Mining in Ontology with Multi Agent System in Semantic Web : A Novel Approach

The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.5, October 2014
MINING IN ONTOLOGY WITH MULTI AGENT
SYSTEM IN SEMANTIC WEB : A NOVEL
APPROACH
Vishal Jain1 and Dr. S. V. A. V. Prasad2
1Research Scholar, Lingaya’s University, Faridabad, Haryana, INDIA
2Professor, Electronics and Communication Engineering Department, Lingaya’s
University, Faridabad, Haryana, INDIA
ABSTRACT
A large amount of data is present on the web. It contains huge number of web pages and to find suitable
information from them is very cumbersome task. There is need to organize data in formal manner so that
user can easily access and use them. To retrieve information from documents, there are many Information
Retrieval (IR) techniques. Current IR techniques are not so advanced that they can be able to exploit
semantic knowledge within documents and give precise results. IR technology is major factor responsible
for handling annotations in Semantic Web (SW) languages. With the rate of growth of web and huge
amount of information available on the web which may be in unstructured, semi structured or structured
form, it has become increasingly difficult to identify the relevant pieces of information on the internet. IR
technology is major factor responsible for handling annotations in Semantic Web (SW) languages.
Knowledgeable representation languages are used for retrieving information. So, there is need to build an
ontology that uses well defined methodology and process of developing ontology is called Ontology
Development. Secondly, Cloud computing and data mining have become famous phenomena in the current
application of information technology. With the changing trends and emerging of the new concept in the
information technology sector, data mining and knowledge discovery have proved to be of significant
importance. Data mining can be defined as the process of extracting data or information from a database
which is not explicitly defined by the database and can be used to come up with generalized conclusions
based on the trends obtained from the data. A database may be described as a collection of formerly
structured data. Multi agents data mining may be defined as the use of various agents cooperatively
interact with the environment to achieve a specified objective. Multi agents will always act on behalf of
users and will coordinate, cooperate, negotiate and exchange data with each other. An agent would
basically refer to a software agent, a robot or a human being Knowledge discovery can be defined as the
process of critically searching large collections of data with the aim of coming up with patterns that can be
used to make generalized conclusions. These patterns are sometimes referred to as knowledge about the
data. Cloud computing can be defined as the delivery of computing services in which shared resources,
information and software’s are provided over a network, for example, the information super highway.
Cloud computing is normally provided over a web based service which hosts all the resources required. As,
the knowledge mining is used in many fields of study such as in science and medicine, finance, education,
manufacturing and commerce. In this paper, the Semantic Web addresses the first part of this challenge by
trying to make the data also machine understandable in the form of Ontology, while Multi-Agent addresses
the second part by semi-automatically extracting the useful knowledge hidden in these data, and making it
available.
DOI : 10.5121/ijma.2014.6504 45

46
KEYWORDS
Information Retrieval, Semantic Web, Ontology, Ontology Mapping, Indexing with Ontology, JADE, Multi-
Agent Systems.
1. INTRODUCTION
A large amount of data is present on the web. It contains huge number of web pages and to find
suitable information from them is very cumbersome task. There is need to organize data in formal
manner so that user can easily access and use them. To retrieve information from documents,
there are many Information Retrieval (IR) techniques. Current IR techniques are not so advanced
that they can be able to exploit semantic knowledge within documents and give precise results.
IR technology is major factor responsible for handling annotations in Semantic Web (SW)
languages [2]. With the rate of growth of web and huge amount of information available on the
web which may be in unstructured, semi structured or structured form, it has become increasingly
difficult to identify the relevant pieces of information on the internet. The need is to organize this
data in a formal system which results in more relevant end useful and structured information. So,
it has become necessary for users to utilize automated tools in finding the desired information
resources, and to track and analyze their navigation patterns. In this context, the role of user
modelling and personalized information access is increasing. Ontology may be a mechanism for
obtaining the information on web in a more structured way through Semantic Web. This work
focuses on the problem of choosing a representation of documents that can be suitable to induce
concept-based user profiles as well as to support a content-based retrieval process. It is at this
juncture that the science of Agent Mining comes to rescue. The Semantic Web addresses the first
part of this challenge by trying to make the data also machine understandable in the form of
Ontology, while Multi-Agent addresses the second part by semi-automatically extracting the
useful knowledge hidden in these data, and making it available. As current web 2.0 is shifting
towards web 3.0, there is need to upgrade technologies according to the same.
Figure 1: Current Web to Semantic Web

