31-01-2013, 10:24 AM
A PERSONALIZED ONTOLOGY MODEL FOR WEB INFORMATION GATHERING USING LOCAL INSTANCE REPOSITORY
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ABSTRACT
Ontology describes a standardized representation of knowledge as a set of concepts within the domain, and
the relationship between those concepts. Ontology is also used to represent user profiles in personalized
web information gathering. For representing user profiles many models have been developed, these models
provide knowledge from either a global or local knowledge base. The global analysis uses existing global
knowledge bases and to produce effective performance. The local analysis observes user behavior in user
profiles. The user background knowledge can be better discovered and represented if we integrate global
and local analysis. Our model proposes to bring out data from global resources depending on the user
whose profiles match the global content. Compared with other models ontology model has an edge. The
LGSM(Local Global Search Methodology) is used for calculating the hit/miss ratio.
INTRODUCTION
Over the last decade, we have witnessed
an explosive growth in the information available
on the Web gathering useful information from
the web has become a challenging issue for
users. The Web users expect more intelligent
systems (or agents) to gather the useful
information from the huge size of Web related
data sources to meet their information needs. The
user profiles are created for user background
knowledge description [1],[2],[3].
RELATED WORK
Ontology Learning
Ontology learning is also known as
ontology extraction and is a subtask of
information extraction. Information extraction
(IE) is a type of information retrieval whose goal
is to automatically extract structured information
from unstructured and/or semi-structured
extract relevant concepts and relations from a
given corpus or other kinds of data sets to form
ontology.
PERSONALIZED ONTOLOGY
CONSTRUCTION
Personalized ontologies that
formally describe and specifies user background
knowledge. For example a user searching for a
word might have different expectations, for
searching the same query. For example if we are
searching for the term “New Jersey”, business
travelers may expect different search from
leisure travelers. A user may become a business
traveler when planning for a business trip, or a
leisure traveler when planning for a family
holiday. A user’s concept model may change
according to different information needs.
Global Knowledge Representation
World Knowledge representation
research involves analysis of how to accurately
and effectively reason and how best to use a set
of symbols to represent a set of facts within a
knowledge domain. In this model user
background knowledge is extracted from a world
knowledge base encoded from the Library of
Congress Subject Headings (LCSH).
RESULTS
The experiments were designed to
compare the results generated by ontology model
and the baseline (category) model. The
comparison is modeled as an graph in Fig.9. In
Ontology model the local profile is used as an aid
to search which can bring out precise information
based on the user’s profile.
CONCLUSION AND FUTURE WORK
In this paper, an Ontology model is proposed for
representing user background knowledge for
personalized web information gathering. This
model constructs the global search from the
world knowledge base and local search from
local instance repository. This model is
compared with the baseline model. In this we
found that the combination of local and global
works in a better way. In addition, Ontology
model using both is-a and part-of is an
advantage. In this ontology model, performing
both local and global search provides a better
solution.