Genetic Algorithm is currently used as a solution to various problems in a wide range of disciplines. In order to improve the convergence rates of Genetic Algorithms, a new branch of evolutionary computation called "cultural algorithms" has been introduced that provides the possibility of exchanging information in the population component of a conventional genetic algorithm. As expected in some applications the convergence rates obtained by cultural algorithms such as Imperialist Competitive Algorithm (ICA) were better or at least similar to those obtained by applying the genetic algorithms. In this paper, we aim to propose a new perspective, which is assumed to increase the capability of exchanging information in the population component of the evolutionary algorithms by providing an infrastructure for dialogues. In other words, we divide a population into several regions equivalent to the empires in ICA where instead of the competition among regions we introduce the notion of Dialogue among regions, which is assumed to improve the convergence rate towards the absolute minimum.
Manual creation of ontologies is a time-consuming, costly and complicated process. Consequently, over the past decade a significant number of methods have been proposed for (semi)automatic generation of ontologies from existing data, especially textual ones. However, there are still significant limitations in this area. This study is an early effort towards reusing the semantic knowledge freely available in Web of Linked Data to improve the results of ontology learning from text in terms of multilingual making of ontology terms, classification of dangling instances, recommending appropriate intensions for ontology concepts, and concept hierarchy enrichment. Actually, these are the tasks associated with the second, third and fourth layer of Ontology Learning Stack. In our first stage experimental efforts, Factforge was used to implement the research objectives. Then, the results gained by an expert were compared against those obtained automatically. Finally, the experimental results verified the importance of the proposed approach through the achieved improvements in most of the objectives mentioned.
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