Tree-Traversing Ant Algorithm for term clustering based on featureless similarities

Many 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 degrees of Wikipedia (n degrees 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. 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.