Toward a Model of Knowledge-Based Graph Comprehension
Toward a Model of Knowledge-Based Graph Comprehension
复制标题
迈向基于知识的图理解模型
DOI：
10.1007/3-540-46037-3_3
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
P. Shah
中科院分区：
文献类型：
--
作者：
E. Freedman;P. Shah
图形理解的研究一直关注相对较低层次的信息提取。然而，实验室研究经常产生相互矛盾的结果，因为现实世界的图表解释需要超越数据呈现来进行推断和解决问题。此外，在真实世界设置中，图形信息是在相关先验知识的上下文中呈现的。根据我们的模型，基于知识的图形理解涉及自上而下和自下而上的交互过程。有几种类型的知识应用于图形：领域知识、图形技能和解释技能。在最初的加工过程中，人们会将图形中的视觉特征分块。然而，先验知识指导视觉特征的处理。我们概述了该模型的关键假设，并展示了该模型如何解释现有数据和生成可测试的预测。
Research on graph comprehension has been concerned with relatively low-level information extraction. However, laboratory studies often produce conflicting findings because real-world graph interpretation requires going beyond the data presentation to make inferences and solve problems. Furthermore, in real-world settings, graphical information is presented in the context of relevant prior knowledge. According to our model, knowledge-based graph comprehension involves an interaction of top-down and bottom up processes. Several types of knowledge are brought to bear on graphs: domain knowledge, graphical skills, and explanatory skills. During the initial processing, people chunk the visual features in the graphs. Nevertheless, prior knowledge guides the processing of visual features. We outline the key assumptions of this model and show how this model explains the extant data and generates testable predictions.
影响因子：
5.4
作者：
KINTSCH, W
通讯作者：
KINTSCH, W