47
2. LITERATURE SURVEY
Anarosa Alves, Franco Brandão, Viviane Torres da Silva, Carlos José Pereira de Lucena [10],
presented a work on a model driven approach to develop multi-Agent Systems. In this paper,
authors described a model driven approach to develop multi-agent systems that begins with an
ontology based on the TAO conceptual framework.
Quynh-Nhu Numi Tran, Graham Low [11], worked on MOBMAS: A methodology for ontology-based
multi-agent systems development. In this authors, authors proposed a new framework and
compared MOBMAS against sixteen well known methodologies: MaSE, MASSIVE , SODA,
GAIA, MESSAGE , Methodology for BDI Agent, INGENIAS, Methodology with High-Level
and Intermediate Levels, Methodology for Enterprise Integration, PROMETHEUS, PASSI,
ADELFE, COMOMAS , MAS-Common, KADS , CASSIOPEIA and TROPOS.
Pakornpong Pothipruk and Pattarachai Lalitrojwong [12], worked on an Ontology-based Multi-agent
System for Matchmaking. Csongor Nyulas, Martin J. O’Connor, Samson Tu1, David L.
Buckeridge, Anna Akhmatovskaia, Mark A. Musen [13], presented their work on An Ontology-
Driven Framework for Deploying JADE Agent Systems. Authors described a methodology and
suite of tools to support the modeling and deployment of agents on the JADE platform. These
models are encoded using the Semantic Web ontology language OWL and provide detailed
computer-interpretable specifications of agent behavior in a JADE system.
Gajun Ganendran, Quynh-Nhu Tran, Pronab Ganguly, Pradeep Ray and Graham Low [14],
proposed a methodology on An Ontology-driven Multi-agent approach for Healthcare. In this
paper, authors described an ontology-driven multi-agent approach to the development of
healthcare systems, with a case study in diabetes management.
Wongthongtham, P., Chang, E., Dillon, T.S. [15], worked on Ontology-based Multi-agent system
to Multi-site Software Development. Authors described software agent utilized ontology as its
intelligence in MSSD and found that it has benefits. Ontology gives computers more knowledge
that the agent can utilize
Maja Hadzic, Elizabeth Chang [16], worked on use of ontology-based multi-agent systems in the
biomedical domain. Authors have shown how the ontologies can be used by multi-agent systems
in intelligent information retrieval processes. The ontologies can be used to support some
important processes involved in the information retrieval such as posing queries by the user,
problem decomposition and task sharing among different agents, result sharing and analysis,
information selection and integration, and structured presentation of the assembled information to
the user.
3. PROBLEM DEFINITION
Internet is considered as repository of collection of web documents. There is plethora of
documents available on web are very difficult to access and extract relevant information from
them. The reason is uncertainty of huge documents that confuse users by providing several
keywords and results in context of given query. The documents may be unstructured, structured
and semi-structured. It is known that mostly of these documents are unstructured. Although there
is larger number of documents residing on web storage house, still the users are unable to find

relevant information about given domain. The reason is the uncertainty of documents that creates
users in dilemma by providing hundreds of results and keywords in response to given query.
When a user wants to find some information, he/she enters a query and results are produced via
hyperlinks linked to various documents available on web. But the information that is retrieved to
us may or may not be relevant. This irrelevance is caused due to huge collection of documents
available on web. Traditional search engines are based on keyword based searching that is unable
to transform raw data into knowledgeable representation data. It is a cumbersome task to extract
relevant information from large collection of web documents. These shortcomings have led to the
concept of Semantic Web and Ontology into existence.
48
4. INFORMATION RETRIEVAL
Information Retrieval involves identifying and extracting relevant pages containing that specific
information according to predefined guidelines. There are many IR techniques for extracting
keywords like NLP based extraction techniques which are used to search for simple keywords.
Then we have Aero Text system for text extraction of key phrases from text documents.
Information Retrieval, retrieve only structured data. But IR provides all types of information
access such as analysis, organization, storage, searching, retrieval of information and structured
data. As according to Slaton’s classic textbook: “Information retrieval is a field concerned with
the structured, analysis, organization, storage, searching, and retrieval of information”.
Information retrieval is defined by Carlo Meghini et al. “Information Retrieval as the task of
identifying documents in a collection on the basis of properties described to the documents by the
user requesting the retrieval” . Information retrieval is a process of retrieval-structured data from
the unstructured data.
5. SEMANTIC WEB
The idea of Semantic Web (SW) as envisioned by Tim Bermers Lee came into existence in 1996
with the aim to translate given information into machine understandable form [7]. The Semantic
Web (SW) is an extension of current www in which documents are filled by annotations in
machine understandable markup language. It is defined as framework of expressing information
to develop various languages and approaches for increasing IR effectiveness. Semantic Web uses
documents called Semantic Web Documents (SWD’s) that are written in SW languages like
OWL, DAML+OIL. SW is an XML (Extensible Markup Language) application. Its aim is to
maintain coordination between users and software agents so that they can find answers to their
queries clearly.
6. ONTOLOGY
The term ontology can be defined in many different ways. Genersereth and Nilsson defined
Ontology as an explicit specification of a set of objects, concepts, and other entities that are
presumed to exist in some area of interest and the relationships that hold them [8]. Ontology
defines a common vocabulary for researchers who need to share common information in a
domain”. It enables the Web for software components can be ideally supported through the use of
Semantic Web technologies . This helps in understanding the concepts of the domain as well as
helps the machine to interpret the definitions of concepts in the domains and also the relations
between them.

