Peter Chapman

Visualizing ontologies: a case study

Concept diagrams were introduced for precisely specifying ontologies in a manner more readily accessible to developers and other stakeholders than symbolic notations. In this paper, we present a case study on the use of concept diagrams in visually specifying the Semantic Sensor Networks (SSN) ontology. The SSN ontology was originally developed by an Incubator Group of the W3C. In the ontology, a sensor is a physical object that implements sensing and an observation is observed by a single sensor. These, and other, roles and concepts are captured visually, but precisely, by concept diagrams. We consider the lessons learnt from developing this visual model and show how to convert description logic axioms into concept diagrams. We also demonstrate how to merge simple concept diagram axioms into more complex axioms, whilst ensuring that diagrams remain relatively uncluttered.

Visualizing Sets: An Empirical Comparison of Diagram Types.

There are a range of diagram types that can be used to visualize sets. However, there is a significant lack of insight into which is the most effective visualization. To address this knowledge gap, this paper empirically evaluates four diagram types: Venn diagrams, Euler diagrams with shading, Euler diagrams without shading, and the less well-known linear diagrams. By collecting performance data (time to complete tasks and error rate), through crowdsourcing, we establish that linear diagrams outperform the other three diagram types in terms of both task completion time and number of errors. Venn diagrams perform worst from both perspectives. Thus, we provide evidence that linear diagrams are the most effective of these four diagram types for representing sets.

Visualizing Sets with Linear Diagrams

This paper presents the first design principles that optimize the visualization of sets using linear diagrams. These principles are justified through empirical studies that evaluate the impact of graphical features on task performance. Linear diagrams represent sets using straight line segments, with line overlaps corresponding to set intersections. This study builds on recent empirical research, which establishes that linear diagrams can be superior to prominent set visualization techniques, namely Euler and Venn diagrams. We address the problem of how to best visualize overlapping sets using linear diagrams. To solve the problem, we investigate which graphical features of linear diagrams significantly impact user task performance. To this end, we conducted seven crowdsourced empirical studies involving a total of 1,760 participants. These studies allowed us to identify the following design principles, which significantly aid task performance: use a minimal number of line segments, use guidelines where overlaps start and end, and draw lines that are thin as opposed to thick bars. We also evaluated the following graphical properties that did not significantly impact task performance: color, orientation, and set order. The results are brought to life through a freely available software implementation that automatically draws linear diagrams with user-controlled graphical choices. An important consequence of our research is that users are now able to create effective visualizations of sets automatically, thus improving human--computer interaction.

Deriving sound inference rules for concept diagrams

The process of designing and modelling an ontology can be difficult, especially if the user finds the syntax to be relatively inaccessible. Providing users with graphical syntax with which they can model and visualise their ontology has the potential to be helpful. Previously, we informally introduced concept diagrams for ontology visualisation and modelling. We present a case study comprising: (a) a set of axioms for an ontology, and (b) a set of theorems that follow from the axioms, together with their proofs. The proofs have been constructed so that they are, in our opinion, of an intuitive style. From these proofs, we derive a set of sound inference rules that can be used to formally reason about ontologies following the same intuitive style. This approach to designing inference rules differs from previous efforts where the primary focus has been on obtaining a set of sound and complete inference rules, rather than on intuitiveness.

Evaluating the Impact of Clutter in Euler Diagrams

Euler diagrams, used to visualize data and as a basis for visual languages, are an effective representation of information, but they can become cluttered. Previous research established a measure of Euler diagram clutter, empirically shown to correspond with how people perceive clutter. However, the impact of clutter on user understanding is unknown. An empirical study was executed with three levels of diagram clutter. We found a significant effect: increased diagram clutter leads to significantly worse performance, measured by time and error rates. In addition, we found a significant effect of zone (a region in the diagram) clutter on time and error rates. Surprisingly, the zones with a middle level of clutter had the highest error rate compared to the zones with lowest and the highest clutter. Also, the questions whose answers were placed in zones with medium clutter had the highest mean time taken to answer questions. In conclusion, both diagram clutter and zone clutter impact the interpretation of Euler diagrams. Consequently, future work will establish whether a single, but cluttered, Euler diagram is a more effective representation of information than multiple, but less cluttered, Euler diagrams.

Visualizing Concepts with Euler Diagrams

We present a new ontology visualization tool, that uses Euler diagrams to represent concepts. The use of Euler diagrams as a visualisation tool allows the visual syntax to be well matched to its meaning.

What can concept diagrams say

Logics that extend the syntax of Euler diagrams include Venn-II, Euler/Venn, spider diagrams and constraint diagrams, which are first-order. We show that concept diagrams can quantify over sets and binary relations, so they are second-order. Thus, concept diagrams are highly expressive compared with other diagrammatic logics.

Introducing Second-Order Spider Diagrams for Defining Regular Languages

There has been significant research effort focussed on the study of regular languages, since they play a vital role in our understanding of computation. This existing research draws a large number of connections with other areas, such as algebra and symbolic logic. Recently, research has begun into how diagrammatic logics can define regular languages, providing another mechanism through which we can understand regular languages. However, the formalised diagrammatic logics are first-order, so they cannot define non-starfree regular languages. The primary contributions of this paper are: (a) to develop and formalise a second-order diagrammatic logic, extending spider diagrams of order, and (b) to establish a class of regular languages that this logic can define. This lays the essential foundations for providing an exact classification of the regular languages that are definable using this new second-order logic.

Creating a second order diagrammatic logic

Many of the formal diagrammatic logics that have been developed are limited to be first order (typically monadic). This means that such logics cannot define commonly occurring concepts and, thus, are not as widely applicable as we might like. Suitably increasing their expressiveness will allow both the formalization of second order concepts and the study of such concepts from a new perspective. Our aim is to produce a second order diagrammatic logic and we present the initial ideas towards the development of such a logic.

The Perception of Clutter in Linear Diagrams

Linear diagrams are an effective way of representing sets and their relationships. The topological and graphical properties of linear diagrams can affect perceived relative levels of clutter. This paper defines four different measures of clutter for linear diagrams. Participants in an empirical study were asked to rank linear diagrams according to their perception of clutter. We analyzed the correlation between how the clutter measures ranked linear diagrams compared to the overall ranking derived from the participants’ perceptions. We concluded that the clutter measure which counts the number of line segments best matches participants’ perception.