Ontology learning from text: a soft computing paradigm

  • Authors:

  • Rowena Chau;Kate Smith-Miles;Chung-Hsing Yeh

  • Affiliations:

  • Clayton School of Information Technology, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;School of Engineering and Information Technology, Burwood, Victoria, Australia;Clayton School of Information Technology, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia

  • Venue:
  • ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Text-based information accounts for more than 80% of today's Web content. They consist of Web pages written in different natural languages. As the semantic Web aims at turning the current Web into a machineunderstandable knowledge repository, availability of multilingual ontology thus becomes an issue at the core of a multilingual semantic Web. However, multilingual ontology is too complex and resource intensive to be constructed manually. In this paper, we propose a three-layer model built on top of a soft computing framework to automatically acquire a multilingual ontology from domain specific parallel texts. The objective is to enable semantic smart information access regardless of language over the Semantic Web.