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.
![[img]](http://eprints.qut.edu.au/style/images/fileicons/application_pdf.png)
Abstract
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.
Citations:
Citation countsare sourced monthly from Scopus and Web of Science citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
| ID Code: | 13119 |
|---|---|
| Item Type: | Journal Article |
| 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 |
| Last Modified: | 29 Feb 2012 23:38 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page