Follow us on Facebook

Header Ads

Semantics-Based Automated Service Discovery


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