Mining world knowledge for analysis of search engine content

King, John D., Li, Yuefeng, Tao, Xiaohui, & Nayak, Richi (2007) Mining world knowledge for analysis of search engine content. Web Intelligence and Agent Systems, 5(3), pp. 233-253.

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Little is known about the content of the major search engines. We present an automatic learning method which trains an ontology with world knowledge of hundreds of different subjects in a three-level taxonomy covering all the documents offered in our university library. We then mine this ontology to find important classification rules, and then use these rules to perform an extensive analysis of the content of the largest general purpose internet search engines in use today. Instead of representing documents and collections as a set of terms, we represent them as a set of subjects, which is a highly efficient representation, leading to a more robust representation of information and a decrease of synonymy.

Impact and interest:

28 citations in Scopus
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537 since deposited on 25 Mar 2008
110 in the past twelve months

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ID Code: 13119
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Ontology, hierarchal classification, taxonomy, collection selection, search engines, data mining
ISSN: 1570-1263
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 IOS Press and The authors
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 25 Mar 2008 00:00
Last Modified: 29 Feb 2012 13:38

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