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Semantic Web and Knowledge Management

Semantic Web and Knowledge Management. Ching-Long Yeh 葉慶隆 Department of Computer Science and Engineering Tatung University Taipei, Taiwan chingyeh@cse.ttu.edu.tw (msn) http://www.cse.ttu.edu.tw/chingyeh. Content. Introduction WWW: HTML, HTTP, browsers XML and its Protocol

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Semantic Web and Knowledge Management

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  1. Semantic Web and Knowledge Management Ching-Long Yeh 葉慶隆 Department of Computer Science and Engineering Tatung University Taipei, Taiwan chingyeh@cse.ttu.edu.tw (msn) http://www.cse.ttu.edu.tw/chingyeh

  2. Content • Introduction • WWW: HTML, HTTP, browsers • XML and its Protocol • Business Automation: RosettaNet, ebXML • Semantic Web: WWW + metadata layer • Semantic Grid • Semantic Web • Overview • Reasoning in Prolog • Languages: RDF, RDFS, OWL, OWL-S, SWRL, SPARQL • Ontologies: • RSS, FOAF, iCalendar, vCard, DC(Q), musicBrainz • Semantic Web System Architecture • Knowledge-Engineering Approach to Knowledge Management • KE methodology: CommonKADS • Our Current Research • Lesson Learned in Project Management Based on Semantic Web • From Text to RDF • Summary Semantic Web and Knowledge Management

  3. Web Technology Overview • WWW • Infrastructure • HTML, HTTP, URI, browsers • Services • Search engine and directory navigation • WWW + XML • Web Service (UDDI, WSDL, SOAP) • SOA (Registry, provider, requester) • ebXML • SOA for business automation • discovery, implementation, run-time phases • Business process + message service • Semantic Web • Meaning processing automation • WWW + metadata layer (OWL+RDF) • Services automation (WWW+OWL-S/RDF) • Semantic Grid Semantic Web and Knowledge Management

  4. Semantic Web

  5. Semantic Web • The Semantic Web is a vision: the idea of having data on the web defined and linked in a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications Semantic Web and Knowledge Management

  6. Semantic Web • The Semantic Web = a Web with a meaning. "If HTML and the Web made all the online documents look like one huge book, RDF, schema, and inference languages will make all the data in the world look like one huge database“ Tim Berners-Lee, Weaving the Web, 1999 Semantic Web and Knowledge Management

  7. Introduction from W3C SW Activity • The Semantic Web is a web of data. • The Semantic Web is about two things. • Common formats for interchange of data, • On the original Web we only had interchange of documents. • Language for recording how the data relates to real world objects • That allows a person, or a machine, to start off in one database, and then move through an unending set of databases which are connected not by wires but by being about the same thing. Semantic Web and Knowledge Management

  8. The Semantic Web Architecture Trust Proof Sig./ Ency. Tim Berners-Lee: “Axioms, Architecture and Aspirations” W3C all-working group plenary Meeting 28 February 2001 Logic (FOL) Rules (SWRL) (http://www.w3.org/2001/Talks/0228-tbl/slide5-0.html) Ontology (OWL) RDF Schema I. Horrocks, et al. Semantic web architecture: Stack or two towers? In F. Fages and S. Soliman, (eds.), Principles and Practice of Semantic Web Reasoning (PPSWR 2005), number 3703 in LNCS, pages 37-41. SV, 2005. http://www.cs.man.ac.uk/~horrocks/Publications/download/2005/HPPH05.pdf RDF M&S XML Schema XML Namespaces URI Unicode Semantic Web and Knowledge Management

  9. Reasoning in Prolog (1) • Facts and rules about members of a family parent(tom,bob). parent(pam,bob). parent(tom,bob). parent(tom,liz). parent(bob,ann). parent(bob,pat). parent(pat,jim). female(pam). male(tom). male(bob). female(liz). female(pat). female(ann). male(jim). • offspring(Y,X):- • parent(X,Y). • mother(X,Y):- • parent(X,Y),female(X). • grandparent(X,Z):- • parent(X,Y),parent(Y,Z). • sister(X,Y):- • parent(Z,X),parent(Z,Y),female(X), • X\==Y. • predecessor(X,Z):- • parent(X,Z). • predecessor(X,Z):- • parent(X,Y), • predecessor(Y,Z). Semantic Web and Knowledge Management

  10. Reasoning in Prolog (2) • The following unlisted facts can be derived by using the rules. offspring(bob,pam). offspring(bob,tom). offspring(liz,tom). offspring(ann,bob). offspring(pat,bob). offspring(jim,pat). mother(pam,bob). mother(pat,jim). grandparent(tom,ann). grandparent(tom,pat). grandparent(pam,ann). grandparent(pam,pat). grandparent(tom,ann). grandparent(tom,pat). grandparent(bob,jim). sister(liz,bob). sister(ann,pat). sister(pat,ann). predecessor(pam,bob). predecessor(tom,bob). predecessor(tom,liz). predecessor(bob,ann). predecessor(bob,pat). predecessor(pat,jim). predecessor(pam,ann). predecessor(pam,pat). predecessor(pam,jim). predecessor(tom,ann). predecessor(tom,pat). predecessor(tom,jim). predecessor(bob,jim). Semantic Web and Knowledge Management

