A survey on ontology based web personalization

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 10 | Oct-2013, Available @ http://www.ijret.org 163
A SURVEY ON ONTOLOGY BASED WEB PERSONALIZATION
Kiran Jammalamadaka1
, I V Srinivas2
1
Research Scholar, Computer Science, K L University, Andhra Pradesh, India, jvskkiran@gmail.com
2
Asst. Professor, Computer Science, K.J.SIMSR, Maharashtra, India, srinivasvj@gmail.com
Abstract
Over the last decade the data on World Wide Web has been growing in an exponential manner. According to Google the data is
accelerating with a speed of billion pages per day [24]. Internet has around 2 million users accessing the World Wide Web for
various information [25].These numbers certainly raise a severe concern over information over load challenges for the users. Many
researchers have been working to overcome the challenge with web personalization, many researchers are looking at ontology based
web personalization as an answer to the information overload, as each individual is unique. In this paper we present an overview of
ontology based web personalization, Challenges and a survey of the work. This paper also points future work in web personalization.
Index Terms: Web Personalization, Ontology, User modeling, web usage mining.
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1. WEB PERSONALIZATION
Today, internet has become a part and parcel of our lives and
one cannot imagine a world without internet, everyday
millions of people use internet for various purposes mostly for
information. And user is often not happy due the amount of
information he has been provided with, as the user needs
further filtering, which is very time consuming and expects the
system to understand his/her thoughts. Understanding user is
not as simple as it’s said, and web personalization is one step
towards to the goal.
Web personalization is the process of personalizing the
content as per the user or set of users, taking advantage of the
knowledge acquired through the analysis of the user’s
navigational behavior [1].
Web personalization can be done in the following methods
[17].
•Implicit: Implicit personalization will be performed by the
system/web page based on the user behavior on the web
•Explicit: User will be able to modify the system using the
features provided by the system itself.
•Hybrid: combination of the above.
1.1 Web Usage Mining In Personalization:
Web Usage Mining is a subset of web mining which in turn a
sub set of Data mining. Web mining has three categories
Fig1.
Web usage mining can be defined as automatic discovery of
user profiles. The goal of web usage mining has been to
support decision making process of website owners to
understand the user in a better way. However, these techniques
can be used for personalization functions.
Web usage mining process consists of three major phases
Fig2
Web usage mining is heavily dependent on the click stream
data which gets generated automatically by the application
servers in the form of logs.

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1.2 Need for Web Personalization
Considering the amount of data and variety of users on the
World Wide Web, key word based search results may not
serve the purpose of providing the relevant information to the
user, as each users’ intention is different and the same may not
reflect in the key words they use.
Because of the above reasons web personalization has
attracted many researchers to look into and provide a
mechanism to understand the user in a better way and provide
most relevant information to the user.
User may not have time to fill in the data (method Explicit)
describing about his/her interests, likes, dislikes, background
educational qualification etc.
Many web mining researchers worked on the above challenge
and provided a few techniques for automatic personalization,
the best example till date was Amazon where user need not
give his/her details the system will fetch the relevant
information to the users.
The below diagram explains overview of personalization
techniques. [2].
Fig 3
Web personalization serves the purpose up to some extent by
understanding the user however, relevant results cannot be
provided to the user With-out understanding the context of the
user. In the whole web personalization process, understanding
the user and the context is the crux; towards it user modeling
would be first step
1.3 User Modeling
Assume a user is searching for a book title called “My
experiments with truth”, then the user might be interested in
autobiographies, in such case the personalized system will
recommend many autobiographies to the user which could be
apt for the user. However the user may not be an avid reader
of autobiographies and the same user may get irritated by the
very personalized system over the time. To avoid such
scenarios the system needs to understand the user in a better
way, certainly keywords without the context would not be
much helpful.
The field of user modeling has more than 35 years of research
by now, User modeling has evolved from labs to commercial
service models. ALFRED KOBSA [21]. in his paper reviewed
the development of generic user modeling systems over the
past twenty years. It described their purposes, services within
user-adaptive systems, and the different design requirements
for research prototypes and commercially deployed servers.
The core idea of adaptation is based on the assumption that
differences in some user characteristics should influence the
individual utility of the service/information provided; hence if
system’s behavior is tailored according to these
characteristics, the system value will be increased. Some
adaptive systems store individual information only for a single
characteristic, others model users along multiple dimensions.
[22].
A personalized system is said to be an adaptive system if the
system is tuning itself to the requirement of the user, some
personalized systems use domain knowledge which is
Ontology.
Ontology techniques will help user modeling in a better way.
2. ONOTOLOGY
Ontology is a formal description and specification of
knowledge. It provides a common understanding of topics to
be communicated between users and systems [8].
As defined by Thomas R. Gruber as Ontology is "an explicit
specification of a conceptualization”. A conceptualization
consists of a set of entities (such as objects and concepts) that
may be used to express knowledge and relationships [7].
Developing a Ontology includes

