30-06-2012, 02:46 PM
A Personalized Ontology Model for Web Information Gathering
A Personalized Ontology .doc (Size: 3.39 MB / Downloads: 911)
INTRODUCTION
The amount of web-based information available has increased dramatically. How to gather useful information from the web has become a challenging issue for users. Current web information gathering systems attempt to satisfy user requirements by capturing their information needs. For this purpose, user profiles are created for user background knowledge description User profiles represent the concept models possessed by users when gathering web information. A concept model is implicitly possessed by users and is generated from their background knowledge. While this concept model cannot be proven in laboratories, many web ontologists have observed it in user behavior . When users read through a document, they can easily determine whether or not it is of their interest or relevance to them, a judgment that arises from their implicit concept models. If a user’s concept model can be simulated, then a superior representation of user profiles can be built. To simulate user concept models, ontology a knowledge description and formalization model—are utilized in personalized web information gathering. Such ontologies are called ontological user profiles or personalized ontologies .
Purpose
An ontology model to evaluate this hypothesis is proposed. This model simulates users’ concept models by using personalized ontologies, and attempts to improve web information gathering performance by using ontological user profiles. The world knowledge and a user’s local instance repository (LIR) are used in the proposed model. World knowledge is commonsense knowledge acquired by people from experience and education; 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. The proposed ontology model is evaluated by comparison against some benchmark models through experiments using a large standard data set. The evaluation results show that the proposed ontology model is successful.
Motivation
This paper presents the extensive work of, but significantly beyond, an earlier paper published in WI ’07. The authors thank the Library of Congress and QUT Library for the use of the LCSH and library catalogs. The authors also thank the anonymous reviewers for their valuable comments. Thanks also go to M. Carey-Smith, P. Delaney, and J. Beale, for their assistance in proofreading and editing the paper.
Definitions
The proposed ontology model was evaluated by objective experiments. Because it is difficult to compare two sets of knowledge in different representations, the principal design of the evaluation was to compare the effectiveness of an information gathering system (IGS) that used different sets of user background knowledge for information gathering. The knowledge discovered by the ontology model was first used for a run of information gathering, and then the
knowledge manually specified by users was used for another run.