Clonal Selection Algorithm for Learning Concept Hierarchy from Malay Text

Concept hierarchy is an integral part of ontology which is the backbone of the Semantic Web. This paper describes a new hierarchical clustering algorithm for learning concept hierarchy named Clonal Selection Algorithm for Learning Concept Hierarchy, or CLONACH. The proposed algorithm resembles the CLONALG. CLONACH's effectiveness is evaluated on three data sets. The results show that the concept hierarchy produced by CLONACH is better than the agglomerative clustering technique in terms of taxonomic overlaps. Thus, the CLONALG based algorithm has been regarded as a promising technique in learning from texts, in particular small collection of texts.

[1]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[2]  Walter Daelemans,et al.  Is Shallow Parsing Useful for Unsupervised Learning of Semantic Clusters? , 2003, CICLing.

[3]  Tefko Saracevic,et al.  Information science: What is it? , 1968 .

[4]  Boi Faltings,et al.  Using hierarchical clustering for learning theontologies used in recommendation systems , 2007, KDD '07.

[5]  Steffen Staab,et al.  Comparing Conceptual, Divise and Agglomerative Clustering for Learning Taxonomies from Text , 2004, ECAI.

[6]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[7]  Vijayan Sugumaran,et al.  Natural Language and Information Systems, 13th International Conference on Applications of Natural Language to Information Systems, NLDB 2008, London, UK, June 24-27, 2008, Proceedings , 2008, NLDB.

[8]  Chenggong Zhang,et al.  Tree structured artificial immune network with self-organizing reaction operator , 2009, Neurocomputing.

[9]  Lucas Drumond,et al.  A Survey of Ontology Learning Procedures , 2008, WONTO.

[10]  Alex Alves Freitas,et al.  An Artificial Immune System for Clustering Amino Acids in the Context of Protein Function Classification , 2009, J. Math. Model. Algorithms.

[11]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[12]  Sharon A. Caraballo.  Automatic construction of a hypernym-labeled noun hierarchy from text , 1999, ACL.

[13]  F. Azuaje.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[14]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[15]  Jon Atle Gulla,et al.  A Hybrid Approach to Ontology Relationship Learning , 2008, NLDB.

[16]  Jiawei Han,et al.  Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases , 1994, KDD Workshop.

[17]  Zellig S. Harris,et al.  Mathematical structures of language , 1968, Interscience tracts in pure and applied mathematics.

[18]  Philipp Cimiano,et al.  Ontology learning and population from text - algorithms, evaluation and applications , 2006 .

[19]  Steffen Staab,et al.  Learning Concept Hierarchies from Text with a Guided Agglomerative Clustering Algorithm , 2005, ICML 2005.

[20]  Azuraliza Abu Bakar,et al.  An Exploratory Study on Malay Processing Tool for Acquisition of Taxonomy Using FCA , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[21]  Mariano Fernández-López,et al.  Ontological Engineering , 2003, Encyclopedia of Database Systems.