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Fig4
Ontologies have been proven an effective means for modeling
digital collections and user context. Ontologies in the form of
hierarchies of user interests have been proposed [11].
This ontology-based user modeling system integrates three
ontologies:
• User ontology: It includes different characteristics of users
and their relationships.
• Domain ontology: It captures the domain or application
specific concepts and their relationships.
• Log ontology: It represents the semantics of the user
interaction with the system. [8].
The personalized ontology can describe different concept
models for different users, although they may have the same
topic.
Ontology is based on two kinds of knowledge:
2.1 World Knowledge:
World knowledge covering large number of topics so that the
user’s individual information needs can be best match
2.2 Expert Knowledge:
Expert knowledge is the kind of knowledge classified by the
people who hold expertise in that domain. [9]. Ontologies are
ever growing, constantly ontology repositories needs to be
updated with the latest click stream data.
Many ways in which ontology can be useful for user modeling
have been discussed [23]. The approach is based on a semantic
representation of the user activity, which takes into account
both the structure of visited sites and the way the user
navigates them.
The most conventional implementation of ontology-based user
modeling is the overlay user modeling that relies on a domain
model represented as ontology. A simplified view on ontology
is a network of concepts. [22].
Now days, Researchers identified the benefits of adapting of
the Ontologies in user modeling.
3. SURVEYS
Recently the need for adaptive systems has been recognized
and the research has been geared up on user profiling and
context. More sophisticated personalised web experiences
have been provided with web mining techniques combined
with Ontology.
Robal and Kalja [12], discussed the possibilities of applying
ontology’s in exploring the web sites’ structures and usage for
producing various recommendations for the visitors.
Content based system analyses item descriptions to identify
items that are of particular interest to the user. Pazzani and
Billsus [13] Discussed Content-based recommendation
systems
Kearney, Anand, & Shapcott [14], investigates how web
visitor usage data may be combined with semantic domain
knowledge to provide a deeper understanding of user
behaviour.
Seth and Zhang [15], focus on news associated content and
suggest the design of a social network based recommender
system for this rationale
Staab & Studer [16] ,formally defined an ontology as a 4-tuple
of a set of concepts ,a set of relations, a set of instances and a
set of axioms.
A context-aware system has to infer which context the user is
in a given moment in time, and consequently adapt the system
to that context [17].
The profiles are constructed using a variety of learning
techniques including the vector space model Genetic
algorithms, the probabilistic model or clustering.
CASTELLS, Miriam FERNÁNDEZ, and David VALLET,
Discussed about using Vector –space model for Ontology
based information retrieval. [18].
C´elia da Costa Pereira1 and Andrea G. B. Tettamanzi,
Discussed user profiling using genetic algorithms [19]
Chen, Q. proposed a neural network approach to user
modeling in the context of information retrieval [20].
4. CHALLENGES
4.1 Accuracy of the User Profile:
The biggest ever challenge is to build user model implicitly
and accurately, as user may behave differently in each session
some time it’s difficult to get a clear co relation of the

