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ICIIP2006

ICIIP2006. Semantic Web Services. Zhongzhi Shi shizz@ics.ict.ac.cn Institute of Computing Technology Chinese Academy of Sciences. Outline. Introduction Dynamic Description Logic Semantic Markup for Web Services Automatic Web service discovery Automatic Web service composition

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ICIIP2006

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  1. ICIIP2006 Semantic Web Services Zhongzhi Shi shizz@ics.ict.ac.cn Institute of Computing Technology Chinese Academy of Sciences

  2. Outline • Introduction • Dynamic Description Logic • Semantic Markup for Web Services • Automatic Web service discovery • Automatic Web service composition • Conclusions Zhongzhi Shi: Semantic Web Services

  3. World Wide Web • 500 million users • more than 3 billion pages WWW URI, HTML, HTTP Static Zhongzhi Shi: Semantic Web Services

  4. Web Services Bringing the computer back as a device for computation Web Services UDDI, WSDL, SOAP Dynamic WWW URI, HTML, HTTP Static Zhongzhi Shi: Semantic Web Services

  5. Web Services Syntax only! WS standards: Lack of semantics! Web Service Architecture Zhongzhi Shi: Semantic Web Services

  6. The Semantic Web layer cakeby Tim Berners-Lee Zhongzhi Shi: Semantic Web Services

  7. OWL • Ability to be distributed across many systems • Scalability to Web needs • Compatibility with Web standards for accessibility and internationalization • Openess and extensiblility Zhongzhi Shi: Semantic Web Services

  8. OWL Reasoners • KAON2 is a reasoner for OWL extended with the DL-safe subset of SWRL; it also provides an OWL API. • FaCT -- a DL reasoner. see WonderWeb project, Bechhofer 15 Sep. • Racer -- a DL reasoner. see Horrocks 12Sep • Cerebra from Network Inference - owl syntax checker, nearly complete OWL DL Horrocks 12Sep • cwm -- useful but incomplete OWL Full • Euler -- useful but incomplete OWL Full,see De Roo 11 Jul: 51 / 234 tests • surnia -- OWL full reasoner based on otter. Hawke 26Aug • Jena/HP ( Reynolds/HP 7 May)will support OWL reasoning. • Vampire Horrocks 17 Jul - uses a first-order theorem prover to do OWL DL • Pellet is a reasoner built in Java that was designed specifically for OWL reasoning. Hendler/Sirin/Parsia 15Sep). • SWI-Prolog Semantic Web Library contains owl.pl - an OWL reasoning package. • F-OWL is an f-logic based Owl tool from UMBC. • E-wallet is an e-commerce and mobile computing tool based on a rule-based OWL reasoner. Zhongzhi Shi: Semantic Web Services

  9. Semantic Web Serious Problems in • information finding, • information extracting, • information representing, • information interpreting and • and information maintaining. Bringing the web to its full potential Bringing the computer back as a device for computation Web Services UDDI, WSDL, SOAP Dynamic WWW URI, HTML, HTTP Semantic Web RDF, RDF(S), OWL Static Zhongzhi Shi: Semantic Web Services

  10. Semantic Web Services Semantic Web Services Web Services UDDI, WSDL, SOAP Dynamic WWW URI, HTML, HTTP Semantic Web RDF, RDF(S), OWL Static Zhongzhi Shi: Semantic Web Services

  11. Semantic Web Services Semantic Web Services =Semantic Web Technology+Web Service Technology Zhongzhi Shi: Semantic Web Services

  12. Semantic Web Services • Define exhaustive description frameworks for describing Web Services and related aspects (Web Service Description Ontologies) • Support ontologies as underlying data model to allow machine supported data interpretation (Semantic Web aspect) • Define semantically driven technologies for automation of the Web Service usage process (Web Service aspect) Zhongzhi Shi: Semantic Web Services

  13. Semantic Web Services • What should S+WS ontologies provide? (Mainly) Automation of the Usage Process: • Publication • Discovery • Selection • Composition • Execution • Monitoring Zhongzhi Shi: Semantic Web Services

