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Personalized Information Retrieval in a Semantic-Based Learning Environment

Personalized Information Retrieval in a Semantic-Based Learning Environment

Antonella Carbonaro, Rodolfo Ferrini
Copyright: © 2009 |Pages: 20
ISBN13: 9781605660349|ISBN10: 1605660345|ISBN13 Softcover: 9781616925512|EISBN13: 9781605660356
DOI: 10.4018/978-1-60566-034-9.ch018
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MLA

Carbonaro, Antonella, and Rodolfo Ferrini. "Personalized Information Retrieval in a Semantic-Based Learning Environment." Semantic Knowledge Management: An Ontology-Based Framework, edited by Antonio Zilli, et al., IGI Global, 2009, pp. 370-389. https://doi.org/10.4018/978-1-60566-034-9.ch018

APA

Carbonaro, A. & Ferrini, R. (2009). Personalized Information Retrieval in a Semantic-Based Learning Environment. In A. Zilli, E. Damiani, P. Ceravolo, A. Corallo, & G. Elia (Eds.), Semantic Knowledge Management: An Ontology-Based Framework (pp. 370-389). IGI Global. https://doi.org/10.4018/978-1-60566-034-9.ch018

Chicago

Carbonaro, Antonella, and Rodolfo Ferrini. "Personalized Information Retrieval in a Semantic-Based Learning Environment." In Semantic Knowledge Management: An Ontology-Based Framework, edited by Antonio Zilli, et al., 370-389. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-034-9.ch018

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Abstract

Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently construct knowledge from information sources. Personalized and customizable access on digital materials collected from the Web according to one’s own personal requirements and interests is an example of active learning. Moreover, it is also necessary to provide techniques to locate suitable materials. In this chapter, we introduce a personalized learning environment providing intelligent support to achieve the expectations of active learning. The system exploits collaborative and semantic approaches to extract concepts from documents, and maintaining user and resources profiles based on domain ontologies. In such a way, the retrieval phase takes advantage of the common knowledge base used to extract useful knowledge and produces personalized views of the learning system.

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