Designing Semantic Web Process: The WSDL-S Approach Presented by Ke Li LSDIS Lab, University of Georgia (Under the Direction of John A. Miller)

Acknowledgment Dr. John A. Miller Dr. Amit P. Sheth Dr. Eileen T. Kraemer Kunal, Meena Doug, Zixin Cary, Scott

Outline Introduction Background Contribution The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Introduction Web Service –Concept Web Services are self-contained modular business applications that have open, internet-oriented and standards-based interface. –Web Service Standards SOAP (Simple Object Access Protocol) WSDL (Web Service Description Language) UDDI (Universal Description, Discovery and Integration) WS-BPEL (Web Service Business Process Execution Language)

Introduction Web Process –Combine individual services to achieve a more complex goal –Advantage enables modular design Implements the control and data flow between services –For example, Service A, B and C. B is dependent on A’s output message. A and C can be invoked at the same time

Introduction Semantic Web Service –Adding semantics to Web service standards –Bring “meaning” to the services –Removes ambiguity in the description of Web service elements. –Enables automation of tasks like discovery, invocation, composition

Introduction WSDL-S Based Tool Suite for Designing Semantic Web Process  Radiant – Web Service Semantic Annotation and Semantic Publish  Lumina – Semantic Web Service Discovery  Saros – Web Services Composition

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Background Problems with current Web Service publishing, discovery and composition –No unified WSDL to UDDI mapping structure –Syntactic search mechanism for service discovery

Background Mapping Structure from WSDL to UDDI

Background Modified Mapping Structure from WSDL-S to UDDI

Background Four Semantic Web Service Standard Proposals (Brief Overview) –OWL-S –WSMO (Web Service Modeling Ontology) –SWSO (Semantic Web Service Ontology) –WSDL-S

Background (OWL-S and WSMO) OWL-S WSMO

Background (WSDL-S) –WSDL-S Built on existing Web Service standard - WSDL Enables semantic annotation of Web Services by using extensibility elements and attributes –Annotation on message types: modelReference, and schemaMapping –Annotation on operation: modelReference, category, precondition and effect Uses external domain models to provide the semantics

Outline Introduction Background Contribution The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Contribution Design and develop the WSDL-S based semantic discovery tool called Lumina. Develop the API for publishing WSDL / WSDL-S files to our enhanced UDDI registry. Design the Semantic Template generator which can be used within Saros.

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

The METEOR-S Semantic Discovery Tool - Lumina Motivation 1.Support semantic discovery (WSDL-S approach) 2.Enable semi-automatic design of Web Processes (using Saros) 3.Supply a unified discovery style to discover from different Universal Business Registries

The METEOR-S Semantic Discovery Tool - Lumina Setting Environment –Web Server: Tomcat –UDDI Registry Implementation: JUDDI 0.9 –Registry Database: MySQL win32 –JDK 1.5 Model Dependency –METEOR-S Discovery API –WSDLS4J –UDDI4J

The METEOR-S Semantic Discovery Tool - Lumina Discovery Modes – UDDI structure based discovery 1.General UDDI Discovery (Basic UDDI Discovery Panel) Unified search style for all the UBRs Provide discovery of “Business Entity”, “Business Service” and “Technical Model” –Business Entity: Business Name, Discovery URL, Categories, TModel Keys –Business Service: Service Name, Categories, Business Key, TModel Keys –TModel: TModel Name

The METEOR-S Semantic Discovery Tool – Lumina – UDDI Structure Based Discovery

The METEOR-S Semantic Discovery Tool - Lumina Discovery Modes – WSDL-S based Semantic Discovery 2. WSDL-S Discovery (WSDL-S Discovery Panel & Semantic Template View) Input to the discovery module 1.Ontology URL 2.Operation functional concepts, semantic inputs and semantic outputs Input modes 1.Typed in by the user 2.Dragged and dropped from the Ontology Navigator in Radiant Output from the discovery module 1.Service information (service name, WSDL location) 2.Service provider information (link to the business entity) 3.Detailed discovered operation information (operation name, input / output variables, ontological concepts about these parameters, input / output types, portType)

The METEOR-S Semantic Discovery Tool – Lumina: Semantic Discovery Panel + Partner Service Viewer

The METEOR-S Semantic Discovery Tool – Lumina: Semantic Template View

The METEOR-S Semantic Discovery Tool - Lumina Discovery Modes – WSDL based Syntactic discovery 3. WSDL Discovery (WSDL Discovery Panel) Input to discovery module: –Exact operation name, input and output variables –Same as WSDL-S Discovery except the semantic information

