摘要：Visualizing and understanding large conceptual schemas requires the use of specific methods. These methods generate clustered, summarized, or focused schemas that are easier to visualize and understand. All of these methods require computing the importance of each entity type in the schema. In principle, the totality of knowledge defined in the schema could be relevant for the computation of that importance but, up to now, only a small part of that knowledge has been taken into account. In this paper, we extend seven existing methods for computing the importance of entity types by taking into account more relevant knowledge de_ned in the structural and behavioural parts of the schema. We experimentally evaluate the original and extended versions of these methods with three large real-world schemas. We present the two main conclusions we have drawn from the experiments.
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