1. Semantic Web Services Sharif University of Technology Spring 2007

2. 2 Outline What is the Problem? –Semantic Web Services –Service Discovery –Service Composition –Context-awareness Related Works Contributions –Proposed Architecture for a Context-aware Service Broker –Proposed Methods for Service Matchmaking

3. What is the Problem?

4. 4 Web Services Web-accessible Self-describing Platform independent Definition of World Wide Web Consortium (W3C): –“a software system designed to support interoperable machine-to-machine interaction over a network. It has an interface described in a machine-processable format (specifically WSDL). Other systems interact with the Web service in a manner prescribed by its description using SOAP messages, typically conveyed using HTTP with an XML serialization in conjunction with other Web related standards”

5. 5 Service Oriented Architecture Service Broker Service User Service Provider Find PublishBind UDDI WSDL SOAP SOAP – Simple Object Access Protocol / SOA Protocol WSDL – Web Services Description Language UDDI – Universal, Description, Discovery, and Integration Only Syntax, Agents can not understand meanings

6. 6 Semantic Web Services Semantic Web –Sharing Information on the Web –Computer-interpretable Web Services –Sharing Programs on the Web Semantic Web Services –Web Services + Semantic Web –Using ontologies to describe web services

7. 7 Ontologies for Semantic Web Services Use ontologies to describe Web Services –DAML-S (since 2001) –OWL-S (since 2003) Based on OWL-DL –WSMO (since 2004) –SWSO (since 2005) Based on FLOWS (First-order Logic Ontology for Web Services)

8. 8 Issues in Semantic Web Services Discovery (Matchmaking): Locate different services suitable for a given task Selection: Choose the most appropriate services among the available ones Composition: Combine services to achieve a goal –Automatic Service Composition might enable programmer to become specifying what to do and not anymore how to do it! Execution: Invoke services following programmatic conventions Monitoring: Control the execution process

9. 9 A trivial Example

10. 10 An Obvious Solution

11. 11 Composition: An Example BookShipped GetInfo BookNamePasswordUserName Login PutInCart BookLookUp CardTypeCardExpiryDateCardName CreditCardCheck ShipItem Goal Available Services Inputs HotelReservation FlightBooking CarRental

12. 12 Composition: An Example BookShipped GetInfo InCart BookName PasswordUserName ISBNBookInStockProfileExists UserType Login PutInCart BookLookUp CardType Approved CardExpiryDateCardName CreditCardCheck ShipItem

13. OWL-S

14. 14 Introduction The ontology of services provides three essential types of knowledge about a service: service profile, service model, and service grounding. The class Service provides an organizational point of reference for a declared Web Service. –1. The class Service “presents” a ServiceProfile: “What does the service provide for and require of agents?” –2. The class Service is “describedBy” a ServiceModel: “How does it work?” –3. The class Service “supports” a ServiceGrounding: “How to access the service?”

15. 15 Service profile

16. 16 Service model

17. 17 Process ontology Process Ontology can have any number of inputs and outputs representing the information required for execution. Besides inputs and outputs, parameters for physical devices, such things as rates, forces, and control settings can be included. The OWL-S defines three types of processes: Atomic (directly invokable), Simple (single-step, but not directly invokable), and Composite (decomposable into other processes).

18. 18 Atomic Process The atomic processes can be invoked directly and are executed in a single step. For each atomic process, there is a grounding that enables a service requester to construct messages. An AtomicProcess is a subclass of a Process

19. 19 Simple Process Simple processes are not associated with a grounding. They are single- step executions. Simple processes are used as elements of abstraction; a simple process may be used either to provide a view of some atomic process, or a simplified representation of some composite process.

20. 20 Composite Process Composite processes are decomposable into other processes. The decomposition shows how the various inputs and outputs are accepted A CompositeProcess must have a composedOf property by which is indicated the control structure of the composite, using a ControlConstruct.

21. 21 Composite Process (cont.)

22. Related Works

23. 23 Composition Approaches Manual –Design-time composition –BPEL4WS (Business Process Execution Language for Web Services) Workflow-based –Only works when the web service environment doesn’t, or only rarely changes Automatic –AI Planning & Workflow-based

24. 24 AI Planning A B move(a,table) move(c,a) move(b,c) C B C A Initial StateGoal State Actions

25. 25 AI Planning for Composition (1) Planning Domain Definition Language (PDDL) –PDDL is a standardized input for state-of-the-art planners –PDDL and OWL-S representations are very similar. –OWLS2PDDL is available. –Different planners have different capabilities and by using this method we can use the best suited planner for each particular composition task.

