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

Ahmad Nazri, Mohd. Zakree and Shamsuddin, Siti Mariyam and Abu Bakar, Azuraliza and Abdullah, Salwani (2009) A hybrid approach for learning concept hierarchy from Malay text using GAHC and immune network. In: Artificial Immune Systems, Proceedings. Springer Verlag, Berlin, Heidelberg, pp. 315-328. ISBN 978-3-642-03245-5

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Official URL: http://dx.doi.org/10.1007/978-3-642-03246-2_29


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.

Item Type:Book Section
Uncontrolled Keywords:artificial immune system, ontology learning, immune network ontology engineering, machine learning, semantic web
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:11775
Deposited By: Nor Asmida Abdullah
Deposited On:17 Jan 2011 12:33
Last Modified:02 Oct 2017 04:49

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