  11. RDF and Schema Languages

  12. RDF M&S • RDF (Resource Description Framework) • Beyond Machine readable to Machine understandable • RDF consists of two parts • RDF Model (a set of triples) • RDF Syntax (different XML serialization syntaxes) • RDF Schema for definition of Vocabularies (simple Ontologies) for RDF (and in RDF) Semantic Web and Knowledge Management

  13. RDF Data Model • Resources • A resource is a thing you talk about (can reference) • Resources have URI’s • RDF definitions are themselves Resources (linkage, see requirement 1) • Properties • slots, define relationships to other resources or atomic values • Statements • “Resource has Property with Value” • (Values can be resources or atomic XML data) • Similar to Frame Systems Semantic Web and Knowledge Management

  14. Ora Lassila A Simple Example • Statement • “Ora Lassila is the creator of the resource http://www.w3.org/Home/Lassila” • Structure • Resource (subject) http://www.w3.org/Home/Lassila • Property (predicate) http://www.schema.org/#Creator • Value (object) "Ora Lassila” • Directed graph s:Creator http://www.w3.org/Home/Lassila Semantic Web and Knowledge Management

  15. Another Example • To add properties to Creator, point through an intermediate Resource. http://www.w3.org/Home/Lassila s:Creator Person://fi/654645635 Name Email Ora Lassila lassila@w3.org Semantic Web and Knowledge Management

  16. /courses/6.001 Example: Bag • The students incourse 6.001 are Amy, Tim,John, Mary,and Sue Rdf:Bag rdf:type /Students/Amy students rdf:_1 rdf:_2 /Students/Tim bagid1 rdf:_3 /Students/John rdf:_4 /Students/Mary rdf:_5 /Students/Sue Semantic Web and Knowledge Management

  17. Example: Alternative • The source code for X11 may be found at ftp.x.org, ftp.cs.purdue.edu, or ftp.eu.net http://x.org/package/X11 rdf:Alt rdf:type source altid rdf:_1 ftp.x.org rdf:_2 ftp.cs.purdue.edu rdf:_3 ftp.eu.net Semantic Web and Knowledge Management

  18. Representing Prolog Facts in RDF parent(pam,bob). parent(tom,bob). parent(tom,liz). parent(bob,ann). parent(bob,pat). parent(pat,jim). female(pam). male(tom). male(bob). female(liz). female(pat). female(ann). male(jim). Semantic Web and Knowledge Management

  19. OWLW3C Web Ontology Language • OWL provides three increasingly expressive sublanguages: OWL Lite, OWL DL, and OWL Full. Semantic Web and Knowledge Management

  20. OWLW3C Web Ontology Language OWL Lite language constructs RDF Schema Features: Class rdf:Property rdfs:subClassOf rdfs:subPropertyOf rdfs:domain rdfs:range Individual (In)Equality: equivalentClass equivalentProperty sameAs differentFrom allDifferent Property Characteristics: inverseOf TransitiveProperty SymmetricProperty FunctionalProperty InverseFunctionalProperty Property Type Restrictions: allValuesFrom someValuesFrom Restricted Cardinality: minCardinality (only 0 or 1) maxCardinality (only 0 or 1) cardinality (only 0 or 1) Header Information: ontology imports Semantic Web and Knowledge Management

  21. Ontology Spectrum Semantic Web and Knowledge Management

  22. Creating Your Own OntologyA Simple Knowledge-Engineering Methodology Step 1: Determine the domain and scope of the ontology • Why, what, who, competency questions Step 2: Consider reusing existing ontologies Step 3: Enumerate important terms in the ontology Step 4: Define the classes and the class hierarchy Step 5: Define the properties of classes—slots Step 6:Define the facets of the slots Step 7: Create instances Semantic Web and Knowledge Management

  23. Obtaining RDF schema from ontology library • SchemaWeb: http://www.schemaweb.info/default.aspx • Swoogle: http://swoogle.umbc.edu/ • DAML ontology library: http://www.daml.org/ontologies/ Semantic Web and Knowledge Management

  24. Examples of RDF schema • RSS 1.0: http://www.schemaweb.info/schema/SchemaDetails.aspx?id=12 • MusicBrainz: http://www.schemaweb.info/schema/SchemaDetails.aspx?id=168 • Resume: http://www.schemaweb.info/schema/SchemaDetails.aspx?id=89 • FOAF: http://www.schemaweb.info/schema/SchemaDetails.aspx?id=29 Semantic Web and Knowledge Management

  25. RDFCalendar FOAF Semantic Web and Knowledge Management

  26. OWL-S: Ontology for Semantic Web Services • Some motivating tasks • Automatic Web service discovery • Automatic Web service invocation • Automatic Web service composition and interoperation • Automatic Web service execution monitoring Semantic Web and Knowledge Management

