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

Yue Hong Eric Tsui, W. M. Wang, Chi Fai Cheung, Adela S.M. Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)44-57
Number of pages14
JournalInformation Processing and Management
Issue number1
Publication statusPublished - 1 Jan 2010


  • Collaborative tagging
  • Folksonomy
  • Knowledge capture
  • Natural language processing
  • Semantic web

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Computer Science Applications
  • Management Science and Operations Research
  • Library and Information Sciences


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