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navigation behavior. Reducing noise from the data collected is
also a challenge.
4.2 Time Taking Process:
User profile can be built over a period of time, until the system
gets a minimum level of data, it may not provide the
recommendations till such level.
4.3 Predicting & Updating the Users’ Model:
Next challenge is predicting and updating the users’ behavior,
a constant learning system needs to be in place.
4.4 Performance:
Too much filtering can cause a performance issue which in
turn leads the user to lose the interest.
4.5 Scalability:
As the systems are dependent on Click stream data, log data
etc., the systems should be capable enough to handle such
large volumes of data. Though the processing can be done
offline handling huge data is a challenge.
4.6 Privacy:
User may not be comfortable in providing/allowing to capture
his/her navigational data, it’s a serious privacy issue and
government imposed many law restrictions on this, thus the
systems may not get needed data from the user, Systems
should be helping the users rather than spying.
4.7 User Model Interoperability:
When the same user works with multiple adaptive systems,
each system maintains its own recommendation system for the
user, however it depends up on the quality of information each
system has. The integration of these systems can definitely
serve the user in a better way However; this task involves
several important issues including privacy, security, trust user
identity etc. [22].
4.8 Identifying Web Robots:
Web robots are also known as crawlers or spiders, which
automatically traverse through the hyperlinks. Major challenge
with web robots is that they hide their identity behind the user,
and this may affect the user’s actual behavior, hence while
building the user model the data generated might have the web
robots’ influence.
CONCLUSIONS
The purpose of this survey has been to describe the state-of-
the-art of personalized recommender systems using ontology
techniques. In this paper, we have presented a comprehensive
description about various web personalizations using ontology
in the recent years. Though lot of research has been going on
in this field, yet a system that effectively integrated various
diverse requirements of the users has not yet been proposed.
FUTURE WORK
Future work in web personalization includes the in depth study
on fusion of ontology and web mining techniques for effective
web personalization. And how the user profiles evolves with
time.
This survey also identified a few areas to be explored like
learning techniques including the vector space model, Genetic
algorithms, and the probabilistic model or clustering in the
field of web personalization. Integrating the systems like
social networking and blogs and other popular websites is also
a potential area to explore.
Owing to the spread of mobile devices in the current era, web
personalization needs to be explored on the mobile arena as
well.
REFERENCES
[1]. [EV03] M. Eirinaki, M. Vazirgiannis, “Web Mining for
Web Personalization”,
ACM Transactions on Internet Technology, February 2003
[2]. Elizabeth Kulin, Rahul & Naureddien, “Web
personalization Presentation” [Slides 5 & 18].
[3]. Chhavi Rana, “Trends in Web Mining for
Personalization”, IJCST Vol. 3, Issue 1, Jan. - March 2012 ,
ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print).
[4]. Sergey Sosnovsky, “Ontological Technologies for User
Modelling”, 2008
[5]. PLABAN KUMAR BHOWMICK, SUDESHNA
SARKAR, ANUPAM BASU “ONTOLOGY BASED USER
MODELING FOR PERSONALIZED INFORMATION
ACCESS”, 2010
[6]. Mobasher, B.,“Data Mining for Web Personalization”.
[7]. Gruber, T., “Toward Principles for the Design of
Ontologies Used for Knowledge sharing”. 1993.
[8]. K. Curran, C. Murphy, and S. Annesley, ” Web
intelligence in information retrieval”. In Proc. of WI’ 03,
pages 409 – 412, 2003.
[9]. Xiaohui Tao, Yuefeng Li, Ning Zhong, Richi Nayak ,
“Ontology Mining for Personalized Web Information
Gathering”
[10].Natalya F. Noy and Deborah L. McGuinness, “Ontology
Development 101: A Guide to Creating Your First Ontology”
[11]. Joana Trajkova, Susan Gauch,” Improving Ontology-
Based User Profiles “
[12]. Tarmo Robal, Ahto Kalja, ”Applying User Profile
Ontology for Mining Web Site Adaptation Recommendations”
[13]. J. Pazzani and Daniel Billsus, “Content-Based
Recommendation Systems Michael “

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[14]. Sarabjot Singh Anand, Mary Shapcott, “Employing a
domain ontology to gain insights into user behaviour Patricia
Kearney†”
[15]. Aaditeshwar Seth and Jie Zhang, “A Social Network
Based Approach to Personalized Recommendation of
Participatory Media Content”
[16]. S. Staab and S. R., editors. “Handbook on Ontologies”
[17]. E. Rich, “Users are individuals: individualizing user
models”. Int. J. Man-Machine Studies (1983) 18, 199-214
[18]. Pablo CASTELLS, Miriam FERNÁNDEZ, and David
VALLET, “An Adaptation of the Vector-Space Model for
Ontology-Based Information Retrieval”
[19]. C´elia da Costa Pereira1 and Andrea G. B. Tettamanzi,
“An Ontology-Based Method for User Model Acquisition”
[20]. Chen, Q, “A neural network approach for user
modeling”.
[21]. ALFRED KOBSA, “Generic User Modeling Systems”
[22]. Sergey Sosnovsky, “Ontological Technologies for User
Modeling”
[23]. Hui Zhang, Yu Song, and Han-tao Song, “Construction
of Ontology-Based User Model for Web Personalization”
[24]. http://www.joop.in/Archive/the-world-wide-web-grows-
a-billion-pages-per-day/
[25]. http://www.internetworldstats.com/stats.htm
BIOGRAPHIES
Kiran Jammalamadaka received his MCA
from Acharya Nagarjuna University,
India. He is currently associated with GE
India. As a Software technical leader His
Interests include Software engineering,
web personalization, Agile->Scrum and
Automated debugging
I.V. Srinivas received his MCA from
Acharya Nagarjuna University, India. He
is currently associated with K. J. Somaiya
Institute of Management Studies and
Research, As a Asst. Professor. His
interests include Distributed Computing,
Software engineering and web
personalization

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