Semantics-Based
Automated Service Discovery
ABSTRACT:
A vast majority of web services exist
without explicit associated semantic descriptions. As a result many services
that are relevant to a specific user service request may not be considered
during service discovery. In this paper, we address the issue of web service
discovery given nonexplicit service description semantics that match a specific
service request. Our approach to semanticbased web service discovery involves
semantic-based service categorization and semantic enhancement of the service
request. We propose a solution for achieving functional level service
categorization based on an ontology framework. Additionally, we utilize clustering
for accurately classifying the web services based on service functionality. The
semantic-based categorization is performed offline at the universal description
discovery and integration (UDDI). The semantic enhancement of the service
request achieves a better matching with relevant services. The service request
enhancement involves expansion of additional terms (retrieved from ontology)
that are deemed relevant for the requested functionality. An efficient matching
of the enhanced service request with the retrieved service descriptions is
achieved utilizing Latent Semantic Indexing (LSI). Our experimental results
validate the effectiveness and feasibility of the proposed approach.
ARCHITECTURE:
EXISTING
SYSTEM:
A majority of the
current approaches for web service discovery call for semantic web services
that have semantic tagged descriptions through various approaches, e.g., OWL-S,
Web Services Description Language (WSDL)-S. However, these approaches have
several limitations. First, it is impractical to expect all new services to
have semantic tagged descriptions. Second, descriptions of the vast majority of
already existing web services are specified using WSDL and do not have
associated semantics. Also, from the service requestor’s perspective, the
requestor may not be aware of all the knowledge that constitutes the domain.
Specifically, the service requestor may not be aware of all the terms related
to the service request. As a result of which many services relevant to the
request may not be considered in the service discovery process.
Existing service discovery approaches
often adopt keyword-matching technologies to locate the published web services.
This syntax-based matchmaking returns discovery results that may not accurately
match the given service request. As a result, only a few services that are an
exact syntactical match of the service request may be considered for selection.
Thus, the discovery process is also constrained by its dependence on human
intervention for choosing the appropriate service based on its semantics.
DISADVANTAGES
OF EXISTING SYSTEM:
Given the large number of web services
and the distribution of similar services in multiple categories in the existing
UDDI infrastructure, it is difficult to find services that satisfy the desired
functionality.
Such service discovery may involve
searching a large number of categories to find appropriate services. Therefore,
there is a need to categorize web services based on their functional semantics
rather than based on the classifications of service providers.
PROPOSED
SYSTEM:
The limitations of existing approaches, an
integrated approach needs to be developed for addressing the two major issues
related to automated service discovery: 1) semantic-based categorization of web
services; and 2) selection of services based on semantic service description
rather than syntactic keyword matching. Moreover, the approach needs to be
generic and should not be tied to a specific description language. Thus, any
given web service could be described using WSDL, OWL-S, or through other means.
Semantic-based categorization of web services is performed at the UDDI that
involves semantics augmented /classification of web services into functional
categories. The semantically related web services are grouped together even
though they may be published under different categories within the UDDI.
Service selection then consists of two key steps: 1) parameters-based service
refinement; and 2) semantic similarity-based matching.
In order to address the limitations of existing
approaches, an integrated approach needs to be developed for addressing the two
major issues related to automated service discovery: 1) semantic-based
categorization of web services; and 2) selection of services based on semantic
service description rather than syntactic keyword matching.
In this paper, we present a novel approach for
semantic based automated service discovery. Specifically, the proposed approach
focuses on semantic-based service categorization and selection as depicted in
Fig. 1. In our proposed approach, semantic-based categorization of web services
is performed at the UDDI that involves semantics augmented classification of
web services into functional categories.
MODULES:
1.
User Registration.
2.
Service Categorization.
3.
Service Refinement.
4.
Semantic Matching.
MODULES DESCRIPTION:
User Registration
This module
explains the design and implementation of user registration via web based
services. This module wills also communication established between client and
web based service.
Service Categorization
The semantic
categorization of UDDI wherein we combine ontologies with an established
hierarchical clustering methodology, following the service description vector
building process. For each term in the service description vector, a
corresponding concept is located in the relevant ontology. If there is a match,
the concept is added to the description vector. Additional concepts are added
and irrelevant terms are deleted based on semantic relationships between the
concepts. The resulting set of service descriptions is clustered based on the
relationship between the ontology concepts and service description terms.
Finally, the relevant semantic information is added to the UDDI for effective
service categorization.
Service Refinement
The next step is
service selection from the relevant category of services using parameter-based
service refinement. Web service parameters, i.e., input, output, and
description, aid service refinement through narrowing the set of appropriate
services matching the service request. The relationship between web service
input and output parameters may be represented as statistical associations.
These associations relay information about the operation parameters that are
frequently associated with each other. To group web service input and output
parameters into meaningful associations, we apply a hyperclique pattern
discovery. These associations combined with the semantic relevance are then
leveraged to discover and rank web services.
Semantic Matching
The
parameter-based refined set of web services is then matched against an enhanced
service request as part of Semantic Similarity-based Matching. A key part of
this process involves enhancing the service request. Our approach for web
semantic similarity-based service selection employs ontology-based request
enhancement and LSI based service matching. The basic idea of the proposed
approach is to enhance the service request with relevant ontology terms and
then find the similarity measure of the semantically enhanced service request
with the web service description vectors generated in the service refinement
phase.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
•
System : Pentium IV 2.4 GHz.
•
Hard
Disk : 40 GB.
•
Floppy
Drive : 1.44 Mb.
•
Monitor : 15 VGA Colour.
•
Mouse : Logitech.
•
Ram : 512 Mb.
SOFTWARE
REQUIREMENTS:
•
Operating system : - Windows XP.
•
Coding Language : J2EE
•
Data Base : MYSQL
REFERENCE:
Aabhas V. Paliwal, Student Member, IEEE,
Basit Shafiq, Member, IEEE, Jaideep Vaidya, Member, IEEE, Hui Xiong, Senior
Member, IEEE, and Nabil Adam, Senior Member, IEEE, “Semantics-Based Automated Service
Discovery”, IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. 5, NO. 2, APRIL-JUNE
2012
