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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: © 2008 |Pages: 19
ISBN13: 9781599045436|ISBN10: 1599045435|EISBN13: 9781599045450
DOI: 10.4018/978-1-59904-543-6.ch014
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MLA

Carbonaro, Antonella, and Rodolfo Ferrini. "Personalized Information Retrieval in a Semantic-based Learning Environment." Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively, edited by Dion Goh and Schubert Foo, IGI Global, 2008, pp. 270-288. https://doi.org/10.4018/978-1-59904-543-6.ch014

APA

Carbonaro, A. & Ferrini, R. (2008). Personalized Information Retrieval in a Semantic-based Learning Environment. In D. Goh & S. Foo (Eds.), Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively (pp. 270-288). IGI Global. https://doi.org/10.4018/978-1-59904-543-6.ch014

Chicago

Carbonaro, Antonella, and Rodolfo Ferrini. "Personalized Information Retrieval in a Semantic-based Learning Environment." In Social Information Retrieval Systems: Emerging Technologies and Applications for Searching the Web Effectively, edited by Dion Goh and Schubert Foo, 270-288. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-543-6.ch014

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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 paper, 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 from the common knowledge base used to extract useful knowledge and produces personalized views of the learning system.

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