Personalized Ontology Model for Web Information Gathering

Personalized Ontology Model for Web Information Gathering

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.Personalized Ontology Model for Web Information Gathering

Hardware Requirements

• System            : Pentium IV 2.4 GHz.
• Hard Disk         : 40 GB.
• Floppy Drive             : 1.44 Mb.
• Monitor             :  15 VGA Colour.
• Mouse             : Logitech.
• Ram                 :512 Mb.

Software Requirements

• Operating system             : Windows XP.
• Coding Language             : ASP.Net with C#
• Data Base                           : SQL Server 2005    

Existing System:

• 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 exhaustively, 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.