26. 26 AI Planning for Composition (2) Rule-based Planning –Medjahed (2003) –Composability rules are used to determine whether two services are composable. Message composability (output of one service is compatible with input of another). Operation semantic composability (defines the compatibility of domains and categories and purposes of two services). Qualitative composability (defines the requester’s preferences for quality of operations). Composition soundness (determines whether a composition of services is reasonable). Composition templates that define dependencies between services are used.

27. 27 AI Planning for Composition (3) Rule-based Planning –SWORD It uses Entity-Relation model to specify web services. A service is modeled by its preconditions and postconditions and is represented in the form of a Horn rule that denotes postconditions are achieved if the preconditions are true. User specifies the initial and final states. A rule-based Expert System is used for plan generation.

28. 28 AI Planning for Composition (4) Situation Calculus –Activities users perform on the web can be viewed as customizations of reusable, high-level generic procedures. –Runtime customization of these generic procedures. –Situation calculus is a logic language for reasoning about action and change. –GOLOG is a logic programming language built on top of the situation calculus. –McIIrith et. al. (2001,2002), adapt and extend the GOLOG language for automatic construction of Web services. –Web Service = Action Primitive –World-altering: change the state of the world –Information-gathering: change the state of the knowledge Complex –Compositions of individual actions –Main Problem: GOLOG programs are difficult to create

29. 29 AI Planning for Composition (5) Hierarchical Task Network Planners Composite task decomposition in HTN planning is very similar to Composite process decomposition in OWL-S.

30. 30 AI Planning for Composition (6) Hierarchical Task Network Planners –User must give an abstract task list. –SHOP2 –More efficient than other planning languages such as GOLOG. –OWL-S can be translated to SHOP2. –JSHOP2 is open source. –Main Problems: Lack of parallel execution, a feature frequently needed for efficient web service usage. Processes either must have outputs or effects, but not both. –It enables information gathering during planning. it is not possible to directly express the semantics of OWL DL using SHOP2 axioms. A task can not be both primitive and nonprimitive.

31. 31 AI Planning for Composition (7) OWLS-Xplan –An open source composition tool released Dec. 2005 –Based on Xplan planner

32. 32 Template-based Composition Sirin et al., Nov. 2005 A workflow template describes the outline of activities that need to be performed to solve a problem. –Some of the activities are defined as abstract activities. Recursive decomposition of templates. Generic templates can be customized for a specific instance of the problem based on the users’ preferences: –Use only certified services –Try to find non-fee services –Do not buy the plane ticket if we can not reserve the hotel room.

33. 33 Semi-automatic Composition Current automatic composition approaches can not scale with the amount of knowledge on Semantic Web. Sirin et al. (2004) Automatic planner and human being can work together to generate the composite service. The user starts the composition process by selecting one of the services registered to the engine. A query is sent to the KB to retrieve the information about the inputs of the service, and for each of the inputs, a new query is run to get the list of the possible services that can supply the appropriate data for this input.

34. 34 Context-awareness What is context? (in our work) –context encompasses all information about the client of a web service that may be utilized by the web service for adjusting the execution and output to provide the client with a customized and personalized behavior. For example, –Profile General-info: –Name, email, credit-card number,... Preferences: –Currency, Language,... –Location –CC/PP (Composite Capabilities / Preferences Profile) –Bandwidth

35. 35 Context-based Adaptation What is the Screen size? Is it JavaScript Enabled?

36. A Proposal for Service Broker

37. 37 A Context-aware Service Broker Request + ContextAdapted Request Matchmaking Request Profile Request Registered Services Info Composition Request Selected Service Ask for inputsInputs requestAdapted inputs Service Specification + Inputs Generated outputsAdapted outputs

38. 38 Matchmaking

39. 39 Proposed method for Matchmaking Fuzzy Matchmaking: –Instead of using strict levels of matching, let’s use a value between 0 and 1. –We can use the concept of Semantic Distance. C1 CCP C2 1/2 1/8 1/4 1/16

40. 40 Evaluation

41. 41 Input/Output Matching is not sufficient Add and Multiply services both have similar signatures: We need to find what services actually do. Add Integer Multiply Integer