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Using Perceptual Syntax to Enhance Semantic Content in Diagrams

Diagrams are essential in documenting large information systems. They capture, communicate, and leverage knowledge indispensable for solving problems and act as cognitive externalizations (intertwining internal and external processes to extract information from the external world to enhance thought). A diagram provides a mapping from the problem domain to the visual representation by supporting cognitive processes that involve perceptual pattern finding and cognitive symbolic operations. However, not all mappings are equal, and for effectiveness we must embed a diagram's representation with characteristics, which lets users easily perceive meaningful patterns. Consequently, a diagram's effectiveness depends to some extent on how well we construct it as an input to our visual system. In our research, we focus on a class of diagrams commonly referred to as graphs or node-link diagrams. Nodes representing entities, objects, or processes, and links or edges representing relationships between the nodes characterize them. Their most common form is outline circles or boxes denoting nodes and lines of different types representing links between the nodes. Entity-relationship diagrams, software structure diagrams, and data-flow models are examples of node-link diagrams used to model the structure of processes, software, or data.


Pourang Irani, Colin Ware and Maureen Tingley. 2001. Using Perceptual Syntax to Enhance Semantic Content in Diagrams. IEEE Computer Graphics & Applications, 21(5), 76-85.

Bibtext Entry

@ARTICLE { irani01a,
    AUTHOR = { Pourang Irani and Colin Ware and Maureen Tingley },
    TITLE = { Using Perceptual Syntax to Enhance Semantic Content in Diagrams },
    JOURNAL = { IEEE Computer Graphics & Applications },
    VOLUME = { 21 },
    NUMBER = { 5 },
    YEAR = { 2001 },
    PAGES = { 76--85 },
    DOI = { 10.1109/38.946634 },


Pourang Irani

Pourang Irani

Canada Research Chair
at University of British Columbia Okanagan Campus

As well as: , Colin Ware and Maureen Tingley