The METEOR-S Semantic Discovery Tool – Lumina – METEOR-S Discovery Class Diagram

The METEOR-S Semantic Discovery Tool - Lumina Architecture of Lumina –Adopts Eclipse Plugin Techniques: Action: Lumina start button in tool bar Editor: UDDI Editor (Basic UDDI Discovery, WSDL-S Discovery and WSDL Discovery) View: (Semantic Template Viewer and Partner Service Viewer) Perspective: Lumina Perspective

The METEOR-S Semantic Discovery Tool – Lumina Model Class diagram

The METEOR-S Semantic Discovery Tool – Lumina View Class diagram

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Design Semantic Web Process Using WSDL-S Semantic Annotation and Publish – Radiant –WSDL-S to UDDI Mapping Structure WSDLUDDI ServiceBusiness Service Local NameName Service DescriptionDescription Namespace, WSDL location CategoryBag portTypeTModel Local NameName WSDL locationOverviewDoc NamespaceCategoryBag OperationTModel Local NameName WSDL locationOverviewDoc Namespace, inputs, outputs, semantic concepts, etc. CategoryBag

Design Semantic Web Process Using WSDL-S : WSDL-S to UDDI Mapping Structure

Design Semantic Web Process Using WSDL-S – Annotating a Web service using Radiant

Design Semantic Web Process Using WSDL-S Semantic Discovery – Lumina –WSDL-S Discovery Panel (UDDI Editor) and Partner Service Viewer Store the candidate partner services to partner service viewer (convenient) –Semantic Template Viewer Save the semantic template to files Load a exist semantic file –Enable discovery of multiple operations Dependence Economic Efficient

Design Semantic Web Process Using WSDL-S: WSDL-S Discovery Panel

Design Semantic Web Process Using WSDL-S: Semantic Template View

Design Semantic Web Process Using WSDL-S Semantic Web Process Design - Saros –Dynamic partner selection using the Semantic Templates that describe virtual partners –Two phase design Generate new semantic template(s) or load the exist semantic template(s) using Semantic Template Viewer (Lumina) Discover the partner services using the semantic templates and bind the discovered services to the process

Design Semantic Web Process Using WSDL-S Semantic Web Process Design - Saros Process Designer Semantic Publication and Discovery UDDI Registry Semantic Template WSDL-S Search BPEL Process Results

Design Semantic Web Process Using WSDL-S: Saros Design Panel

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Sample Use Case Sample Scenario –Goal: investment strategy for buying stock –Input: stock ticker, possible investment amount –Output: value analysis for proceeding with the investment

Sample Use Case 1.Analyze the business requirement and build a UML diagram

Sample Use Case 2.Fill in the process skeleton

Sample Use Case 3.Fill in the nested constructs / structured activities

Sample Use Case 4. Identify partners –Binding the real partners: using concrete service information –Binding the virtual partners: filling a semantic template –Discovery based on: Operation Input and output

Sample Use Case : S earch for “investment” service

Sample Use Case: S earch for the other two services

Sample Use Case: Add virtual partner

Sample Use Case 1.Analyze the business requirement and build a UML diagram 2. Fill in the process skeleton 3. Fill in the nested constructs / structured activities 4. Identify partners –Binding real partners –Binding virtual partners

Sample Use Case 5.Add namespace, variables 6. Link partners to “invoke”, “receive” and “reply” 7. Add the supplementary elements and fill in details 8. Generate BPEL process –BPEL file –WSDL file of the Process

Sample Use Case: Complete BPEL Process part I

Sample Use Case: Complete BPEL Process part II

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Evaluation 1.Efficient and effective service discovery –Comparative UBRs: XMethods and Microsoft UBR –Search scenario: Stock Quote –Comparative formulas: Precision = Recall =

Evaluation Precision of “Stock Quote” Web Services Discovery Result on the Regular UBRs KeywordsXMethodsMicrosoft UBR TModelServiceTModel Stock quote3/3 = 100%17/17 = 100%28/28=100% Stockquote3/3=100%4/4=100%18/18=100% Stock7/7=100%64/70=90%18/18=100% Delayed1/1=100%5/5=100%1/1=100% Real time1/3=33%0/1=00/0 Realtime0/00/1=00/0 Recall of “Stock Quote” Web Services Discovery Result on XMethods: 9/13 = 69%

Evaluation The “Stock Quote” services in the enhanced UDDI registry ServicesOperationsOntological Concepts DelayedStockQuoteGetQuoteOntologyNS#DelayedStockQuote GetQuickQuoteOntologyNS#RealTimeStockQuote BasicRealTimeQuotesGetOneQuoteOntologyNS#RealTimeStockQuote StockQuotesGetStockQuotesOntologyNS#DelayedStockQuote StockScraperGetQuoteOntologyNS#StockQuote StockServicesGetQuotesOntologyNS#StockQuote GetQuickQuotesOntologyNS#DelayedStockQuote StockQuoteGetQuoteOntologyNS#StockQuote DOTSFastQuoteGetStockInfoOntologyNS#DelayedStockQuote Nexus6Studio_x0020_ Stock_x0020_Quote GetQuickQuoteOntologyNS#StockQuote GetDetailedQuoteOntologyNS#StockQuote

