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Forming Cognitive Maps of Ontologies Using Interactive Visualizations

机译：使用交互式可视化形成本体的认知地图

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Ontology datasets, which encode the expert-defined complex objects mapping the entities, relations, and structures of a domain ontology, are increasingly being integrated into the performance of challenging knowledge-based tasks. Yet, it is hard to use ontology datasets within our tasks without first understanding the ontology which it describes. Using visual representation and interaction design, interactive visualization tools can help us learn and develop our understanding of unfamiliar ontologies. After a review of existing tools which visualize ontology datasets, we find that current design practices struggle to support learning tasks when attempting to build understanding of the ontological spaces within ontology datasets. During encounters with unfamiliar spaces, our cognitive processes align with the theoretical framework of cognitive map formation. Furthermore, designing encounters to promote cognitive map formation can improve our performance during learning tasks. In this paper, we examine related work on cognitive load, cognitive map formation, and the use of interactive visualizations during learning tasks. From these findings, we formalize a set of high-level design criteria for visualizing ontology datasets to promote cognitive map formation during learning tasks. We then perform a review of existing tools which visualize ontology datasets and assess their interface design towards their alignment with the cognitive map framework. We then present PRONTOVISE (PRogressive ONTOlogy VISualization Explorer), an interactive visualization tool which applies the high-level criteria within its design. We perform a task-based usage scenario to illustrate the design of PRONTOVISE. We conclude with a discussion of the implications of PRONTOVISE and its use of the criteria towards the design of interactive visualization tools which help us develop understanding of the ontological space within ontology datasets.
机译：Ontology数据集，其编码映射域本体的实体，关系和结构的专家定义的复杂对象，越来越多地纳入了基于知识的特征的挑战性的性能。然而，在我们的任务中难以使用本体数据集，而无需首先理解它描述的本体。使用视觉表示和交互设计，交互式可视化工具可以帮助我们学习和发展我们对不熟悉的本体的理解。在查看可视化本体数据集的现有工具之后，我们发现当前的设计实践在尝试建立本体数据集中的本体空间内的本体空间时，支持学习任务。在具有陌生空间的遇到期间，我们的认知过程与认知地图形成的理论框架对齐。此外，为促进认知地图形成的设计遭遇可以在学习任务期间提高我们的性能。在本文中，我们研究了在学习任务期间的认知负荷，认知地图形成和交互式可视化的使用工作。从这些调查结果中，我们正规化一组高级设计标准，用于可视化本体数据集，以促进学习任务期间的认知地图地层。然后，我们对现有工具进行审查，这些工具可视化本体数据集并评估其与认知地图框架对齐的界面设计。然后，我们在其设计中呈现不可展开（逐行本体可视化资源化探索器），该工具应用于其设计内的高级标准。我们执行基于任务的使用场景，以说明不垂吞的设计。我们讨论了对非诺维的影响及其对互动可视化工具设计的标准的影响，这有助于我们在本体数据集中制定对本体空间内的本体空间的理解。

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