1. Knowledge Enabled Information and Services Science Semantics in Services Dr. Amit P. Sheth, Lexis-Nexis Eminent Scholar, kno.e.sis center, Wright State University, Dayton, OH.

2. Knowledge Enabled Information and Services Science Outline Motivation SAWSDL Dynamic Configuration Event Identification

3. Knowledge Enabled Information and Services Science Motivation Evolution of business needs drives IT innovation Initial focus on automation led to workflow technology In order to facilitate efficient inter-organizational processes distributed computing paradigms were developed –CORBA, JMS, Web Services The current and future needs include: –Creating highly adaptive process that react to changing conditions Focus on real time events and data – RFID and ubiquitous devices –Have the ability to quickly collaborate with new partners –Aligning business goals and IT processes

4. Knowledge Enabled Information and Services Science Advantages of Semantics Reuse –Semantic descriptions of services to help find relevant services Interoperability –Beyond syntax to semantics (ontology based approach) Composition –Enable dynamic binding of partners Some degree of automation across process lifecycle –Process Configuration (Discovery and Constraint analysis) –Process Execution (Addressing run time heterogeneities like data heterogeneities.) 4

5. Knowledge Enabled Information and Services Science Semantics for SOA Lifecycle Data Semantics: The semantics of the data that is either the input to a service or that is the output of the service. –Eg. Annotated service input and output specification. Functional Semantics: The semantics of what the service offers, the interfaces and the operations in a service. –Eg. Annotated interface and operation specifications of a service. Non-Functional Semantics: The qualitative and the quantitative non-functional aspects of a service and its operations are modeled using non-functional semantics. –Adding semantics to policy specifications such as WS-Policy and WS- Agreement. Execution Semantics: The semantics concerning the various exceptions and the actions to adapt to these during service execution. –Modeling execution paths using task skeletons

6. Knowledge Enabled Information and Services Science SAWSDL: Semantic Annotations for WSDL

7. Knowledge Enabled Information and Services Science SAWSDL Semantic Annotations for WSDL –Adds semantics to service descriptions via model references. –W3C Candidate Recommendation Offer an evolutionary and compatible upgrade of existing Web services standards Externalize the semantic domain models –agnostic to ontology representation languages. –reuse of existing domain models –allows annotation using multiple ontologies (same or different domain) 7

8. Knowledge Enabled Information and Services Science SAWSDL at a glance 8 Ack: Jacek Kopecky

9. Knowledge Enabled Information and Services Science SAWSDL Example ………… <xs:element name= "processPurchaseOrderResponse" type="xs:string wssem:modelReference="POOntology#OrderConfirmation"/> http://www.naics.com/ <input messageLabel = ”processPurchaseOrderRequest" element="tns:processPurchaseOrderRequest"/> <output messageLabel ="processPurchaseOrderResponse" element="processPurchaseOrderResponse"/> <wssem:precondition name="ExistingAcctPrecond" wssem:modelReference="POOntology#AccountExists"> <wssem:effect name="ItemReservedEffect" wssem:modelReference="POOntology#ItemReserved"/> 9

10. Knowledge Enabled Information and Services Science Using modelReference and SchemaMapping modelReference at the complex type level –Typically used when specifying complex associations at leaf level is not possible –Allows for specification of a mapping function 10 Address xsd:string OWL ontology has_City has_StreetAddress has_Zip WSDL complex type element semantic match

11. Knowledge Enabled Information and Services Science Using modelReference and schemaMapping modelReference at the leaf levels –assumes a 1:1 correspondence between leaf elements and domain model concepts 11 Item dueDate ItemDesc Quantity OWL ontology hasIemDesc hasDueDate hasQuantity WSDL complex type element

12. Knowledge Enabled Information and Services Science Representing mappings 12 Address xsd:string OWL ontology has_City has_StreetAddress has_Zip WSDL complex type element........ Mapping using XSLT

13. Knowledge Enabled Information and Services Science

14. Configuration and Adaptation – Roadmap Semantic Web Enablers Ontologies: Specification of conceptualization. Mode of capturing concepts and their relationships, etc. OWL: Ontology Web Language SWRL: Semantic Web Rule Language Annotation/ Representation WSDL-S/SAWSDL (02-06) Discovery (MWSDI / SSR) Mapping WSDL-S into UDDI Constraint Analysis Semantically Enhanced Policies/ SWAPS Dynamic Execution Extenstions to SOA Middleware and Runtime WSDL UDDI WS-Policy, WS-Agreement BPEL Engines (BPWS4J, ActiveBPEL) BPEL Composition Planning and patterns Existing WS Standards/ Infrastructure Process Adaptation Dynamic Process Configuration

15. Knowledge Enabled Information and Services Science Linking the different layers in a business process

16. Knowledge Enabled Information and Services Science Semantic Templates SAWSDL + Enhanced policy descriptions to model the data, functional and non-functional semantics at the various tiers –Business Process Tier: Capture process level requirements –Implementation Tier: Capture partner level requirements Non-functional semantics captured at template and operation levels. XML representation for interoperability.

17. Knowledge Enabled Information and Services Science Semantic Templates SAWSDL for data and functional semantics Semantic Policy Descriptions for non-functional semantics

18. Knowledge Enabled Information and Services Science Example of a semantic template in the supply chain domain

19. Knowledge Enabled Information and Services Science Dynamic Binding: Guiding principles Semantic templates to capture the requirements for each partner. Partners are selected during the run time of the process and the process is configured –Semantically Enhanced UDDI Registries for discovery of partners. –Approaches to match enhanced policies (Sem-Pol) and agreements (SWAPS) Execution environment supporting discovery, configuration and invocation.

20. Knowledge Enabled Information and Services Science Example of a process with semantic templates

21. Knowledge Enabled Information and Services Science Dynamic Configuration: Components Semantically enhanced Services Registry (SSR) –Domain aware services registry for publishing and selecting services. –Extends UDDI data structures natively –Domain awareness allows for reasoning on annotations. –Support for WS-Policy and Enhanced WS-Policy for service selection. –Mapping UDDI to SAWSDL

22. Knowledge Enabled Information and Services Science High Level Architecture of Middleware infrastructure Entities Process Manager (PM): Responsible for global process configuration Service Manager (SM): Responsible for interaction of process with service Configuration Module (CM): Discovery and constraint analysis Adaptation Module (AM): Process adaptation from exceptions/events

23. Knowledge Enabled Information and Services Science Towards Autonomy at the middleware level Self adapting, Self Optimizing, Self Healing and may be Self Buying (If you are selling these) What can go wrong, How do I know when things go wrong and what do I do? What can go wrong? –Identifying events How do I know when things go wrong? –Subscription management and notification management What do I do when things go wrong? –Adaptation modeling and Strategies

24. Knowledge Enabled Information and Services Science Algorithm for Event Identification