In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.
Today cloud computing has become one of the common technologies that most of the companies want to migrate their legacy systems or deploy their new system to it. Besides modeling the system, software designers need to model the deployment infrastructure, which their system will be deployed on it. In this paper first of all we presented and categorized the requirements of modeling cloud and then illustrated how the software designer can use the advantages of UML's extendibility to model the deployment of cloud computing systems. By using stereotypes and tag values, it is possible to define a meta-model which is suitable for modeling the system deployed in cloud computing. We show that by using the UML profile, it's possible to model the infrastructures and instances of the cloud. Also it's probable to cover all the requirements that the software designer needs to model the cloud computing. It is concluded that it is important to use a standard modeling language to model the cloud, which makes it possible to model and test the whole system with a unified language. The standard languages such as UML reduce the cost required for understanding and designing the cloud computing's model. It's also possible to use this model in MDA (Model Driven Architecture) to understand and test the system's behavior in the cloud computing before deploying it in the cloud.
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