NDLI: Featureless similarities for terms clustering using tree-traversing ants

Content Provider	ACM Digital Library
Author	Bennamoun, Mohammed Liu, Wei Wong, Wilson
Abstract	Besides being difficult to scale between different domains and to handle knowledge fluctuations, the results of terms clustering presented by existing ontology engineering systems are far from desirable. In this paper, we propose a new version of ant-based method for clustering terms known as Tree-Traversing Ants (TTA). With the help of the Normalized Google Distance (NGD) and n° of Wikipedia (n°W) as measures for similarity and distance between terms, we attempt to achieve an adaptable clustering method that is highly scalable across domains. Initial experiments with two datasets show promising results and demonstrated several advantages that are not simultaneously present in standard ant-based and other conventional clustering methods.
Starting Page	177
Ending Page	191
Page Count	15
File Format	PDF
ISBN	1740521307
DOI	10.1145/1232425.1232448
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-11-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Terms clustering Ant-based clustering method Featureless similarity measures
Content Type	Text
Resource Type	Article

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Featureless similarities for terms clustering using tree-traversing ants