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Using Wikipedia Content to Derive an Ontology for Modeling and Recommending Web Pages across Systems

By Pei-chia Chang and Luz M. Quiroga

Abstract

In this work, we are building a cross-system recommender at the client side that uses the Wikipedia’s content to derive an ontology for content and user modeling. We speculate the collaborative content of Wikipedia may cover many of the topical areas that people are generally interested in and the vocabulary may be closer to the general public users and updated sooner. Using the Wikipedia derived ontology as a shared platform to model web pages also addresses the issue of cross system recommendations, which generally requires a unified protocol or a mediator. Preliminary tests of our system may indicate that our derived ontology is a fair content model that maps an unknown webpage to its related topical categories. Once page topics can be identified, user models are formulated through analyzing usage pages. Eventually, we will formally evaluate the topicality-based user mode

Topics: User Modeling, Wikipedia, Management, Measurement, Documentation, Design, Experimentation. Keywords Recommender, Agent, User Modeling, Ontology
Year: 2012
OAI identifier: oai:CiteSeerX.psu:10.1.1.212.2745
Provided by: CiteSeerX
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