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Hidden Markov Models for Context-Aware Tag Query Prediction in Folksonomies

Hidden Markov Models for Context-Aware Tag Query Prediction in Folksonomies

Chiraz Trabelsi, Bilel Moulahi, Sadok Ben Yahia
Copyright: © 2012 |Pages: 23
ISBN13: 9781466608948|ISBN10: 1466608943|EISBN13: 9781466608955
DOI: 10.4018/978-1-4666-0894-8.ch010
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MLA

Trabelsi, Chiraz, et al. "Hidden Markov Models for Context-Aware Tag Query Prediction in Folksonomies." Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources, edited by Stefan Brüggemann and Claudia d’Amato, IGI Global, 2012, pp. 168-190. https://doi.org/10.4018/978-1-4666-0894-8.ch010

APA

Trabelsi, C., Moulahi, B., & Ben Yahia, S. (2012). Hidden Markov Models for Context-Aware Tag Query Prediction in Folksonomies. In S. Brüggemann & C. d’Amato (Eds.), Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources (pp. 168-190). IGI Global. https://doi.org/10.4018/978-1-4666-0894-8.ch010

Chicago

Trabelsi, Chiraz, Bilel Moulahi, and Sadok Ben Yahia. "Hidden Markov Models for Context-Aware Tag Query Prediction in Folksonomies." In Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources, edited by Stefan Brüggemann and Claudia d’Amato, 168-190. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-0894-8.ch010

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Abstract

Recently, social bookmarking systems have received surging attention in academic and industrial communities. In fact, social bookmarking systems share with the Semantic Web vision the idea of facilitating the collaborative organization and sharing of knowledge on the web. The reason for the apparent success of the upcoming tools for resource sharing (social bookmarking systems, photo sharing systems, etc.) lies mainly in the fact that no specific skills are needed for publishing and editing, and an immediate benefit is yielded to each individual user, e.g., organizing one’s bookmarks in a browser-independent, persistent fashion, without too much overhead. As these systems grow larger, however, the users address the need of enhanced search facilities. Today, full-text search is supported, but the results are usually simply listed decreasingly by their upload date. The challenging research issue is, therefore, the development of a suitable prediction framework to support users in effectively retrieving the resources matching their real search intents. The primary focus of this chapter is to propose a new, context aware tag query prediction approach. Specifically, the authors adopted Hidden Markov Models and formal concept analysis to predict users’ search intentions based on a real folksonomy. Carried out experiments emphasize the relevance of the proposal and open many issues.

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