A Hybrid Approach for Learning Concept Hierarchy from Malay Text Using GAHC and Immune Network

Authors:
Mohd Zakree Nazri;Siti Mariyam Shamsuddin;Azuraliza Abu Bakar;Salwani Abdullah
Affiliations:
Data Mining & Optimization Research Group, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia 43650 and Soft Computing Research Group, Faculty of Computer ...;Soft Computing Research Group, Faculty of Computer Science & Information System, Universiti Teknologi Malaysia, Skudai, Malaysia 81100;Data Mining & Optimization Research Group, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia 43650;Data Mining & Optimization Research Group, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia 43650
Venue:
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Year:
2009

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A hybrid approach for learning concept hierarchy from Malay text using artificial immune network

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Abstract

The human immune system provides inspiration in the attempt of solving the knowledge acquisition bottleneck in developing ontology for semantic web application. In this paper, we proposed an extension to the Guided Agglomerative Hierarchical Clustering (GAHC) method that uses an Artificial Immune Network (AIN) algorithm to improve the process of automatically building and expanding the concept hierarchy. A small collection of Malay text is used from three different domains which are IT, Biochemistry and Fiqh to test the effectiveness of the proposed approach and also by comparing it with GAHC. The proposed approach consists of three stages: pre-processing, concept hierarchy induction using GAHC and concept hierarchy learning using AIN. To validate our approach, the automatically learned concept hierarchy is compared to a reference ontology developed by human experts. Thus it can be concluded that the proposed approach has greater ability to be used in learning concept hierarchy.