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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
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Author(s): Chiraz Trabelsi (University Tunis El-Manar, Tunisia), Bilel Moulahi (University Tunis El-Manar, Tunisia) and Sadok Ben Yahia (University Tunis El-Manar, Tunisia)
Copyright: 2012
Pages: 23
Source title: Collaboration and the Semantic Web: Social Networks, Knowledge Networks, and Knowledge Resources
Source Author(s)/Editor(s): Stefan Brüggemann (Astrium Space Transportation, Germany) and Claudia d’Amato (University of Bari, Italy)
DOI: 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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