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A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies

A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies
A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies
Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.
web search, classification, folksonomy, tagging
70-76
Au Yeung, Ching Man
c83390b1-d3a1-459e-8f09-01c81576e066
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Au Yeung, Ching Man
c83390b1-d3a1-459e-8f09-01c81576e066
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Shadbolt, Nigel
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7

Au Yeung, Ching Man, Gibbins, Nicholas and Shadbolt, Nigel (2008) A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies. 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Australia. 09 - 12 Dec 2008. pp. 70-76 .

Record type: Conference or Workshop Item (Paper)

Abstract

Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.

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More information

Published date: 9 December 2008
Additional Information: Event Dates: 9-12 December 2008
Venue - Dates: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Australia, 2008-12-09 - 2008-12-12
Keywords: web search, classification, folksonomy, tagging
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 266991
URI: https://eprints.soton.ac.uk/id/eprint/266991
PURE UUID: 7609459e-5087-41d2-bc28-d539afb7d764
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956

Catalogue record

Date deposited: 21 Dec 2008 13:43
Last modified: 20 Jul 2019 01:11

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Contributors

Author: Ching Man Au Yeung
Author: Nicholas Gibbins ORCID iD
Author: Nigel Shadbolt

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