Learning Personalized Ontologies from Text: A Review on an Inherently Transdisciplinary Area

Learning Personalized Ontologies from Text: A Review on an Inherently Transdisciplinary Area

Shan Chen (University of Technology, Sydney, Australia) and Mary-Anne Williams (University of Technology, Sydney, Australia)
DOI: 10.4018/978-1-59904-510-8.ch001
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Abstract

Ontology learning has been identified as an inherently transdisciplinary area. Personalized ontology learning for Web personalization involves Web technologies and therefore presents more challenges. This chapter presents a review of the main concepts of ontologies and the state of the art in the area of ontology learning from text. It provides an overview of Web personalization, and identifies issues and describes approaches for learning personalized ontologies. The goal of this survey is—through the study of the main concepts, existing methods, and practices of the area—to identify new connections with other areas for the future success of establishing principles for this new transdisciplinary area. As a result, the chapter is concluded by presenting a number of possible future research directions.

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