Ontologies can be broadly divided into two main types: lightweight and heavyweight.
Lightweight Ontologies involve taxonomy (or class hierarchy) that contains classes, subclasses,
attributes and values. Heavy weight Ontologies model domains in a deeper way and include
axioms and constraints. Ontology layer consists of hierarchical distribution of important concepts
in the domain and describing about the Ontology concepts, relationships and constraints. Figure
1.9 displays the Ontology and its Constituents parts.
49
Figure 1: “Ontology and its Constituents [3]”
7. MULTI AGENT SYSTEM
Multi-agent systems have been successful in the distributed implementation of KM processes.
Knowledge acquisition agents have been one of the most successful applications of software
agents, specifically in the Internet, where knowledge-collector agents operate within available
information resources and validate them in accordance with the users' interests. On the other
hand, knowledge transfer lies in an end-to-end routing of knowledge that is generated by some
actor, and it is another typical task that has been realized by software agents. Therefore, it is
reasonable to approach the multi-agent paradigm for knowledge production. Knowledge-producing
agents need to do formulations in keeping with a validation scheme that supports the
knowledge construction.
Multi-agent systems can support the coordinated interaction needed to achieve an agreement on
the knowledge that is eventually generated. They can also support the validation scheme.
Advances in multi-agent systems as an alternative way to build distributed systems have made
agents a facilitator of human-computer interaction. Agents have been proven as helpful tools for
the coordination of people who are performing a given task. Agent interaction protocols govern
the exchange of a series of messages among agents, in accordance with the interaction style
among actors.
The interaction styles of individual agents can be competitive, cooperative or negotiating. But,
both the group behavior and the authoring of knowledge objects are social, group-level issues,
more than individual subjects, and it is there where multi-agent systems can give their

contribution. The architecture presented in this work is a bottom-up, multi-agent approach for
knowledge production. Our working hypothesis is that a group of agents can help in the
collaborative production of knowledge by coordinating their creation activities. Therefore,
different agents can act as representatives of knowledge-producing actors, according to the
following principles:
50
• Agents can be structured into separable knowledge domains of interaction. This structuring
reflects the knowledge differences between developers.
• A dynamic rethinking of the structure of interactions in different domains can help to reduce
conflicts during.
8. PROPOSED SOLUTION
Semantic Web is a well defined portal that helps in extracting relevant information using many
Information Retrieval techniques. Current Information Retrieval techniques are not so advanced
that they can be able to exploit semantic knowledge within documents and give precise result.
The terms, Information Retrieval, Semantic Web and Ontology are used differently but they are
interconnected with each other. Information Retrieval technology and Web based Indexing
contributes to existence of Semantic Web. Use of Ontology also contributes in building new
generation of web- Semantic Web. With the help of ontologies, make content of web as it will be
markup with the help of Semantic Web documents can be possible. Ontology is considered as
backbone of Software system. It improves understanding between concepts used in Semantic
Web. So, there is need to build an ontology that uses well defined methodology and process of
developing ontology is called Ontology Development. Ontology can be used to build structured
data and Multi-Agent system for information extraction from the ontology database. In order to
realize with Multi Agent Systems and interact with their agents, a framework is developed that is
called JADE (Java Agent Development Environment). It is considered as middleware that implies
agent platform and development framework.
The study of the related works and literature review that in order to retrieve meaningful
information from the data warehouse through the help of a multi-agent system and data mining
techniques in a cloud computing environment, the following architecture is designed [20]:The
Infrastructure-as-a-Service (IaaS) provides a virtual environment with storage and network
without having physical hardware. Therefore, Infrastructure cloud computing provides a data
warehouse for storage of data for further analysis. The user will submit a flat-text based request
on the IaaS for information retrieval from its integrated data warehouse that has gathered data
from numerous areas of business to present to the user a wide variety to choose from. The IaaS
will forward this request to the MAS to find the information as requested. However, MAS does
not have the ability to find the information that has large amounts of data from a data warehouse.
Therefore, it will use the data mining algorithm to analyze the large amount of data from the data
warehouse that is residing in the IaaS.As a pre-processing stage, the MAS will first develop a
target data set which will be large enough to contain all the possible data patterns and send it into
he system. Then the processing will begin where the data will be analyzed through anomaly
detection, clustering, classification, regression and summarization. The result of the process will
be shown on the screen to the user.

