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Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus

by: L. De Silva, and L. Jayaratne

In: Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the (August 2009) , p. 446-451.

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URL:	http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5273826&arnumber=5273871&count=156&index=116
DOI:	10.1109/ICADIWT.2009.5273871

This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using natural language processing NLP and other machine learning ML techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.

Description

Welcome to IEEE Xplore 2.0: Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus

BibTeX record

@inproceedings{silva2009semiautomatic,
  abstract = {This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using natural language processing (NLP) and other machine learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.},
  added-at = {2011-02-17T17:43:07.000+0100},
  author = {Silva, L. De and Jayaratne, L.},
  biburl = {http://www.bibsonomy.org/bibtex/266bec053541e521fbe68c0119806ae49/dbenz},
  booktitle = {Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the},
  description = {Welcome to IEEE Xplore 2.0: Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus},
  doi = {10.1109/ICADIWT.2009.5273871},
  file = {silva2009semiautomatic.pdf:silva2009semiautomatic.pdf:PDF},
  groups = {public},
  interhash = {c1996cb9e69de56e2bb2f8e763fe0482},
  intrahash = {66bec053541e521fbe68c0119806ae49},
  keywords = {learning ol_web2.0 ontology ontology_learning semi_automatic wikipedia data_wikis},
  month = {Aug.},
  pages = {446-451},
  timestamp = {2011-02-17T17:43:07.000+0100},
  title = {Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus},
  url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5273826&arnumber=5273871&count=156&index=116},
  username = {dbenz},
  year = 2009
}

Endnote record

%0 Conference Paper
%1 silva2009semiautomatic
%A Silva, L. De
%A Jayaratne, L.
%B Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the
%D 2009
%K 
%P 446-451
%T Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus
%R 10.1109/ICADIWT.2009.5273871
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5273826&arnumber=5273871&count=156&index=116
%X This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using natural language processing (NLP) and other machine learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well.