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

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Abstract

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.

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Acknowledgments

We would like to acknowledge the reviewers of the International Journal of Natural Computing as well as the ICARIS’09 conference on which this work was presented for valuable comments. This research is currently supported by Universiti Kebangsaan Malaysia, Malaysia Ministry of Higher Education under the Fundamental Research Grant Scheme. We would also like to thank all our friends for feedback and comments, in particular Jon Timmis for clarifying our AIS-related questions, Hafiz Mohd Sarim for comments and Tri Basuki Kurniawan for optimization-related questions.

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Authors and Affiliations

  1. Data Mining and Optimization Research Group, Centre for Artificial Information Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43650, Bangi, Selangor, Malaysia

    Mohd Zakree Ahmad Nazri, Azuraliza Abu Bakar & Salwani Abdullah

  2. Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81100, Skudai, Johor, Malaysia

    Mohd Zakree Ahmad Nazri & Siti Mariyam Shamsuddin

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  1. Mohd Zakree Ahmad Nazri
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Correspondence to Mohd Zakree Ahmad Nazri.

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Nazri, M.Z.A., Shamsuddin, S.M., Bakar, A.A. et al. A hybrid approach for learning concept hierarchy from Malay text using artificial immune network. Nat Comput 10, 275–304 (2011). https://doi.org/10.1007/s11047-010-9228-7

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