Semantic Web Services

Sharif University of Technology

Spring 2007

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

What is the Problem?

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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”

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Service Oriented Architecture

ServiceBroker

ServiceUser

ServiceProvider

Find

Publish Bind

UDDI

WSDL

SOAP

SOAP SOAP

SOAP – Simple Object Access Protocol / SOA ProtocolWSDL – Web Services Description LanguageUDDI – Universal, Description, Discovery, and Integration

Only Syntax,Agents can not

understand meanings

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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

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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)

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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

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A trivial Example

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An Obvious Solution

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Composition: An Example

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Composition: An Example

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OWL-S

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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?”

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Service profile

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Service model

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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).

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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

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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.

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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.

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Composite Process (cont.)

Related Works

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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

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AI Planning

A

B

move(a,table)move(c,a)move(b,c)C

B

C

A

Initial State Goal State Actions

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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.

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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.

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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.

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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

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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.

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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.

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AI Planning for Composition (7)

• OWLS-Xplan – An open source composition tool released Dec. 2005– Based on Xplan planner

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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.

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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.

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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

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Context-based Adaptation

What is the Screen size?

Is it JavaScript Enabled?

A Proposal for Service Broker

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Service Registry

Composed Services Cache

Composer

Service Requester

Service Matchmaker

Service Provider

AdapterCommunication

ManagerService Executor

Profile Server

Registry Manager

Context Manager

Service Broker

Service Registry

Composed Services Cache

Composer

Service Requester

Service Matchmaker

Service Provider

AdapterCommunication

ManagerService Executor

Profile Server

Registry Manager

Context Manager

Service Broker

Service Registry

Composed Services Cache

Composer

Service Requester

Service Matchmaker

Service Provider

AdapterCommunication

ManagerService Executor

Profile Server

Registry Manager

Context Manager

Service Broker

A Context-aware Service Broker

Request + Context Adapted Request

Matchmaking Request

Profile Request

Registered Services Info

Composition RequestSelected Service

Ask for inputsInputs request Adapted inputs

Service Specification +

InputsGenerated outputsAdapted outputs

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Matchmaking

Keyword-based Filter

Matched Services

Matchmaker

OutputsFilterService Registry

Service Request

Inputs Filter

Ontologies

Concepts DB

Ontology Processor

Concept Manager

Ontology Cache

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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

1/41/4

1/16

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Evaluation

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65

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85

90

95

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0 10 20 30 40 50 60 70 80 90 100Recall

Pre

cis

ion Proposed Method

Conventional Methods

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Input/Output Matching is not sufficient

• Add and Multiply services both have similar signatures:

• We need to find what services actually do.

AddInteger

IntegerInteger

MultiplyInteger

IntegerInteger