Semantic Models for Adaptive Interactive Systems by Tim Hussein Heiko Paulheim Stephan Lukosch Jürgen Ziegler & Gaëlle Calvary

Author:Tim Hussein, Heiko Paulheim, Stephan Lukosch, Jürgen Ziegler & Gaëlle Calvary
Language: eng
Format: epub
Publisher: Springer London, London

5.4.6 Perception and Knowledge Conversion

Once the coordinated view has been set up, novices can study and interact with the visualized data with the goal of increasing their knowledge or solving specific tasks. This phase is referred to as internalization Wang et al. (2009) (cf. Fig. 5.3-9). As stated in van Wijk (2005), the amount of knowledge gained depends on the kinds of visual representations used, the users’ prior knowledge and their perceptional capabilities. Thus, we took care to address three requirements in our workflow to enhance the internalization process.

To foster Perception and Internalization (cf. Fig. 5.3-9), we follow two approaches. First, as for every interactive application, the user interface and interaction design of the visualization platform must be geared towards novices. As an example, this includes self-descriptive and intuitive mechanisms to configure the coordination as discussed above (cf. Sect. 5.4). Based on our prototypes and a number of small user studies, we are continuously working on these issues towards an elaborated user study. Second, beside the platform itself, the visual representations, i.e., the resulting composite application plays the key role for successful internalization. Thus, we consider the users’ contexts in the recommendation algorithm of visualization components (cf. Sect. 5.4) to offer him the most suitable and understandable graphical representations. Contextual triggers for this are basic user properties (age, mother tongue, disabilities), preferences, usage (mobile vs. stationary) and device characteristics (e.g., the available screen real estate).

Knowledge Tracking and Externalization (cf. Fig. 5.3-10) is a background task that actively supports several phases of the Information Visualization workflow. As Fig. 5.3 shows, its purpose is to extract implicit visualization knowledge in every workflow step to support the upcoming phases. In the following, we briefly present possibilities we see and use in this regard. Augmentation It is worth considering an expert review step for the data augmentation (cf. Sect. 5.4). By checking and updating automatically generated annotations, their knowledge is externalized, which can be the input for further formalization. Here, the proposed the usage of declarative annotation rules proves its value as they (1) are independent of a special dataset and (2) can be revised by Semantic Web experts without programming.

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