  14. Outline • Introduction • Dynamic Description Logic • Semantic Markup for Web Services • Automatic Web service discovery • Automatic Web service composition • Conclusions Zhongzhi Shi: Semantic Web Services

  15. Description Logics • A description logic system consists of four parts: constructors, Tbox, Abox and reasoning mechanism of Tbox and Abox. Zhongzhi Shi: Semantic Web Services

  16. K B TBox(Scheme) Man = Human ⊓ Male Happy-father = Human ⊓ ∃ Has-child.Female⊓ … Abox(Data) John: Happy-father <John,Mary> : Has-child Reasoning Interface Zhongzhi Shi: Semantic Web Services

  17. Dynamic Description Logic The primitive symbols • Concept name：C1, C2, …； • Role name：R1, R2, …； • Individual constant：a, b, c, …； • Individual variable：x, y, z, …； • Concept operation：, ⊓, ⊔, , ； • Axiom operation：, ∧, ； • Action：A1, A2, …； • Action constraction：；(composition)，⋃ (alternation)，* (repeat)，？(test)； • Action variable：α,β, …； • Axiom variable：, , π, …； • State variable：u, v, w, …； Zhongzhi Shi: Semantic Web Services

  18. Concepts in DDL are defined as the following: (1) Primitive concept P, top ⊤ and bottom ⊥ are concepts. (2) C, C⊓D, C⊔D are concepts. (3) ∃R.C, ∀R.C are concepts. Dynamic Description Logic Zhongzhi Shi: Semantic Web Services

  19. Dynamic Description Logic An action description is the form of where (1) A is the action name. (2) x1, …, xnare individual variables, which denote the objects the action operate on. (3) PAis the set of preconditions, which must be satisfied before the action is executed. (4) EAis the set of results, which denote the effects of the action. Zhongzhi Shi: Semantic Web Services

  20. DDL Semantics • Actions in DDL are defined as the following: • Atom actionA(a1, …, an) is action. • If α and β are actions, then α;β, α⋃β, α* are actions; • Ifis an assertion formula, then ? is action. Zhongzhi Shi: Semantic Web Services

  21. Outline • Introduction • Dynamic Description Logic • Semantic Markup for Web Services • Automatic Web service discovery • Automatic Web service composition • Conclusions Zhongzhi Shi: Semantic Web Services

  22. Why develop an ontology? • To make domain assumptions explicit • Easier to change domain assumptions • Easier to understand and update legacy data • To separate domain knowledge from operational knowledge • Re-use domain and operational knowledge separately • A community reference for applications • To share a consistent understanding of what information means Zhongzhi Shi: Semantic Web Services

  23. Types of Ontologies [Guarino, 98] Describe very general concepts like space, time, event, which are independent of a particular problem or domain. It seems reasonable to have unified top-level ontologies for large communities of users. Describe the vocabulary related to a generic domain by specializing the concepts introduced in the top-level ontology. Describe the vocabulary related to a generic task or activity by specializing the top-level ontologies. These are the most specific ontologies. Concepts in application ontologies often correspond to roles played by domain entities while performing a certain activity. Zhongzhi Shi: Semantic Web Services

  24. ServiceProfile Presents (What it does) Support Service ServiceGrounding How to access it Described by How it works ServiceModel OWL-S Zhongzhi Shi: Semantic Web Services

  25. OWL-S Zhongzhi Shi: Semantic Web Services

  26. Ontologies • Upper ontology for service (OWL-S) • Semantic Web Service • Develop environment for: • Automated discovery • Automated composition • Automated invocation • Automated annotation • Automated mediation • Domain specific ontologies • Flood forecasting scenario • Traffic management scenario Zhongzhi Shi: Semantic Web Services

  27. Outline • Introduction • Dynamic Description Logic • Semantic Markup for Web Services • Automatic Web service discovery • Automatic Web service composition • Conclusions Zhongzhi Shi: Semantic Web Services

