Learning non-taxomomic relations on demand for ontology extension
Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.Citation
Y. Xu, G. Li, L. Mou, Y. Lu. "Learning non-taxomomic relations on demand for ontology extension". International Journal of Software Engineering and Knowledge Engineering, 24(8), pp 1159–1175, October 2014.| Keywords: | Learning on demand, ontology extension, non-taxonomic relations, information retrieval, dependency parsing |
| Category: | In Journal |
| Web Links: | World Scientific |
BibTeX
@article{Xu+al:14,
author = {Yan Xu and Ge Li and Lili Mou and Yangyang Lu},
title = {Learning non-taxomomic relations on demand for ontology extension},
Volume = "24",
Number = "8",
Pages = {1159–1175},
journal = {International Journal of Software Engineering and Knowledge
Engineering},
year = 2014,
}Last Updated: February 04, 2021Submitted by Sabina P
