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

TitleA concept-relationship acquisition and inference approach for hierarchical taxonomy construction from tags
Authors
KeywordsCollaborative tagging
Folksonomy
Knowledge capture
Natural language processing
Semantic web
Issue Date2010
Citation
Information Processing and Management, 2010, v. 46, n. 1, p. 44-57 How to Cite?
AbstractTaxonomy 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. © 2009 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/335192
ISSN
2023 Impact Factor: 7.4
2023 SCImago Journal Rankings: 2.134
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorTsui, Eric-
dc.contributor.authorWang, W. M.-
dc.contributor.authorCheung, C. F.-
dc.contributor.authorLau, Adela S.M.-
dc.date.accessioned2023-11-17T08:23:49Z-
dc.date.available2023-11-17T08:23:49Z-
dc.date.issued2010-
dc.identifier.citationInformation Processing and Management, 2010, v. 46, n. 1, p. 44-57-
dc.identifier.issn0306-4573-
dc.identifier.urihttp://hdl.handle.net/10722/335192-
dc.description.abstractTaxonomy 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. © 2009 Elsevier Ltd. All rights reserved.-
dc.languageeng-
dc.relation.ispartofInformation Processing and Management-
dc.subjectCollaborative tagging-
dc.subjectFolksonomy-
dc.subjectKnowledge capture-
dc.subjectNatural language processing-
dc.subjectSemantic web-
dc.titleA concept-relationship acquisition and inference approach for hierarchical taxonomy construction from tags-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ipm.2009.05.009-
dc.identifier.scopuseid_2-s2.0-70349991301-
dc.identifier.volume46-
dc.identifier.issue1-
dc.identifier.spage44-
dc.identifier.epage57-
dc.identifier.isiWOS:000271709400004-

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