Figure 2: “Information Retrieval through Multi-Agent System with Data Mining in Cloud Computing” [20]
51
9. TECHNOLOGY STACK
1. OWL: Web ontology languageis used here to represent the university domain
including, Universities, Courses, People, Library and Materials.
2. Protégé: A free, open-source ontology editor and framework for building intelligent
systems
3. OWLIM: OWLIM is a family of semantic repositories, or RDF database management
systems, with the following characteristics:
a. native RDF engines, implemented in Java
b. delivering full performance through both Sesame and Jena
c. robust support for the semantics of RDFS, OWL 2 RL and OWL 2 QL
d. best scalability, loading and query evaluation performance
4. WEKA - Apriori Algorithm: Weka is a collection of machine learning algorithms for
solving real-world data mining problems. It is written in Java and runs on almost any
platform. The Apriori Algorithm is an influential algorithm for mining frequent itemsets
for boolean association rules.
5. Empire: Empire provides Java developers an easy way to integrate and start using
SPARQL & RDF via JPA persistence annotations.
6. JADE: Java Agent DEvelopment Framework, or JADE, is a software for the
development of agents, implemented in Java. JADE system supports coordination
between several agents FIPA and provides a standard implementation of the
communication language FIPA-ACL, which facilitates the communication between
agents and allows the services detection of the system

52
Figure 3: “Propose Framework”
10. CONCLUSION
The information retrieval practical model through the multi-agent system with data mining in a
cloud computing environment has been proposed. It is however, recommended that users should
ensure that the request made to the IaaS is within the scope of integrated data warehouse and is
clear and simple. Thus, making the work for the multi-agent system easier through application of
the data mining algorithms to retrieve meaningful information from the data warehouse. In this
proposed research model/architecture, the use of cloud computing allows the users to retrieve
meaningful information from virtually integrated data warehouse that reduces the costs of
infrastructure and storage. In this paper, new proposed model has been proposed; “Mining in
Ontology with Multi Agent Systems” has been discussed. In this model, the Semantic Web
addresses the first part of this challenge by trying to make the data also machine understandable
in the form of Ontology, while Multi-Agent addresses the second part by semi-automatically
extracting the useful knowledge hidden in these data, and making it available. This model can be
developed, which may give better results as compare to RDBMS. This paper will also helpful to
other researchers, who would like to do work in this area.
ACKNOWLEDGEMENTS
I, Vishal Jain, would like to give my thanks to Dr. M. N. Hoda, Director, Bharati Vidyapeeth’s
Institute of Computer Applications and Management (BVICAM), New Delhi, for giving me
opportunity to do Ph.D from Linagay’s University, Faridabad, Haryana.
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by Jaypee University of Information Technology, Waknaghat, Shimla, Himachal Pradesh, INDIA
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AUTHORS
Vishal Jain has completed his M.Tech (CSE) from USIT, Guru Gobind Singh
Indraprastha University, Delhi and doing P.hD from Computer Science and Engineering
Department, Lingaya’s University, Faridabad. Presently he is working as Assistant
Professor in Bharati Vidyapeeth’s Institute of Computer Applications and Management,
(BVICAM), New Delhi. His research area includes Web Technology, Semantic Web and
Information Retrieval. He is also associated with CSI, ISTE.
Dr. S. V. A. V. Prasad has completed M.Tech., Ph.D. Presently working as professor and
Dean (Academic Affairs), Dean (R&D, IC), Dean School of Electrical Sciences. Sir has
actively participated and organized many refresher courses, seminars, workshops on
ISO, ROHS, Component Technology, WEEE, Organizational Methods, Time Study,
Productivity enhancement and product feasibility etc. Sir has developed various products
like 15 MHz dual Oscilloscope, High Voltage Tester, VHF Wattmeter, Standard Signal,
Generator with AM/FM Modulator, Wireless Becom, High power audio Amplifier, Wireless microphone
and many more in the span of 25 years (1981 – 2007). Sir has awarded for excellence in R&D in year 1999,
2004 and National Quality Award during the Year 1999, 2000, 2004 and 2006.Sir is Fellow member of
IEEE and life member of ISTE, IETE, and Society of Audio & Video System. Sir has published more than
90 research papers in various National & International conferences and journals. Sir research area includes
Wireless Communication, Satellite Communication & Acoustic, Antenna, Neural Networks, and Artificial
Intelligence.

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