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A review on learning taxonomies from Malay text corpora

Ahmad Nazri, Mohd. Zakree and Shamsuddin, Siti Mariyam and Abu Bakar, Azuraliza (2007) A review on learning taxonomies from Malay text corpora. Jurnal Teknologi Maklumat, 19 (2). pp. 85-99. ISSN 0128-3790

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Abstract

Taxonomy is a science of classifying living things. In the 21st century, taxonomy is also known as a form of business intelligence, used to integrate information, reduce semantic heterogeneity, describe emergent communities and interest groups, facilitate the communication between information systems. However, in building a taxonomy, knowledge acquisition is the bottleneck that . Ontology engineers also need guidelines about the effectiveness, efficiency and trade-offs of different methods in order to decide which techniques to apply in which settings. But there are no comparative work systematically analyzing different techniques and algorithms on learning concept hierarchies from a Malay text. In this paper we review the state of the arts in taxonomy learning and address the lack of work in the field of concept hierarchy induction from Malay text. We also defme an evaluation methodology to systematically comparing different approaches. In our further works section, we proposed an experimental approach to study various approaches and methods to automatically acquire concept hierarchies from Malay texts.

Item Type:Article
Uncontrolled Keywords:ontology learning, artificial intelligence, natural language processing, formal concept analysis, taxonomy
Subjects:Q Science > QM Human anatomy
Divisions:Computer Science and Information System (Formerly known)
ID Code:8173
Deposited By: Norshiela Buyamin
Deposited On:02 Apr 2009 06:44
Last Modified:09 Jan 2014 04:07

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