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Semantic Web Services Composition via Planning as Model Checking

Explore the background and framework of SWSC, discussing the planning-as-model-checking approach, advantages, and future work. Case study on Web Services composition exemplifies the phases of specification, model extraction, planning, and physical composition. Gain insights into challenges and solutions in Semantic Web Services.

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Semantic Web Services Composition via Planning as Model Checking

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  1. Semantic Web Services Composition via Planning as Model Checking Hong Qing Yu and Dr. Stephan Reiff-Marganiec Computer Science Department

  2. Introduction • Background and the framework of SWSC • Case study • Web Services model • Planning as model checking • Advantages and future work

  3. Background of SWS Syntax only! WS standards: Lack of semantics! Web Service Architecture

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

  5. The framework of SWSC Phase 1 : Specification Specify the Planning Goal Provide the initial situation WS Repository Phase 2 : Model extraction Select WS which in the plan domain Extract WS models Ontologies Phase 3 : Planning Phase 4 : Physical Composition & Execution Selection Generation Execution

  6. Case study of Web Services Composition Services WS1=Locate IP Initial Situation Smart Portal WS2= TV Information WS3= TV shop (S) && After WS4= Item delivery (D) I) (D S WS5= Insurance (I) Goal WS6= TV License

  7. WS model (Type, Role) Precondition Input message (Parameters) Operation Name Domain Communications Purpose Quality Output message (Parameters) State Operation Operation Operation Operation

  8. WS model Got_TVL Min (string Brand, double S_size, string Type, string Location, string TV_license ) Min (string IP_address) WS1 WS2 WS3 Confirm E-shopping E-shopping E-shopping Select Locating TV_infor TV_sell Request high high high Mout (string Location) Mout (string Brand, string Type, double S_size, string review, Colo_type) Mout (string S_adress, double value, double TV_size) Located Got_infor Purchased Purchased Located Min (double value, string C_address, string Goods_type) Min (string S_address string Location, double size) Min (string C_address, string Colo_type) WS4 WS5 WS6 Confirm Confirm Select E-shopping E-shopping E-shopping Delivery_item Insurance TV_license Request Request Request high high high Mout (date delivery_time, double cost) Mout (string reference) Mout (string TV_license) Deliveried_Item Bought_insurance Got_TVL

  9. Composition problem model • Specification for the goal • Specification for start conditions and data • We are planning from initial operation state • The initial knowledge is the information which submitted by Client user • Our case • Initial state is start • Initial knowledge is Customer address, Goods type (TV), IP address I) (D S

  10. Planning as Model Checking State: {Start, Located, Got_infor, s Got_TVL, Purchased, Delivered_item, 1 2 Bought_insurance} 2 Parameter: {string C_address, string Goods_type, string IP_address, 1 string Location, string TV_license 6 old string Brand, string Type, double S_size, string review, Colo_type, old string S_adress, double value, double TV_size, 3 date delivery_time, double cost, 4 old 5 string reference} 5 4 I) (D S

  11. Advantages and future work Advantages: • Not rely on any particular ontology language • Simple specification • Executable + Reusable Future Work: • More complex goals • Add non-functional requirement to planning algorithm • Interleaving of services in plans • Complete the framework

  12. Thanks Any Questions? TR available : http://www.cs.le.ac.uk/people/hqy1/swsc_pamc1.0.pdf

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