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1
Matchmaking of Semantic Web Services Using Semantic-Distance
Information
Mehmet Şenvar, Ayşe Bener
Boğaziçi University Department of Computer Engineering
2
OUTLINE Introduction
Matchmaking
Related Work Concepts
Ontologies, UDDI,...
Matching Details Algorithms
Simulation & Results Conclusion and Future Work
3
Introduction
Use of Web Services Semantic Web Matchmaking Properties of matchmaking process
Extendable, efficient,general..
4
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
5
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
6
Related Work LARKS
ITLsyntactic and semantic matchingRepresentation
Input-output
Ian Horrocks and Lei Lui’s architecturebased on DAML-S ontologyDescription Logic reasoner
7
Background
OntologiesConceptsFormalizationsShared VocabularyRelations
8
Background
UDDIAn open frameworkWeb Services RegistryKeyword searchAPI usageLocal usage available
9
Background
OWL-SOWLWeb Service descriptionsSemanticsProperties
presents describedBy supportedBy
10
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
11
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?
12
Proposed Hybrid Architecture
Hybrid Architecture
UDDI
SuperPeers SuperPeers
UDDIUDDIConsumer Producer Consumer Producer
13
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
14
Matching Algorithm
Layered structure
Extendable with plug-ins
Based on subsumption relation and Semantic Distance information mainly
Partial Result Set concept
15
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)
16
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
17
Matchmaking
Matching inputs – outputs Match levels exact > plug-in > subsume > fail
18
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
19
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
20
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
21
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)
22
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
23
Algorithms
Concept/Domain matching Input/Output matching Pre/Post condition matching Add-Value matching Level of Filtering Applied Maximum Result Size
24
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
25
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
26
Simulation Scenario 2
Given Price, return list of Electronics and Pre(Histroy) books
27
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
28
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
29
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
30
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