ABSTRACT:
As a model for knowledge description and formalization, ontology’s are
widely used to represent user profiles in personalized web information
gathering. However, user profiles, many models have utilized only knowledge
from either a global knowledge base or user local information. In this paper, a
personalized ontology model is proposed for knowledge representation and
reasoning over user profiles. This model learns ontological user profiles from
both a world knowledge base and user local instance repositories. The ontology
model is evaluated by comparing it against benchmark models in web information
gathering. The results show that this ontology model is successful.
ARCHITECTURE:
ALGORITHM:
EXISTING SYSTEM:
1. GOLDEN MODEL: TREC
MODEL:
The TREC model was used to demonstrate
the interviewing user profiles, which reflected user concept models perfectly.
For each topic, TREC users were given a set of documents to read and judged
each as relevant or nonrelevant to the topic. The TREC user profiles perfectly
reflected the users’ personal interests, as the relevant judgments were
provided by the same people who created the topics as well, following the fact
that only users know their interests and preferences perfectly.
2. BASELINE MODEL:
CATEGORY MODEL:
This model demonstrated the
noninterviewing user profiles, a user’s interests and preferences are described
by a set of weighted subjects learned from the user’s browsing history. These
subjects are specified with the semantic relations of superclass and subclass
in an ontology. When an OBIWAN agent receives the search results for a given
topic, it filters and reranks the results based on their semantic similarity
with the subjects. The similar documents are awarded and reranked higher on the
result list.
3. BASELINE MODEL: WEB
MODEL:
The web model was the implementation of
typical semi interviewing user profiles. It acquired user profiles from the web
by employing a web search engine. The feature terms referred to the interesting
concepts of the topic. The noisy terms referred to the paradoxical or ambiguous
concepts.
DISADVANTAGES:
Ø The
topic coverage of TREC profiles was limited.
Ø The
TREC user profiles had good precision but relatively poor recall performance.
Ø Using
web documents for training sets has one severe drawback: web information has
much noise and uncertainties. As a result, the web user profiles were
satisfactory in terms of recall, but weak in terms of precision. There was no
negative training set generated by this model
PROPOSED SYSTEM:
The
world knowledge and a user’s local instance repository (LIR) are used in the
proposed model.
v
World knowledge is
commonsense knowledge acquired by people from experience and education
v
An LIR is a user’s
personal collection of information items. From a world knowledge base, we
construct personalized ontologies by adopting user feedback on interesting
knowledge. A multidimensional ontology mining method, Specificity and
Exhaustivity, is also introduced in the proposed model for analyzing concepts
specified in ontologies. The users’ LIRs are then used to discover background
knowledge and to populate the personalized ontologies.
ADVANTAGES:
Ø Compared
with the TREC model, the Ontology model had better recall but relatively weaker
precision performance. The Ontology model discovered user background knowledge
from user local instance repositories, rather than documents read and judged by
users. Thus, the Ontology user profiles were not as precise as the TREC user
profiles.
Ø The
Ontology profiles had broad topic coverage. The substantial coverage of
possibly-related topics was gained from the use of the WKB and the large number
of training documents.
Ø Compared
to the web data used by the web model, the LIRs used by the Ontology model were
controlled and contained less uncertainties. Additionally, a large number of
uncertainties were eliminated when user background knowledge was discovered. As
a result, the user profiles acquired by the Ontology model performed better
than the web model.
MODULES:
WORLD
KNOWLEDGE BASE:
The world knowledge base must cover an
exhaustive range of topics, since users may come from different backgrounds.
The structure of the world knowledge base used in this research is encoded from
the LCSH references.
The LCSH system
contains three types of references:
v
Broader term- The BT
references are for two subjects describing the same topic, but at different
levels of abstraction (or specificity). In our model, they are encoded as the
is-a relations in the world knowledge base.
v
Used-for- The UF
references in the LCSH are used for many semantic situations, including
broadening the semantic extent of a subject and describing compound subjects
and subjects subdivided by other topics. When object A is used for an action,
becomes a part of that action (e.g., “a fork is used for dining”); when A is
used for another object, B, A becomes a part of B (e.g., “a wheel is used for a
car”). These cases can be encoded as the part-of relations.
v
Related term- The RT
references are for two subjects related in some manner other than by hierarchy.
They are encoded as the related-to relations in our world knowledge base.
ONTOLOGY LEARNING
ENVIRONMENT:
The subjects of user interest are
extracted from the WKB via user interaction. A tool called Ontology Learning
Environment (OLE) is developed to assist users with such interaction. Regarding
a topic, the interesting subjects consist of two sets: positive subjects are
the concepts relevant to the information need, and negative subjects are the
concepts resolving paradoxical or ambiguous interpretation of the information
need. Thus, for a given topic, the OLE provides users with a set of candidates to
identify positive and negative subjects. These candidate subjects are extracted
from the WKB. Who are not fed back as either positive or negative from the
user, become the neutral subjects to the given topic.
ONTOLOGY MINING:
Ontology mining discovers interesting
and on-topic knowledge from the concepts, semantic relations, and instances in
ontology. Ontology mining method is introduced: Specificity and Exhaustivity.
Specificity (denoted spe) describes a subject’s focus on a given topic.
Exhaustivity (denoted exh) restricts a subject’s semantic space dealing with
the topic. This method aims to investigate the subjects and the strength of
their associations in ontology. In User Local Instance Repository, User
background knowledge can be discovered from user local information collections,
such as a user’s stored documents, browsed web pages, and composed/received
emails.
SYSTEM
REQUIREMENTS:
HARDWARE REQUIREMENTS:
Processor : Intel Duel Core.
Hard Disk :
60 GB.
Floppy Drive : 1.44
Mb.
Monitor : LCD Colour.
Mouse : Optical Mouse.
RAM : 512 Mb.
SOFTWARE
REQUIREMENTS:
Operating system :
Windows XP.
Coding Language : ASP.Net with C#
Data Base : SQL
Server 2005
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