Measuring data-driven ontology changes using text mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Determining termhood for learning domain ontologies using domain prevalence and tendency
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Constructing Web Corpora through Topical Web Partitioning for Term Recognition
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Web service clustering using text mining techniques
International Journal of Agent-Oriented Software Engineering
Acquiring Semantic Relations Using the Web for Constructing Lightweight Ontologies
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A probabilistic framework for automatic term recognition
Intelligent Data Analysis
Wikispeedia: an online game for inferring semantic distances between concepts
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Discovering homogenous service communities through web service clustering
SOCASE'08 Proceedings of the 2008 AAMAS international conference on Service-oriented computing: agents, semantics, and engineering
Resources for Turkish morphological processing
Language Resources and Evaluation
Biologically-inspired clustering of semantic Web services. Birds or ants intelligence?
Concurrency and Computation: Practice & Experience
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
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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° of Wikipedia (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. 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.