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Tree-traversing ant algorithm for term clustering based on featureless similarities
Abstract Many conventional methods for concepts forMany conventional methods for concepts formation in ontology learning have relied on the use of predefined templates and rules, and static resources such as WordNet. Such approaches are not scalable, difficult to port between different domains and incapable of handling knowledge fluctuations. Their results are far from desirable, either. In this paper, we propose a new ant-based clustering algorithm, Tree-Traversing Ant (TTA), for concepts formation as part of an ontology learning system. With the help of Normalized Google Distance (NGD) and n of Wikipedia (nW) as measures for similarity and distance between terms, we attempt to achieve an adaptable clustering method that is highly scalable and portable across domains. Evaluations with an seven datasets show promising results with an average lexical overlap of 97% and ontological improvement of 48%. At the same time, the evaluations demonstrated several advantages that are not simultaneously present in standard ant-based and other conventional clustering methods.and other conventional clustering methods.
Added by wikilit team Added on initial load  +
Collected data time dimension Cross-sectional  +
Comments we have proposed the innovative use of feawe have proposed the innovative use of featureless similarity based on Normalized Google Distance (NGD) and n◦ of Wikipedia (n◦W). The use of the two similarity measures as part of a new hybrid clustering algorithm called Tree-Traversing Ant (TTA) demonstrated excellent results during our evaluations. excellent results during our evaluations.
Conclusion Seven of the most notable strength of the Seven of the most notable strength of the TTA with NGD and n◦W are: – Able to further distinguish hidden structures within clusters; – Flexible in regards to the discovery of clusters; – Capable of identifying and isolating outliers; – Tolerance to differing cluster sizes; – Able to produce consistent results; – Able to identify implicit taxonomic relationships between clusters; and – Inherent capability of coping with synonyms, word senses and the fluctuation in terms usage.senses and the fluctuation in terms usage.
Data source Experiment responses  + , Wikipedia pages  +
Doi 10.1007/s10618-007-0073-y +
Google scholar url http://scholar.google.com/scholar?ie=UTF-8&q=%22Tree-traversing%2Bant%2Balgorithm%2Bfor%2Bterm%2Bclustering%2Bbased%2Bon%2Bfeatureless%2Bsimilarities%22  +
Has author Wilson Wong + , Wei Liu + , Mohammed Bennamoun +
Has domain Computer science +
Has topic Ontology building +
Issue 3  +
Pages 349-381  +
Peer reviewed Yes  +
Publication type Journal article  +
Published in Data Mining and Knowledge Discovery +
Research design Experiment  +
Research questions In this paper, we propose a new antbased cIn this paper, we propose a new antbased clustering algorithm, Tree-Traversing Ant (TTA), for concepts formation as part of an ontology learning system.With the help of Normalized GoogleDistance (NGD) and n◦ ofWikipedia (n◦W) as measures for similarity and distance between terms, we attempt to achieve an adaptable clustering method that is highly scalable and portable across domains.ghly scalable and portable across domains.
Revid 11,008  +
Theories the TTAs will employ a new measure called n◦ ofWikipedia (n◦W) for quantifying the distance between two terms based on the cross-linking of Wikipedia articles (Wong et al. 2006).
Theory type Analysis  + , Design and action  +
Title Tree-traversing ant algorithm for term clustering based on featureless similarities
Unit of analysis Article  +
Url http://dx.doi.org/10.1007/s10618-007-0073-y  +
Volume 15  +
Wikipedia coverage Other  +
Wikipedia data extraction Dump  +
Wikipedia language English  +
Wikipedia page type Article  +
Year 2007  +
Creation dateThis property is a special property in this wiki. 15 March 2012 20:31:58  +
Categories Ontology building  + , Computer science  + , Publications  +
Modification dateThis property is a special property in this wiki. 30 January 2014 20:32:01  +
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