Thursday, December 27, 2012
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IEEE Java project - Bootstrapping Ontologies for Web Services
Bootstrapping
Ontologies for Web Services
ABSTRACT:
Ontologies have become the de-facto
modeling tool of choice, employed in many applications and prominently in the semantic
web. Nevertheless, ontology construction remains a daunting task. Ontological bootstrapping,
which aims at automatically generating concepts and their relations in a given
domain, is a promising technique for ontology construction. Bootstrapping an ontology
based on a set of predefined textual sources, such as web services, must address
the problem of multiple, largely unrelated concepts. In this paper, we propose
an ontology bootstrapping process for web services. We exploit the advantage
that web services usually consist of both WSDL and free text descriptors. The
WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse
Document Frequency (TF/IDF) and web context generation. Our proposed ontology
bootstrapping process integrates the results of both methods and applies a
third method to validate the concepts using the service free text descriptor, thereby
offering a more accurate definition of ontologies. We extensively validated our
bootstrapping method using a large repository of real-world web services and
verified the results against existing ontologies. The experimental results
indicate high precision. Furthermore, the recall versus precision comparison of
the results when each method is separately implemented presents the advantage
of our integrated bootstrapping approach.
Architecture:
AIM:
To develop an Ontological bootstrapping which aims
at automatically generating concepts and their relations in a given domain is a
promising technique for ontology construction. Bootstrapping an ontology based
on a set of predefined textual sources, such as Web services, must address the
problem of multiple, largely unrelated concepts.
EXISTING SYSTEM:
Ontology creation and evolution and in particular on
schema matching. Many heuristics were proposed for the automatic matching of
schema and several theoretical models were proposed to represent various
aspects of the matching process such as representation of mappings between Ontologies.
However, all the methodologies described require comparison between existing Ontologies.
DISADVANTAGES OF EXISTING SYSTEM:
·
Previous work on
ontology bootstrapping focused on either a limited domain or expanding an
existing ontology.
·
UDDI registries have
some major flaws. In particular, UDDI registries either are publicly available and
contain many obsolete entries or require registration that limits access. In
either case, a registry only stores a limited description of the available
services.
PROPOSED SYSTEM:
The ontology bootstrapping process is based on
analyzing a Web service using three different methods, where each method
represents a different perspective of viewing the Web service. As a result, the
process provides a more accurate definition of the ontology and yields better
results. In particular, the Term Frequency/ Inverse Document Frequency (TF/IDF)
method analyzes the Web service from an internal point of view, i.e., what
concept in the text best describes the WSDL document content. The Web Context
Extraction method describes the WSDL document from an external point of view,
i.e., what most common concept represents the answers to the Web search queries
based on the WSDL content. Finally, the Free Text Description Verification
method is used to resolve inconsistencies with the current ontology.
ADVANTAGES
OF PROPOSED SYSTEM:
The web service ontology
bootstrapping process proposed in this paper is based on the advantage that a
web service can be separated into two types of descriptions:
1) The Web Service Description
Language (WSDL) describing “how” the service should be used and
2) A textual description of the web
service in free text describing “what” the service does. This advantage allows
bootstrapping the ontology based on WSDL and verifying the process based on the
web service free text descriptor.
MODULES:
·
Data Extraction
·
Token Extraction
·
Term Frequency/IDF
Analysis
·
Web context extraction
·
Ontology Evolution
MODULES DESCRIPTION:
Data Extraction:
In
this module we develop the data extraction process using Whois. Whois is a Web
service that allows domain details to be identified by based on the domain name
.It maintains a web services related with operations and services.
Token Extraction:
In
this module we develop the token extraction process using WSDL (Web Service
Description Language). WSDL document with the token list bolded. The extracted
token list serves as a baseline. These tokens are extracted from the WSDL
document of a Web service Whois. The service is used as an initial step in our
example in building the ontology. Additional services will be used later to
illustrate the process of expanding the ontology.
Term Frequency/IDF Analysis:
Term
Frequency/Inverse Document Frequency analysis is made in this module. TF/IDF is
applied here to the WSDL descriptors. By building an independent corpus for
each document, irrelevant terms are more distinct and can be thrown away with a
higher confidence. To formally define TF/IDF, we start by defining frequency as
the number of occurrences of the token within the document descriptor.
Web context extraction:
In this module, we develop the web context
extraction process. Where, the Web pages clustering algorithm is based on the
concise all pairs profiling (CAPP) clustering method. This method approximates
profiling of large classifications. It compares all classes’ pair wise and then
minimizes the total number of features required to guarantee that each pair of
classes is contrasted by at least one feature.
Ontology Evolution:
Ontology
evolution is the last module where, the descriptor is further validated using
the textual service descriptor. The analysis is based on the advantage that a
Web service can be separated into two descriptions: the WSDL description and a
textual description of the Web service in free text. The WSDL descriptor is
analyzed to extract the context descriptors and possible concepts as described.
CONCLUSION:
In
this project we propose an approach for bootstrapping an ontology based on Web
service descriptions. The approach is based on analyzing Web services from
multiple perspectives and integrating the results. Our approach takes advantage
of the fact that Web services usually consist of both WSDL and free text
descriptors.
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:
Aviv Segev, and Quan Z. Sheng,
“Bootstrapping Ontologies for Web Services”, IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. 5, NO. 1, JANUARY-MARCH
2012.
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