7,703 research outputs found
A visual representation of part-whole relationships in BFO-conformant ontologies
In the visual representation of ontologies, in particular of part-whole relationships, it is customary to use graph theory as the representational background. We claim here that the standard graph-based approach has a number of limitations, and we propose instead a new representation of part-whole structures for ontologies, and describe the results of experiments designed to show the effectiveness of this new proposal especially as concerns reduction of visual complexity. The proposal is developed to serve visualization of ontologies conformant to the Basic Formal Ontology. But it can be used also for more general applications, particularly in the biomedical domain
A diagrammatic representation for entities and mereotopological relations in ontologies
In the graphical representation of ontologies, it is customary to use graph theory as the representational background. We claim here that the standard graph-based approach has a number of limitations. We focus here on a problem in the graph-based representation of ontologies in complex domains such as biomedical, engineering and manufacturing: lack of mereotopological representation. Based on such limitation, we proposed a diagrammatic way to represent an entity’s structure and various forms of mereotopological relationships between the entities
Understanding the calculus
A number of significant changes have have occurred recently that give us a golden opportunity to review the teaching of calculus. The most obvious is the arrival of the microcomputer in the mathematics classroom, allowing graphic demonstrations and individual investigations into the mathematical ideas. But equally potent are new
insights into mathematics and mathematics education that suggest new ways of approaching the subject.
In this article I shall consider some of the difficulties encountered studying the calculus and outline a viable alternative approach suitable for specialist and non-specialist mathematics students alike
The Impact of Shape on the Perception of Euler Diagrams
Euler diagrams are often used for visualizing data collected into sets. However, there is a significant lack of guidance regarding graphical choices for Euler diagram layout. To address this deficiency, this paper asks the question `does the shape of a closed curve affect a user's comprehension of an Euler diagram?' By empirical study, we establish that curve shape does indeed impact on understandability. Our analysis of performance data indicates that circles perform best, followed by squares, with ellipses and rectangles jointly performing worst. We conclude that, where possible, circles should be used to draw effective Euler diagrams. Further, the ability to discriminate curves from zones and the symmetry of the curve shapes is argued to be important. We utilize perceptual theory to explain these results. As a consequence of this research, improved diagram layout decisions can be made for Euler diagrams whether they are manually or automatically drawn
Perception of Symmetries in Drawings of Graphs
Symmetry is an important factor in human perception in general, as well as in
the visualization of graphs in particular. There are three main types of
symmetry: reflective, translational, and rotational. We report the results of a
human subjects experiment to determine what types of symmetries are more
salient in drawings of graphs. We found statistically significant evidence that
vertical reflective symmetry is the most dominant (when selecting among
vertical reflective, horizontal reflective, and translational). We also found
statistically significant evidence that rotational symmetry is affected by the
number of radial axes (the more, the better), with a notable exception at four
axes.Comment: Appears in the Proceedings of the 26th International Symposium on
Graph Drawing and Network Visualization (GD 2018
Proximity, Communities, and Attributes in Social Network Visualisation
The identification of groups in social networks drawn as graphs is an important task for social scientists whowish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: that is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout
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