OPTIMUM ONLINE KNOWLEDGE BASE

OPTIMUM ONLINE KNOWLEDGE BASE

Abstract:
As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing 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.

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:

 The topic coverage of TREC profiles was limited.
 The TREC user profiles had good precision but relatively poor recall performance.
 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.
1) World knowledge is commonsense knowledge acquired by people from experience and education
2) 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:

 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.
 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.
 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.

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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