Mining world knowledge for analysis of search engine content
Article
Article Title | Mining world knowledge for analysis of search engine content |
---|---|
ERA Journal ID | 32174 |
Article Category | Article |
Authors | King, John D. (Author), Li, Yuefeng (Author), Tao, Xiaohui (Author) and Nayak, Richi (Author) |
Journal Title | Web Intelligence |
Journal Citation | 5 (3), pp. 233-253 |
Number of Pages | 21 |
Year | 2007 |
Publisher | IOS Press |
Place of Publication | Amsterdam, Netherlands |
ISSN | 1570-1263 |
1875-9289 | |
2405-6456 | |
2405-6464 | |
Web Address (URL) | http://wi-consortium.org/wicweb/html/journal.php |
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. |
Keywords | ontology; hierarchal classification; taxonomy; collection selection; search engines; data mining |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460508. Information retrieval and web search | |
460605. Distributed systems and algorithms | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Queensland University of Technology |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q0zwx/mining-world-knowledge-for-analysis-of-search-engine-content
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