caa a22 4500 445842725 CHVBK 20180317145349.0 cr unu---uuuuu 170323e20110301xx s 000 0 eng 10.1007/s11047-010-9228-7 doi (NATIONALLICENCE)springer-10.1007/s11047-010-9228-7 A hybrid approach for learning concept hierarchy from Malay text using artificial immune network [Elektronische Daten] [Mohd Nazri, Siti Shamsuddin, Azuraliza Bakar, Salwani Abdullah] A concept hierarchy is an integral part of an ontology but it is expensive and time consuming to build. Motivated by this, many unsupervised learning methods have been proposed to (semi-) automatically develop a concept hierarchy. A significant work is the Guided Agglomerative Hierarchical Clustering (GAHC) which relies on linguistic patterns (i.e., hypernyms) to guide the clustering process. However, GAHC still relies on contextual features to build the concept hierarchy, thus data sparsity still remains an issue in GAHC. Artificial Immune Systems are known for robustness, noise tolerance and adaptability. Thus, an extension to the GAHC is proposed by hybridizing it with Artificial Immune Network (aiNet) which we call Guided Clustering and aiNet for Learning Concept Hierarchy (GCAINY). In this paper, we have tested GCAINY using two parameter settings. The first parameter setting is obtained from the literature as a baseline parameter setting and second is by automatic parameter tuning using Particle Swarm Optimization (PSO). The effectiveness of the GCAINY is evaluated on three data sets. For further validations, a comparison between GCAINY and GAHC has been conducted and with statistical tests showing that GCAINY increases the quality of the induced concept hierarchy. The results reveal that the parameters value found by using PSO significantly produce better concept hierarchy than the vanilla parameter. Thus it can be concluded that the proposed approach has greater ability to be used in the field of ontology learning. Springer Science+Business Media B.V., 2010 Artificial immune system nationallicence Immune network nationallicence Machine learning nationallicence Malay language nationallicence Ontology learning nationallicence Automatic taxonomy induction nationallicence Nazri Mohd Data Mining and Optimization Research Group, Centre for Artificial Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia aut Shamsuddin Siti Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81100, Skudai, Johor, Malaysia aut Bakar Azuraliza Data Mining and Optimization Research Group, Centre for Artificial Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia aut Abdullah Salwani Data Mining and Optimization Research Group, Centre for Artificial Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia aut Natural Computing Springer Netherlands 10/1(2011-03-01), 275-304 1567-7818 10:1<275 2011 10 11047 https://doi.org/10.1007/s11047-010-9228-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11047-010-9228-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nazri Mohd Data Mining and Optimization Research Group, Centre for Artificial Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia aut NATIONALLICENCE 700 1- Shamsuddin Siti Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81100, Skudai, Johor, Malaysia aut NATIONALLICENCE 700 1- Bakar Azuraliza Data Mining and Optimization Research Group, Centre for Artificial Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia aut NATIONALLICENCE 700 1- Abdullah Salwani Data Mining and Optimization Research Group, Centre for Artificial Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia aut NATIONALLICENCE 773 0- Natural Computing Springer Netherlands 10/1(2011-03-01), 275-304 1567-7818 10:1<275 2011 10 11047 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer