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IIID |
Expert Forum for Knowledge Presentation | |
Conference |
Preparing for the Future of Knowledge Presentation | |
| Summary | ||
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| Corin Gurr |
A Short Note on Computational Diagrammatics | |
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| Conference presentation Video | ||
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Throughout
the history of engineering, diagrams have been used to model and reason
about systems. In engineering computer systems, the relative novelty
of concepts to be modeled has given rise to a plethora of diagrammatic
languages, often based upon simple “graphs” consisting of
nodes with edges linking them. Graphs have the advantage of being simple
and thus easy to read, yet are rather inexpressive and so are typically
significantly extended and embellished. Such extensions often risk swamping
the simplicity of the underlying graphs with overloaded symbology and
a confusion of textual annotations (Figure 1). Addressing this issue
requires a theory of diagrammatic languages that explains how meaning
can be attached to the components of a language both naturally (by exploiting
intrinsic graphical properties) and intuitively (taking consideration
of human cognition). I have constructed such a theory by analogy to
theories of natural languages as studied in computational linguistics.
This approach, dubbed “Computational Diagrammatics” by a
colleague, separates and clarifies issues of diagram morphology, syntax,
semantics, pragmatics and so forth (Figure 2); facilitating the design
of diagrammatic languages that maximize expressiveness without sacrificing
readability. |
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| Figure 1: A UML (Unified Modeling Language) class diagram. | ||
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| Figure 2: A simple UML class diagram. | ||
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Corin
Gurr |
Corin Gurr is a researcher who combines a background in theoretical Computer Science and AI with a broad understanding of Cognitive Science approaches to the understanding of human communication and reasoning. He has spent the past nine years in the Human Communication Research Centre and School of Informatics at the University of Edinburgh, studying issues of human communication and reasoning, particularly in domains where complex information is distributed amongst numerous cross-disciplinary participants. This work combines semantic and cognitive accounts of representations - and how human users react to them - and is informed through empirical and observational analysis, of both industrial best practice in software engineering and of more general human reasoning. | |
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