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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)275-304
Number of pages30
JournalNatural Computing
Volume10
Issue number1
DOIs
Publication statusPublished - Mar 2011

Fingerprint

Particle swarm optimization (PSO)
Ontology
Unsupervised learning
Statistical tests
Immune system
Linguistics
Tuning

Keywords

  • Artificial immune system
  • Automatic taxonomy induction
  • Immune network
  • Machine learning
  • Malay language
  • Ontology learning

ASJC Scopus subject areas

  • Computer Science Applications

Cite this

A hybrid approach for learning concept hierarchy from Malay text using artificial immune network. / Ahmad Nazri, Mohd Zakree; Shamsuddin, Siti Mariyam; Abu Bakar, Azuraliza; Abdullah, Salwani.

In: Natural Computing, Vol. 10, No. 1, 03.2011, p. 275-304.

Research output: Contribution to journalArticle

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