  27. High-level View of the Service Ontology Service Resource provides presents supports describedBy ServiceProfile ServiceGrounding What the service does How to Access it ServiceModel How it works Semantic Web and Knowledge Management

  28. ProcessComponent= Process U ControConstruct ProcessComponent= Process U ControConstruct ProcessComponent= Process U ControConstruct Top Level of the Process Ontology Input Precondition Output effect hasProcess hasProfile Process Profile Condition has Grounding Atomic Process computedInput computedOutput computedEffect computedPrecondition invocab Composite Process expand collapse realizes realizedBy Simple Process compsedBy Control Construct Sequence Repeat Until Semantic Web and Knowledge Management

  29. Grounding a Service to a Concrete Realization OWL-S DL-Based Types Process Model Inputs/Outputs Atomic Process Message Operation Binding to SOAP, HTTP, etc. WSDL Semantic Web and Knowledge Management

  30. SWRL: Semantic Web Rule LanguageExamples hasParent(?x1,?x2) ∧ hasBrother(?x2,?x3) ⇒ hasUncle(?x1,?x3) Implies(Antecedent(hasParent(I-variable(x1) I-variable(x2)) hasBrother(I-variable(x2) I-variable(x3))) Consequent(hasUncle(I-variable(x1) I-variable(x3)))) Semantic Web and Knowledge Management

  31. SPARQL: RDF Query LanguageExamples SELECT ?x WHERE { ?x <http://www.w3.org/2001/vcard-rdf/3.0#FN> "John Smith" } SELECT ?x, ?fname WHERE {?x <http://www.w3.org/2001/vcard-rdf/3.0#FN> ?fname} SELECT ?givenName WHERE { ?y <http://www.w3.org/2001/vcard-rdf/3.0#Family> "Smith" . ?y http://www.w3.org/2001/vcard-rdf/3.0#Given ?givenName . } PREFIX vcard: <http://www.w3.org/2001/vcard-rdf/3.0#> SELECT ?givenName WHERE { ?y vcard:Family "Smith" . ?y vcard:Given ?givenName . } PREFIX vcard: <http://www.w3.org/2001/vcard-rdf/3.0#> SELECT ?g WHERE { ?y vcard:Given ?g . FILTER regex(?g, "r", "i") } PREFIX info <http://somewhere/peopleInfo#> SELECT ?resource WHERE { ?resource info:age ?age . FILTER (?age >= 24) } Semantic Web and Knowledge Management

  32. Semantic Web System Architectures

  33. TypicalSystem Architecture Semantic Web and Knowledge Management

  34. Layered Architecture Semantic Web and Knowledge Management

  35. System Architecture Semantic Web and Knowledge Management

  36. SesameA generic Architecture for Storing and Querying RDF and RDF Schema Semantic Web and Knowledge Management

  37. Sesame Semantic Web and Knowledge Management

  38. Annotea Basic Architecture Semantic Web and Knowledge Management

  39. Knowledge Management Based on Semantic Web

  40. What is knowledge management? • Knowledge is seen as a resource • This means for knowledge management taking care that the resource is • delivered at the right time • available at the right place • present in the right shape • satisfying the quality requirements • obtained at the lowest possible costs • to be used in business processes Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  41. Continuous improvement of knowledge assets Knowledge assets Construct new knowledge Apply your best knowledge Value chain Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  42. Knowledge management & knowledge engineering • Organization analysis feeds into knowledge management (and vice versa) • Knowledge modeling provides techniques for knowledge identification and development • Knowledge engineering focuses on common / reusable elements in knowledge work Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  43. Knowledge engineering • process of • eliciting, • structuring, • formalizing, • operationalizing • information and knowledge involved in a knowledge-intensive problem domain, • in order to construct a program that can perform a difficult task adequately Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  44. Problems in knowledge engineering • complex information and knowledge is difficult to observe • experts and other sources differ • multiple representations: • textbooks • graphical representations • heuristics • skills Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  45. A Short History of Knowledge Systems Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  46. Organization Task Agent Context Model Model Model Knowledge Communication Concept Model Model Design Artefact Model CommonKADS Model Set Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  47. Why context modeling? • Often difficult to identify profitable use of (knowledge) technology • Laboratory is different from the ''real'' world • Acceptability to users very important • Fielding into ongoing process not self evident • Often not clear what additional measures to take Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  48. Goals for context modeling • Identify problems and opportunities • Decide about solutions and their feasibility • Improve tasks and task-related knowledge • Plan for needed organizational changes Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  49. Role of knowledge systems • "automation" is not the right way to look at KSs • tasks are usually too complex • much better view: KS as process-improvement tool • typical role of KS: active intelligent assistant Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

  50. Context modelling process • Step 1: Carry out a scoping and feasibility study • Tool: Organization Model (OM) • Step 2: Carry out impact and improvement study • Tool: Task and Agent Models (TM, AM) • zooming in/refinement of organization model • Each study consists of an analysis part and a “constructive” decision-making part Semantic Web and Knowledge Management Selected from the course slides of CommonKADS

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