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Context-aware Semantic Web Service Composition

Context-aware Semantic Web Service Composition. Yasser Ganji Saffar ganji@ce.sharif.edu. Semantic Web Laboratory Computer Engineering Department Sharif University of Technology http://sw.ce.sharif.edu. Outline. What is the Problem? Semantic Web Services Service Discovery

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Context-aware Semantic Web Service Composition

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  1. Context-aware Semantic Web Service Composition Yasser Ganji Saffar ganji@ce.sharif.edu Semantic Web Laboratory Computer Engineering Department Sharif University of Technology http://sw.ce.sharif.edu

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

  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. 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. 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. A trivial Example

  10. An Obvious Solution

  11. Composition: An Example BookName UserName Password Inputs CardType CardName CardExpiryDate Login HotelReservation BookLookUp ShipItem PutInCart Available Services CarRental FlightBooking CreditCardCheck GetInfo Goal BookShipped

  12. Composition: An Example UserName Password BookName Login UserType BookLookUp CardType CardName CardExpiryDate GetInfo ProfileExists BookInStock ISBN CreditCardCheck PutInCart Approved InCart ShipItem BookShipped

  13. Related Works

  14. 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

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

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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.

  21. 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.

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

  23. 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.

  24. 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.

  25. 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

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

  27. Contributions

  28. A Context-aware Service Broker Service Specification + Inputs Profile Request Inputs request Adapted outputs Ask for inputs Adapted inputs Generated outputs Request + Context Adapted Request Matchmaking Request Composition Request Registered Services Info Selected Service

  29. Matchmaking

  30. 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. 1/2 CCP 1/4 1/4 C1 1/8 C2 1/16

  31. Evaluation

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

  33. Publications • Y. Ganji Saffar, H. Abolhassani, R. Jalili, “An Architecture for a Context-aware Service Broker for Ubiquitous Computing Environments”, to appear, The 4th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA-06), UAE, March 2006 • Y. Ganji Saffar, H. Abolhassani, “Context-aware Semantic Web Service Brokering”, to appear, 11th Computer Society of Iran Computer Conference, Iran, 2006

  34. Future Plan

  35. Questions ?

  36. Main References • M. Klusch, B. Fries, and M. Khalid, “OWLS-MX: Hybrid Semantic Web Service Retrieval”, In Proceedings of 1st International AAAI Fall Symposium on Agents and the Semantic Web, Arlington VA, USA, 2005. • M. Klusch, A. Gerber, and M. Schmidt, “Semantic Web Service Composition Planning with OWLS-Xplan”, AAAI Fall Symposium Series, Arlington, Virginia, USA, Nov. 2005. • B. Medjahed, A. Bouguettaya, and A. K. Elmagarmid, “Composing Web services on the Semantic Web”, The VLDB Journal, vol. 12, no. 4, Nov. 2003. • S. McIlraith and T. C. Son, “Adapting Golog for composition of Semantic Web services”, In Proceedings of the 8th International Conference on Knowledge Representation and Reasoning (KR2002), Toulouse, France, April 2002. • S. R. Ponnekanti and A. Fox, “SWORD: A developer toolkit for Web service composition”, In Proceedings of the 11th World Wide Web Conference, Honolulu, HI, USA, 2002. • D. Wu et al., “Automatic Web services composition using SHOP2”, In Proceedings of the Workshop on Planning for Web Services, Trento, Italy, June 2003. • E. Sisrin, B. Parsia, and J. Hendler, “Filtering and selecting semantic web services with interactive composition techniques”, IEEE Intelligent Systems, vol. 19, no. 4, pp. 42-49, 2004 • M. Paolucci et al., “Semantic matching of web services capabilities”, In Proceedings of the 1st International Semantic Web Conference (ISWC), Springer Verlag, 2002, pp. 333-347. • S. Ben Mokhtar et al., “Context-aware Service Composition in Pervasive Computing Environments”, In Proceedings of the 2nd International Workshop on Rapid Integration of Software Engineering techniques (RISE’05), Heraklion Crete, Greece, Sep. 2005.

  37. Discovery Approaches • Based on service inputs and outputs • Pure logic-based • Exact • Subsume • Plug-in • Fail • Logic-based methods + Information retrieval methods • Using implicit knowledge which is available in service descriptions BookName ? ISBN

  38. AI Planning for Composition (2) • Situation Calculus • Every situation is defined by a world history, that is a sequence of actions. • The constant s0 describes the initial situation, that is a situation where no actions have occurred yet. • A state do(putDown(A), do(walk(L), do(pickUp(A), s0))) describes the situation created by the execution of a sequence [pickUp(A),walk(L), putDown(A)]. • A composite service is a set of atomic services which connected by procedural programming language constructs (like if-then-else, while, for and so forth).

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