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Matchmaking of Semantic Web Services Using Semantic-Distance

Information

Mehmet Şenvar, Ayşe Bener

Boğaziçi University Department of Computer Engineering

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OUTLINE Introduction

Matchmaking

Related Work Concepts

Ontologies, UDDI,...

Matching Details Algorithms

Simulation & Results Conclusion and Future Work

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Introduction

Use of Web Services Semantic Web Matchmaking Properties of matchmaking process

Extendable, efficient,general..

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State of the Art Discovery

Provides non-semantic search

Keyword and attribute-based

match Search retrieves lot of services (irrelevant results

included)

UDDI Business Registry

Which service to select ? How to select?

Search

Results

Selection

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Discovery Arhitectures

Web Service Discovery Architectures Matchmaking Brokerage Peer-to-Peer (P-2-P)

Matchmaking is the process of finding an appropriate provider for a requestor through a middle agent

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Related Work LARKS

ITLsyntactic and semantic matchingRepresentation

Input-output

Ian Horrocks and Lei Lui’s architecturebased on DAML-S ontologyDescription Logic reasoner

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Background

OntologiesConceptsFormalizationsShared VocabularyRelations

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Background

UDDIAn open frameworkWeb Services RegistryKeyword searchAPI usageLocal usage available

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Background

OWL-SOWLWeb Service descriptionsSemanticsProperties

presents describedBy supportedBy

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Problems in Current Semantic Discovery Solutions

Set-based returned result to service requestor mostly Ontological information is not fully used User preferences and ordering choices of cannot be

defined in search Threshold appliance rather than result size filtering Elimination of any mismatch case

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PROPOSED FRAMEWORK Motivation and Goal

To provide a semantic web service discovery framework based on currently accepted technologies in a simple and effective manner

Return discovered services in an ordered and rated set Allowing users to define their view-of-world concepts and search

preferences Use this information, named as semantic-distance, in

matchmaking process

Question : I am interested in tehchnology books and more on computer books than electronic books.How to define?

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Proposed Hybrid Architecture

Hybrid Architecture

UDDI

SuperPeers SuperPeers

UDDIUDDIConsumer Producer Consumer Producer

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Service Category Based Distribution

High level service ontology is defined and services are distributed to UDDI registries according to this classification

Finacial Services

Banking Services Retirement Services

UDDI

Payment Services EFT Services

UDDI UDDI

UDDI

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Matching Algorithm

Layered structure

Extendable with plug-ins

Based on subsumption relation and Semantic Distance information mainly

Partial Result Set concept

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Definition of Semantic Distance

How user/agent view relation of concepts

Reflect perspective of agent on ontologic concepts

Weight assignment to subClassOf relation of concepts

Semantic Weight /Distance = (parent-class, sub-class, similarity-weight)

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Setting Semantic Weights

Case I Assignment is done by local users/agents on local/global

ontologies

Case II Assignment is done on the global ontology by the

Ontology Designer

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Matchmaking

Matching inputs – outputs Match levels exact > plug-in > subsume > fail

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Matchmaking Assigning values for matching types

Exact =1Plug-in = 0.8Subsume = 0.5Fail =0.

Level of matchMinimum of the set of matches for inputs

and outputs

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General Concepts of Similarity Subsumption is determination of subconcept and

superconcept relationships between concepts of a given ontology

More generel concept called subsumer and more specific concept the subsumee

Vehicle

Car

Sedan

Vehicle

Car

Sedan

Case I Case II

S :Searched For

S

S

Vehicle

Car

Sedan

Case III

S

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General Concepts of Similarity II Axiom I : Most strongest match is where advertised

concept match with the requested concept exactly.

Axiom II : For the search result concepts under the target concept, the one that is upper in the ontologic representation is preferred.

Axiom III : For the concepts over the target concept, the one that is closer to the searched concept which is in the lower part of the ontologic representation is chosen.

Vehicle

Car

Sedan

Vehicle

Car

Sedan

Vehicle

Car

Sedan

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Semantic Distance Weight Assignment

the rate of coverage of sub-concepts for each concept in relation to subClassOf.

done by sub-ontology managers Representation:

a tuple relation : SD = (parent_concept, subclass_concept,

similarity)

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MatchmakingAlgorithm Service Requestor

Serv. Req

(owl-s)

Sem. Dist.

File(*.sd)

+

Serv. Adv.

(owl-s)

Inp

ut F

ilterin

g

Ou

tpu

t F

ilterin

g

Pre

/Po

st Co

n

Filte

ring

Se

rvice C

at.

Filte

ring

MS-MatchMaker

Ma

ximu

m R

esu

lt S

ize

Plu

g-in

Filte

rs

Service Provider

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Algorithms

Concept/Domain matching Input/Output matching Pre/Post condition matching Add-Value matching Level of Filtering Applied Maximum Result Size

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Sample Weight Assignments on Ontologies

For sample scenarios and test cases following ontology and semantic distance assigments are used

Press

Book

TechnologyBooks HistoryBooks

Computer Electronics

Pre Middle Close

1

1/21/2

1/2 1/2

1/3 1/31/3

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Simulation Scenario 1

Search for : input : Price output : ComputerBooks

Computer engineering student, mostly interested in computer books.It is not a strict rule given and open to other types of books offer and I have some preferences on these kind of books

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Simulation Scenario 2

Given Price, return list of Electronics and Pre(Histroy) books

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Scenario 3

100 web services registered in the matchmaker 10 of them related with the context of

BuyBookService, others not related maximum result set size to 5

No other constraints given Strict matching Assume 8 services still match -> top 5 returned

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Comparasion with other Matchmakers

Framework LARKS OWL-S Matchmaker

Lei Lui’s Framework

MS-Matchmaker

Language ITL OWL-S DAML-S OWL-S

Repository Local KB UDDI UDDI UDDI

Service Category Filter

x x x

Input Filter x x x x

Output Filter x x x x

Pre/post Condition Filter

x x x x

Plug-in Filter x x

Semantic Dist. Usage

partial x

Ranked List x x

Type Based List x x x x

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Conclusion

A novel web semantic web service discovery framework is proposed with sematic distance information usage

Ranking of services is realized using ontological parent-child relations

Layered, extandable, simple matching algorithm A new Partial Result Set concept introduced

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Future Work

Quality of services can be integrated Similarity concept can be widened to properties,

constraints etc. Mediation can be analized an integrated in a detail

manner Complex ontologies, services, scenarios are required

to validate the evaluation of semantic distance information usage

Performance and security can be integrated to the framework