AUTOMATIC GENERATION OF CONCEPT TAXONOMIES FROM WEB SEARCH DATA USING SUPPORT VECTOR MACHINE
Robertas Damaševičius
2009
Abstract
Ontologies and concept taxonomies are essential parts of the Semantic Web infrastructure. Since manual construction of taxonomies requires considerable efforts, automated methods for taxonomy construction should be considered. In this paper, an approach for automatic derivation of concept taxonomies from web search results is presented. The method is based on generating derivative features from web search data and applying the machine learning techniques. The Support Vector Machine (SVM) classifier is trained with known concept hyponym-hypernym pairs and the obtained classification model is used to predict new hyponymy (is-a) relations. Prediction results are used to generate concept taxonomies in OWL. The results of the application of the approach for constructing colour taxonomy are presented.
DownloadPaper Citation
in Harvard Style
Damaševičius R. (2009). AUTOMATIC GENERATION OF CONCEPT TAXONOMIES FROM WEB SEARCH DATA USING SUPPORT VECTOR MACHINE . In Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-8111-81-4, pages 666-673. DOI: 10.5220/0001842206660673
in Bibtex Style
@conference{webist09,
author={Robertas Damaševičius},
title={AUTOMATIC GENERATION OF CONCEPT TAXONOMIES FROM WEB SEARCH DATA USING SUPPORT VECTOR MACHINE},
booktitle={Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2009},
pages={666-673},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001842206660673},
isbn={978-989-8111-81-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the Fifth International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - AUTOMATIC GENERATION OF CONCEPT TAXONOMIES FROM WEB SEARCH DATA USING SUPPORT VECTOR MACHINE
SN - 978-989-8111-81-4
AU - Damaševičius R.
PY - 2009
SP - 666
EP - 673
DO - 10.5220/0001842206660673