Evaluation Precision of Lumina Discovery Result Using StockQuote Ontological Concepts Ontological ConceptsLumina TModel (Operations)Service OntologyNS# StockQuote 11/11=100%8/8=100% OntologyNS# DelayedStockQuote 9/9=100%7/7=100% OntologyNS# RealTimeStockQuote 7/7=100%6/6=100%

Evaluation Recall of Lumina Discovery Result Using StockQuote Ontological Concepts Ontological ConceptsLumina TModel (Operations)Service OntologyNS# StockQuote 11/11=100%8/8=100% OntologyNS# DelayedStockQuote 9/9=100%7/7=100% OntologyNS# RealTimeStockQuote 7/7=100%6/6=100%

Evaluation 2. Accurate discovery of specific operations –Service level discovery and operation level discovery –Operation level discovery: operation functional concept, semantic inputs, semantic outputs –Discovery of Multiple operations within one partner service

Evaluation Web Services Based on the Currency Ontology Searching RequirementSearching Result Web Service: Operation Operation: ontologyNS#CurrencyRate Input: /n Output: /n Currencyws: GetRate CurrencyConvertor: ConvertionRate CurrencyExchangeService: getRate Operation: ontologyNS#Currency Input: /n Output: /n Country: GetCurrencies; GetCurrencyByCountry; GetCurrencyCode; GetCurrencyCodeByCurrencyName Operation: ontologyNS#Currency.country Input: /n Output: /n Country: GetCountries; GetCountryByCountryCode; GetCountryByCurrencyCode; GetISOCountryCodeByCountryName Operation: ontologyNS#Currency Input: ontologyNS#Currency.country Output: /n Country: GetCurrencyByCountry Operation: ontologyNS#Currency Input: ontologyNS#Currency.name Output: ontologyNS#Currency.code Country: GetCurrencyCodeByCurrencyName Operation: ontologyNS#Currency.country Input: ontologyNS#Currency.country_iso_code Output: ontologyNS#Currency.country Country: GetCountryByCountryCode;

Evaluation Web Services annotated with a part of the Travel Ontology describing the Weather domain Searching RequirementSearching Result Operation: ontologyNS#Forcast Input: /n Output: /n ForecastByZip: GetForecastByZip WeatherFetcher: GetWeather DOTSFastWeather: GetWeatherByCityState GetWeatherByZip Operation: ontologyNS#Forcast Input: ontologyNS#ZipCode Output: /n ForecastByZip: GetForecastByZipDOTSFastWeather: GetWeatherByZipWeatherFetcher: GetWeather Operation: ontologyNS#Forcast Input: ontologyNS#City Output: /n DOTSFastWeather: GetWeatherByCityState Operation: ontologyNS#Forcast Input: ontologyNS#City Output: OntologyNS#Weather.hasPercipitation DOTSFastWeather: GetWeatherByZip

Evaluation 3. Semi-automatic BPEL process design –Supply the detailed information of the service and operations; these information are useful to design the process.

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Related Work Two categories of Web Services Composition: –Workflow composition Static workflow composition: –EFlow (Casati, Ilnicki, et al. 2000) Dynamic workflow composition: –PPM (Polymorphic Process Model) (Schuster, Georgakopoulos, et al. 2000) –AI Planning –Golog – Logic Programming Language –Planning Domain Definition Language (PDDL) – action value –Rule-based plan generation – SWORD (Ponnekanti and Fox, 2002) –Hierarchical Task Network (HTN) – SHOP2 (Wu, Sirin, et al. 2003)

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Conclusion By using Semantics to annotate the Web Services, we provide the efficient, effective and accurate services discovery. It enables the automatic and dynamic Web Process Design. WSDL-S supplies a easy way to add semantics to the Web Services.

Outline Introduction Background The METEOR-S Semantic Discovery Tool – Lumina Design Semantic Web Process Using WSDL-S Sample Use Case Evaluation Related Work Conclusion Future Work

Organize Saros to run in the same workspace as Lumina, and supply the Drag and Drop functionality to further ease the work of the process developer Develop a constraint analyzer to extend our WSDL-S based tool suite. Adopt data mapping techniques to implement Web Process composition automatically. Extend the discovery, composition by using “preconditions” and “effects” to achieve a more accurate result. Develop a process monitor to trace the process at execution time.

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