2012 International Conference on Software and Computer Applications (ICSCA 2012)
IPCSIT vol. 41 (2012) © (2012) IACSIT Press, Singapore
Information Retrieval using Ontology based Authentication for
Personal Identification
Lt.Dr.S Santhosh Baboo, P Shanmuga Priya
1 Assistant Professor, Department of Computer Science, D.G.Vaishnav College, Chennai, TamilNadu, India,
600 106, santhos2001@sify.com
2 Research Scholar, Department of Computer Science, D.G.Vaishnav College, Chennai, TamilNadu,
India,600 106, shanmusekar@gmail.com
Abstract-The main objective of this paper is to impart Integration of Information effectively from
various databases to retrieve reliable information about an individual. This can be achieved by
using the concept of Ontology since enormous data has to be processed for extracting information.
This is an important challenge for answering queries. We propose an Ontology Builder for
creating ontologies for various domains and an ontology search AUI frame work for authenticating
the reliable information. As ontology is a source of knowledge for a particular domain which
links to ontologies of related domains, the search can give a refined set of reliable information
about an individual.
Keywords: Ontology Builder, Ontology Search, Ontology Identifier, Reliability Refiner
1. INTRODUCTION
In Artificial Intelligence Ontology is the formal, explicit specification of a shared conceptualization
[8]. It is the representation of knowledge as a set of concepts within a domain and the relationships between
those concepts. The main advantages of developing an ontology [1] is
To enable reuse of domain knowledge for various operations
To simplify search from enormous amount of data
To ease changes in domain knowledge [Add or delete Class / Instances]
To improve information organisation and management
To share common understanding of knowledge
The process of building and maintaining ontologies, which is known as ontology engineering presents
unique challenges. These challenges are related to lack of trustworthy and authoritative knowledge
sources and absence of centralised repository to locate ontologies to be reused.
1.1. How to develop an Ontology
Developing an ontology includes
Defining classes in the ontology
Arranging the classes in a hierarchy
Defining properties /attributes for the classes
Describing values / instances for the properties
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Ontology is the explicit and modelled representation of already defined finite set of terms and
concepts involved in knowledge engineering and information integration which provides a vocabulary to
represent and communicate knowledge. Explicit specification means the concept and relations which
have been given explicit names and definitions.
2. REVIEW ON ONTOLOGY
The role of ontology in data integration provides a rich and predefined vocabulary as well as knowledge
representation. The method of building the structure of the ontology has three main stages of building the
shared vocabulary, local ontologies and defining mappings and the ontology construction method [5]
includes analysis of information sources, search for terms and defining the global ontology. The domain
concepts can be represented by the concept category ontology model and the ontology search process for
the concept category strategy is described in the framework of concept finder tool discussed in [3]. The
concept of hybrid approach for ontology integration is based on the IV and MV framework as explained in
[6] considers the integrated view and materialised view to improve query answering time.
3. PROBLEM SCOPE AND APPROACH
The main objective of the paper is to search for the personal information about an individual by
comparing information among various databases to get 99% reliable information using Ontology based
approach. To achieve the result of the search process, create ontology using the Ontology Builder[2] which
builds ontology for all the ways of probabilities of the keywords and stores information in Ontology
Repository. The information queried is taken and compared with the Global Ontology with respect to the
information from Reliability Refiner which in turn is taken from Global Ontology Identifier. Our Global
Ontology Identifier uses the single ontology approach[4] which provides a shared vocabulary for the
specification of semantics.
4. METHODOLOGY
4.1. DESCRIPTION OF THE FRAMEWORK
Our AUI framework deals with the concepts of three main processes namely
Ontology builder (Create Ontology)
Global Ontology Identifier (Identifies to compare with which ontology)
Authenticator (Authenticates the information)
Ontology Picker
The ontology picker selects the keywords from the given query. Keywords if possible can also be
made to choose from options. The selected keyword is passed on to the Ontology Repository.
Ontology Repository
This acts as a repository for the various domain ontologies created by the Ontology Builder based on the
probability of the keyword parameters. The selected information from the repository is passed on to the
Authenticator.
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Ontology Builder
The Ontology Builder is one of the main components in our system. It builds the ontology by
considering the all possible ways of n! probabilities where n denotes the number of parameters of
various domains. Based on the probability, the number of parameters given in the query, the ontologies are
build and stored in the Ontology repository.
Global Ontology Identifier
The Global Ontology component contains the repository of all the necessary databases like Ration card,
UID, Voter ID etc., collected and stored from the state government programs. Since it contains vast
amount of data, the concept of ontology drastically reduces the storage area and also helps to manage the
data more effectively.
Class Extractor
The class extractor extracts the class names of the domains and the instances from the Global Ontology
Identifier which shares the global vocabulary with the Class Extractor. Information regarding the search is
selected and sent to the Authenticator.
Authenticator
Authentication is done in the Authenticator component which compares the information from the
Ontology Repository with the Class Extractor from Global Ontology Identifier with respect to the
information from the Reliability Refiner. This is done based on the Search and Comparison algorithms.
Reliability Refiner
The work of the Reliability Refiner is to make the search more refined since the
Authenticator validates the values with respect to the information from the Reliability Refiner. The
final verified personal information of an individual are sent to the User Interface if desired in a report format.
4.2. THE SEARCH ALGORITHM
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The Algorithm starts by getting user input as keywords and decides which ontology to search and gets
the necessary information from other databases. Both the values are compared and if a match found, lists
all the information in a report form, otherwise refine the search with other sets of data.
5. RESULT ANALYSIS
The AUI methodology when implemented with the algorithm (fig 2), retrieves the personal information
about an individual as given in the expected result (fig 4) as below. The main aim of the paper has
been achieved without the user being unaware of the underlying ontology concepts.
Figure 3 The Search Screen
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Figure 4 The Result Report
6. CONCLUSION AND FUTURE WORK
The current research on data integration uses the concept of ontology for solving the heterogeneity
problems. The AUI framework works in accordance with the algorithm. This paper creates a useful and
practical method for the integration of personal information from various databases based on the single
ontology approach for the user query. The future work includes
1. The implementation of the concepts of the AUI framework
2. Automatically build ontology in ontology builder
3. Provide reliable information about an individual
The work can also be enhanced by new methods which minimize the time consumption and improve
efficiency.
7. REFERENCES
[1] Debajyothi Mukhopadhyay, Aritra Banik, Sreemoyee Mukherjee, Jhilik Bhattacharya, Young-Chon Kim “A
Domain Specific Ontology Based Semantic Web Search Engine”, Cornell University Library Feb 2011.
[2] Abd-Elrahman Elsayed, Samhaa R. El-Beltagy, Mahmoud Rafea, Osman Hegazy, “Applying Data Mining for
Ontology Buiding”, Conference in CLAES 2007.
[3] Wararat Rungworawut, Twittie Senivongse, “Using Ontology Search in the Design of Class Diagram
form Business Process Model”, World Academy of Science, Engineering and Technology 2005.
[4] U. Visser, “Intelligent Information Intelligent for Semantic Web”, vol 3159 of Lecture Notes in AI Heidelberg:
Springer, 1, ed., 2005.
[5] Agustina Buccella, Alejandra Cechich, “An Ontology Approach to data Integration” JCS&T Vol. 3 No.2, Oct
2003.
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[6] Ahmed Alasoud, Volker Haarslev, Nematollaah Shiri, “A Hybrid Approach for Ontology Integration”, 31
VLDB Conference, Norway, 2005.
[7] Gruber T, “A translation approach to portable ontology specifications”, Knowledge Acquisition, Vol.
5, No. 2 1993, pp 199-200.
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