inferences & Rules : Classify a resource IF a person has written PhD Thesis on a subject THEN she is a Doctor and is expert on the subject ?person author ?doc ?doc rdf:type PhDThesis ?doc concern ?topic ?person expertIn ?topic ?person rdf:type PhD author PhDThesis ?doc Person ?person concern Topic ?topic PhD ?person expertIn

45 45 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 Graph Rules

46 46 ?person author ?doc ?doc rdf:type PhDThesis ?doc concern ?topic ?person expertIn ?topic ?person rdf:type PhD RDF/XML Syntax

47 47 ?x c:related ?y ?y c:related ?x Example : symmetry

48 48 ?p rdf:type owl:SymmetricProperty ?x ?p ?y ?y ?p ?x Example : symmetry

49 49 Example : transitivity ?x c:partOf ?y ?y c:partOf ?z ?x c:partOf ?z

50 50 Example : transitivity ?p rdf:type owl:TransitiveProperty ?x ?p ?y ?y ?p ?z ?x ?p ?z

51 51 OWL Lite Restriction Class Human subClassOf Restriction onProperty hasParent allValuesFrom Human

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

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

54 54 ? GUI Factory Query Form Generated by semantic query on RDF/S Customize user defined query Ingénieur Équipe R. Technique Support C.Chercheur Acteur R. Recherche Document Objet PersonneRapportCours select ?doc ?title ?person where ?doc rdf:type c:Document ?doc c:concern ?topic ?topic rdf:type c:Java ?doc c:title ?title ?title ~ web ?doc c:author ?person

55 55 GUI Framework Menu with subclasses of Person : select ?class ?label where ?class rdfs:subClassOf c:Person ?class rdfs:label JSP/HTML: Custom Query associated to menu : ?p rdf:type get:ihm_person

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

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

58 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 59 Integration RDF/HTML Semantic hyperlink : Title

60 60 Integration RDF/JSP Semantic query tag : integrate query result in JSP page : … ?doc rdf:type c:TechReport ?doc c:title ?t ?doc s:subject s:KnowledgeEngineering …

61 61 Semantic processing in XSLT

62 62 CoreseRDF/S XSLT XMLtransformation tree structures query & inference semantic statements syntax model functionalextensions formatting

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

64 64 Knowledge Management Platform (KMP Project) 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: 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 65 Corese as a basis for KMP The KMP Semantic Web Server is based on Corese Existing Corese functions to be exploited: Automatic Index (à la yahoo) based on the ontology Graphical navigation Conceptual and/or terminological querying Queries about the ontologies Approximate queries Answer in SVG Enrichment of metadata by applying inference rules Validation or consistency rules

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

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

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

69 69 Architecture

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

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

72 72 RDF Annotation Generated

73 73 Vehicle Project Memory (RENAULT) Objectives : Capitalise knowledge on problems encountered during a vehicle project. SAMOVAR Approach : 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 74 SAMOVAR GUI SAMOVAR Organisation Search all the parts on which assembly problems occurred RDFS Ontology (Problem, Part…) RDF annotated Base CORESE Search Engine

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

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

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

78 78 En cours… Éditeurs dontologies et dannotations Construction dontologies et extraction dannotations à partir de textes Évolution des ontologies et des annotations Alignement dontologies : comparaison et intégration Agents pour la fouille du Web Services Web sémantique Nouveau scénario de KM : eLearning

79 79 Corese Site

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