Decoding Wikipedia Categories for Knowledge Acquisition
V. Nastase, und M. Strube. Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 2, Seite 1219--1224. AAAI Press, (2008)
Zusammenfassung
This paper presents an approach to acquire knowledge from Wikipedia categories and the category network. Many Wikipedia categories have complex names which reflect human classification and organizing instances, and thus encode knowledge about class attributes, taxonomic and other semantic relations. We decode the names and refer back to the network to induce relations between concepts in Wikipedia represented through pages or categories. The category structure allows us to propagate a relation detected between constituents of a category name to numerous concept links. The results of the process are evaluated against ResearchCyc and a subset also by human judges. The results support the idea that Wikipedia category names are a rich source of useful and accurate knowledge.
%0 Conference Paper
%1 Nastase:2008:DWC:1620163.1620262
%A Nastase, Vivi
%A Strube, Michael
%B Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 2
%D 2008
%I AAAI Press
%K imported
%P 1219--1224
%T Decoding Wikipedia Categories for Knowledge Acquisition
%U http://dl.acm.org/citation.cfm?id=1620163.1620262
%X This paper presents an approach to acquire knowledge from Wikipedia categories and the category network. Many Wikipedia categories have complex names which reflect human classification and organizing instances, and thus encode knowledge about class attributes, taxonomic and other semantic relations. We decode the names and refer back to the network to induce relations between concepts in Wikipedia represented through pages or categories. The category structure allows us to propagate a relation detected between constituents of a category name to numerous concept links. The results of the process are evaluated against ResearchCyc and a subset also by human judges. The results support the idea that Wikipedia category names are a rich source of useful and accurate knowledge.
%@ 978-1-57735-368-3
@inproceedings{Nastase:2008:DWC:1620163.1620262,
abstract = {This paper presents an approach to acquire knowledge from Wikipedia categories and the category network. Many Wikipedia categories have complex names which reflect human classification and organizing instances, and thus encode knowledge about class attributes, taxonomic and other semantic relations. We decode the names and refer back to the network to induce relations between concepts in Wikipedia represented through pages or categories. The category structure allows us to propagate a relation detected between constituents of a category name to numerous concept links. The results of the process are evaluated against ResearchCyc and a subset also by human judges. The results support the idea that Wikipedia category names are a rich source of useful and accurate knowledge.},
acmid = {1620262},
added-at = {2016-11-28T10:15:50.000+0100},
author = {Nastase, Vivi and Strube, Michael},
biburl = {https://www.bibsonomy.org/bibtex/2ce830844472b2cc149392bdf1d254b29/kde-alumni},
booktitle = {Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 2},
interhash = {11a25a068561112fdd8123ba5d382252},
intrahash = {ce830844472b2cc149392bdf1d254b29},
isbn = {978-1-57735-368-3},
keywords = {imported},
location = {Chicago, Illinois},
numpages = {6},
pages = {1219--1224},
publisher = {AAAI Press},
series = {AAAI'08},
timestamp = {2016-11-28T10:15:50.000+0100},
title = {Decoding Wikipedia Categories for Knowledge Acquisition},
url = {http://dl.acm.org/citation.cfm?id=1620163.1620262},
year = 2008
}