Properties of learning of a fuzzy ART variant
Neural Networks
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
An Empirical Study on Performance Comparison of Lucene and Relational Database
ICCSN '09 Proceedings of the 2009 International Conference on Communication Software and Networks
Ontology-Driven Relation Extraction by Pattern Discovery
EKNOW '10 Proceedings of the 2010 Second International Conference on Information, Process, and Knowledge Management
Conceptual Maps as the First Step in an Ontology Construction Method
EDOCW '10 Proceedings of the 2010 14th IEEE International Enterprise Distributed Object Computing Conference Workshops
Using Formal Concept Analysis for Maritime Ontology Building
IFITA '10 Proceedings of the 2010 International Forum on Information Technology and Applications - Volume 02
Large scale Hamming distance query processing
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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The best known approaches to learning ontologies from unstructured text focus on the extraction of ontologies by applying the techniques from natural language processing and machine learning. In the present study, we propose a semiautomatic approach that uses the variables selection and clustering to find the candidate changes. The model found in the training set is used to classify the new examples and to derive candidate changes. Our approach uses an alignment process to compare the ontological entities and candidate changes. The results show that the conceptual model is critically dependence on the measures distance. Good experimental studies demonstrate the multidisciplinary applications of our approach.