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 machine-understandable 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.
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Chau, R., Smith-Miles, K., Yeh, CH. (2006). Ontology Learning from Text: A Soft Computing Paradigm. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_34
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DOI: https://doi.org/10.1007/11893295_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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