Wikipedia has become a huge phenomenon on the WWW. As a corpus for knowledge extraction, it has various impressive characteristics
such as a huge amount of articles, live updates, a dense link structure, brief link texts and URL identification for concepts.In our previous work, we proposed link structure mining algorithms to extract a huge scale and accurate association thesaurusfrom Wikipedia. The association thesaurus covers almost 1.3 million concepts and the significant accuracy is proved in detailedexperiments. To prove its practicality, we implemented three features on the association thesaurus; a search engine for browsingWikipedia Thesaurus, an XML Web service for the thesaurus and a Semantic Web support feature. We show these features in thisdemonstration.
%0 Journal Article
%1 keyhere
%A Nakayama, Kotaro
%A Hara, Takahiro
%A Nishio, Shojiro
%D 2008
%J Database Systems for Advanced Applications
%K KOS Terminologies Web2.0
%P 690--693
%T A Search Engine for Browsing the Wikipedia Thesaurus
%U http://dx.doi.org/10.1007/978-3-540-78568-2_64
%X Wikipedia has become a huge phenomenon on the WWW. As a corpus for knowledge extraction, it has various impressive characteristics
such as a huge amount of articles, live updates, a dense link structure, brief link texts and URL identification for concepts.In our previous work, we proposed link structure mining algorithms to extract a huge scale and accurate association thesaurusfrom Wikipedia. The association thesaurus covers almost 1.3 million concepts and the significant accuracy is proved in detailedexperiments. To prove its practicality, we implemented three features on the association thesaurus; a search engine for browsingWikipedia Thesaurus, an XML Web service for the thesaurus and a Semantic Web support feature. We show these features in thisdemonstration.
@article{keyhere,
abstract = {Wikipedia has become a huge phenomenon on the WWW. As a corpus for knowledge extraction, it has various impressive characteristics
such as a huge amount of articles, live updates, a dense link structure, brief link texts and URL identification for concepts.In our previous work, we proposed link structure mining algorithms to extract a huge scale and accurate association thesaurusfrom Wikipedia. The association thesaurus covers almost 1.3 million concepts and the significant accuracy is proved in detailedexperiments. To prove its practicality, we implemented three features on the association thesaurus; a search engine for browsingWikipedia Thesaurus, an XML Web service for the thesaurus and a Semantic Web support feature. We show these features in thisdemonstration.},
added-at = {2008-10-21T22:49:11.000+0200},
author = {Nakayama, Kotaro and Hara, Takahiro and Nishio, Shojiro},
biburl = {https://www.bibsonomy.org/bibtex/2b6f084114fe136658a7f4978b32817c5/georgemacgregor},
description = {SpringerLink - Book Chapter},
interhash = {03cda4cd597ed04b6acf9f891e1b38ec},
intrahash = {b6f084114fe136658a7f4978b32817c5},
journal = {Database Systems for Advanced Applications},
keywords = {KOS Terminologies Web2.0},
pages = {690--693},
timestamp = {2008-10-21T22:49:11.000+0200},
title = {A Search Engine for Browsing the Wikipedia Thesaurus},
url = {http://dx.doi.org/10.1007/978-3-540-78568-2_64},
year = 2008
}