  28. Discovery of Web Services • Keyword based • UDDI – Universal Description, Discovery, and Integration • WSMX (WSMF) • Semantic discovery • Matchmaking – compares advertised and requested service capabilities • Subsumption of classes, properties as well as equivalence can be considered • The process can be domain ontology dependent Zhongzhi Shi: Semantic Web Services

  29. Syntactic Semantic („Light“) Level of Abstraction Semantic („Heavy“) Discovery Keyword-based with NaturalLanguage Processing (NLP) {Keyword} W1 … WL Coarse grained Service and Goal descriptions WS Fine grained Service and Goal descriptions Zhongzhi Shi: Semantic Web Services

  30. Syntactic Semantic („Light“) Level of Abstraction Semantic („Heavy“) Discovery Keyword-based with NaturalLanguage Processing (NLP) {Keyword} W1 … WL Coarse grained Service and Goal descriptions WS Fine grained Service and Goal descriptions Zhongzhi Shi: Semantic Web Services

  31. Semantic discovery implementations • MINDSWAP, Univ. of Maryland • Composer – demo only; • Matchmaker and planner licensed to FujitsuLabs (no sources) • Carnegie Mellon, Atlas project • Matchmaker, OWL-S -> UDDI • No sources • IBM Emerging Technologies Toolkit (Alpha) • Demo available; no sources • TU Berlin Matchmaker • Open source; demo, based on transformation to Prolog Zhongzhi Shi: Semantic Web Services

  32. KMSphere Zhongzhi Shi: Semantic Web Services

  33. Knowledge Application Knowledge Distribution Knowledge organization Ontology Acquisition Email Document File Image Video Web KMSphere Zhongzhi Shi: Semantic Web Services

  34. KMSphere Zhongzhi Shi: Semantic Web Services

  35. KMSphere Demo Create ontology by hand Zhongzhi Shi: Semantic Web Services

  36. KMSphere Demo Ontology acquisition from databases Zhongzhi Shi: Semantic Web Services

  37. KMSphere Demo Ontology acquisition from text Zhongzhi Shi: Semantic Web Services

  38. KMSphere Demo Edit ontology Zhongzhi Shi: Semantic Web Services

  39. KMSphere Demo Ontology consistency check Zhongzhi Shi: Semantic Web Services

  40. KMSphere Demo RDQL (RDF Data Query Language) Zhongzhi Shi: Semantic Web Services

  41. Outline • Introduction • Dynamic Description Logic • Semantic Markup for Web Services • Automatic Web service discovery • Conclusions • Automatic Web service composition Zhongzhi Shi: Semantic Web Services

  42. Services Composition • Planning based approaches: • construct a plan from elementary services to obtain a required functionality. • reasoning based only on component specifications • plan built every time from scratch • Knowledge based approaches: • re-use preconfigured templates • reasoning with specialised knowledge in a narrow domain • sophisticated domain knowledge is needed micro IEUM only-1/0 single solution explanative aggregate-IEUM Zhongzhi Shi: Semantic Web Services

  43. Services Composition Zhongzhi Shi: Semantic Web Services

  44. Services Composition Zhongzhi Shi: Semantic Web Services

  45. OWL-S Context Provides Uses Resource Service User Show R - Context Supports Presents Show U - Context Described by R - Context Service Profile Service Grounding U - Context Show S - Context Service Model S - Context Zhongzhi Shi: Semantic Web Services

  46. Context-based Planning for Services Composition Planner Zhongzhi Shi: Semantic Web Services

  47. Context-based Planning for Services Composition Planner Retrieves context information from Web Zhongzhi Shi: Semantic Web Services

  48. Context-based Planning for Services Composition Planner Store context of users, services Zhongzhi Shi: Semantic Web Services

  49. Context-based Planning for Services Composition Planner Store Defined rules Zhongzhi Shi: Semantic Web Services

  50. Context-based Planning for Services Composition Planner Create a sequence of actions Zhongzhi Shi: Semantic Web Services

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