A concept–relationship acquisition and inference approach for hierarchical taxonomy construction from tags

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 wh...

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Bibliographic Details
Published in:Information processing & management 2010, Vol.46 (1), p.44-57
Main Authors: Tsui, Eric, Wang, W.M., Cheung, C.F., Lau, Adela S.M.
Format: Article
Language:English
Subjects:
Acquisitions
Automatic classification
Classification
Collaborative tagging
Construction
Construction equipment
Documentary languages. Terminology
Exact sciences and technology
Folksonomy
Harnesses
Inference
Information and communication sciences
Information management
Information processing and retrieval
Information science. Documentation
Knowledge capture
Logical and linguistic tools
Marketing
Metadata
Natural language processing
Sciences and techniques of general use
Semantic web
Studies
Tagging
Tags
Taxonomy
Terminology. Lexicons. Thesaurus
Vocabularies & taxonomies
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Summary: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.
ISSN:0306-4573
1873-5371
DOI:10.1016/j.ipm.2009.05.009