Ontologies are an important resource for knowledge representation. Their structure can be complex due to role relations between several concepts, distinct attributes, and different instances. In this paper, we discuss a Visual Analytics solution, relying on the use of multiple coordinated views for exploring different ontology aspects and a novel use of the degree of interest (DoI) suppression technique to reduce the complexity of the ontology visual representation. Visual Analytics facilitates the understanding of the domain and tasks represented by ontologies, thus allowing to carry out exploratory analysis to optimize the comprehension of data semantics including non-explicit relationships between data. Through the DoI technique, we place the main concept in focus, distinguishing it from the unnecessary information and facilitating the analysis and understanding of correlated data. We evaluated all the devised solutions, and the results reinforce the importance of providing visualization and analysis techniques dedicated to the schema and instances levels of ontologies for the discovery of non-explicit information.

本体是知识表示的重要资源。由于几个概念，不同的属性和不同的实例之间的角色关系，它们的结构可能很复杂。在本文中，我们讨论了一种Visual Analytics解决方案，该解决方案依赖于使用多个协调视图来探索不同的本体论方面，以及一种新颖的使用兴趣度（DoI）抑制技术来降低本体论视觉表示的复杂性的方法。可视化分析有助于理解由本体论表示的领域和任务，从而允许进行探索性分析以优化对数据语义的理解，包括数据之间的非显式关系。通过DoI技术，我们将主要概念放在了重点位置，将其与不必要的信息区分开来，并有助于对相关数据的分析和理解。我们对所有设计的解决方案进行了评估，结果强调了提供可视化和分析技术的重要性，这些可视化和分析技术专用于本体和架构级别的实例，以发现非明确信息。