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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1324

Title: A concept–relationship acquisition and inference approach for hierarchical taxonomy construction from tags
Authors: Tsui, Eric
Wang, Wai Ming
Cheung, Chi-fai Benny
Lau, Sau-mui Adela
Subjects: Collaborative tagging
Folksonomy
Natural language processing
Knowledge capture
Semantic web
Issue Date: 25-Jun-2009
Publisher: Elsevier
Citation: Information processing & management, 2009, Article in Press, available online 25 June 2009.
Abstract: Taxonomy construction is a resource-demanding, top–down, and time consuming effort. It does not always cater for the prevailing context of the captured information. This paper proposes a novel approach to automatically convert tags into a hierarchical taxonomy. Folksonomy describes the process by which many users add metadata in the form of keywords or tags to shared content. Using folksonomy as a knowledge source for nominating tags, the proposed method first converts the tags into a hierarchy. This serves to harness a core set of taxonomy terms; the generated hierarchical structure facilitates users’ information navigation behavior and permits personalizations. Newly acquired tags are then progressively integrated into a taxonomy in a largely automated way to complete the taxonomy creation process. Common taxonomy construction techniques are based on 3 main approaches: clustering, lexico-syntactic pattern matching, and automatic acquisition from machine-readable dictionaries. In contrast to these prevailing approaches, this paper proposes a taxonomy construction analysis based on heuristic rules and deep syntactic analysis. The proposed method requires only a relatively small corpus to create a preliminary taxonomy. The approach has been evaluated using an expert-defined taxonomy in the environmental protection domain and encouraging results were yielded.
Description: DOI: 10.1016/j.ipm.2009.05.009
Rights: Information Processing & Management Copyright © 2009 Elsevier Ltd. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1324
ISSN: 0306-4573
Appears in Collections:ISE Journal/Magazine Articles
SN Journal/Magazine Articles

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