THE USE OF KEYPHRASES FOR SELECTING METADATA FROM TAXONOMIES

Rohana K. Rajapakse, Brian Mushens and Chris Johnson
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Rohana K. Rajapakse: Research and Innovation, University of Plymouth, Plymouth, PL4 8AA, U.K.
Brian Mushens: School of Computing Communications and Electronics, University of Plymouth, Plymouth, PL4 8AA, U.K.
Chris Johnson: School of Computing Communications and Electronics, University of Plymouth, Plymouth, PL4 8AA, U.K.

Chapter 22 in Creating Collaborative Advantage Through Knowledge and Innovation, 2007, pp 329-345 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractThis paper presents a meta-tag suggestion tool that selects topics from a given taxonomy of categories. It works by extracting keyphrases from the textual content of electronic data and using them as entry points to search for appropriate topics in a hierarchically-organized taxonomy. The keyphrase extraction module, based on the Kea system, has the functionality to use prebuilt Kea models or create new Kea models on the fly, thereby providing flexibility in controlling keyphrase extraction. Terms in each keyphrase are stemmed and used as a query to search for appropriate topics in the taxonomy. All taxonomy topics retrieved by repeated searching are considered as candidate meta-tags. They are ranked based on the significance of keyphrase(s) matching the topic, the significance of the type of hit, and the specificity of the matching topic based on its position in the taxonomy.

Keywords: Collaboration Innovation; Knowledge Creation; Relationship; Social Networks; Knowledge Discovery; Knowledge Organization (search for similar items in EconPapers)
Date: 2007
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