Abstract
This paper is the first to establish the impact of colour on users’ ability to interpret the informational content of concept diagrams, a logic designed for ontology engineering. Motivation comes from results for Euler diagrams, which form a fragment of concept diagrams: manipulating curve colours affects user performance. In particular, using distinct curve colours yields significant performance benefits in Euler diagrams. Naturally, one would expect to obtain similar empirical results for concept diagrams, since colour is a graphical feature to which we are perceptually sensitive. Thus, this paper sets out to test this expectation by conducting a crowdsourced empirical study involving 261 participants. Our study suggests that manipulating curve colours no longer yields significant performance differences in this syntactically richer logic. Consequently, when using colour to visually group syntactic elements with common semantic properties, we ask how different do the elements’ shapes need to be in order for there to be significant performance benefits arising from using colours?
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Concept diagrams were developed specifically for ontology engineering. Other visual ‘ontology’ notations include SOVA [15], which is based on node-link diagrams and, thus, is syntactically very different from concept diagrams.
- 2.
We acknowledge the blurring between syntax and semantics here; strictly speaking, A and B are monadic predicates and p is a dyadic predicate.
- 3.
Whilst [2] reports on OWL and DL, their study also included a third treatment: concept diagrams. None of the diagrams used in our studies were syntactically identical to Alharbi et al.’s diagrams; we adjusted the layouts and represented Some statements differently. Our training material was not the same as that provided by Alharbi et al., in part since we followed a crowdsourced approach.
- 4.
The impact of the three colourblind participants on the data collected was not significant.
References
Data: https://research.gold.ac.uk/id/eprint/31899/1/dataSetTidy_forWeb.xlsx, Materials: https://research.gold.ac.uk/id/eprint/31899/
Alharbi, E., Howse, J., Stapleton, G., Hamie, A., Touloumis, A.: The efficacy of OWL and DL on user understanding of axioms and their entailments. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 20–36. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68288-4_2
Alharbi, E., Howse, J., Stapleton, G., Hamie, A., Touloumis, A.: Visual logics help people: an evaluation of diagrammatic, textual and symbolic notations. In: Visual Languages and Human-Centric Computing, pp. 255–259. IEEE (2017)
Alqadah, M., Stapleton, G., Howse, J., Chapman, P.: Evaluating the impact of clutter in Euler diagrams. In: Dwyer, T., Purchase, H., Delaney, A. (eds.) Diagrams 2014. LNCS (LNAI), vol. 8578, pp. 108–122. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44043-8_15
Blake, A., Stapleton, G., Rodgers, P., Howse, J.: How should we use colour in Euler diagrams. In: Visual Information Communication and Interaction. ACM (2014)
Blake, A., Stapleton, G., Rodgers, P., Howse, J.: The impact of topological and graphical choices on the perception of Euler diagrams. Inf. Sci. 330, 455–482 (2016)
Callaghan, C.: Interference and dominance in texture segregation: hue, geometric form, and line orientation. Percept. Psychophys. 46, 299–311 (1989). https://doi.org/10.3758/BF03204984
Chen, J., Menezes, N., Bradley, A., North, T.: Opportunities for crowdsourcing research on Amazon Mechanical Turk. Hum. Fact. 5(3), 1 (2011)
Duncan, J., Humphreys, G.: Visual search and stimulus similarity. Psychol. Rev. 96(3), 433 (1989)
Harrower, M., Brewer, C.: ColorBrewer.org: an online tool for selecting colour schemes for maps. Cartographic J. 40(1), 27–37 (2003)
Healey, C.: Choosing effective colours for data visualization. In: 7th Conference on Visualization, pp. 263-ff. IEEE (1996)
Hou, T., Chapman, P., Blake, A.: Antipattern comprehension: an empirical evaluation. In: Formal Ontology in Information Systems, pp. 211–224 (2016)
Hou, T., Chapman, P., Oliver, I.: Measuring perceived clutter in concept diagrams. In: IEEE Visual Languages and Human-Centric Computing, pp. 31–39 (2016)
Howse, J., Stapleton, G., Taylor, K., Chapman, P.: Visualizing ontologies: a case study. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 257–272. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_17
Itzik, N., Reinhartz-Berger, I.: Sova - a tool for semantic and ontological variability analysis. In: CAiSE 2014 Forum, Advanced Information Systems Engineering, pp. 177–184. CEUR (2014)
John, C., Fish, A., Howse, J., Taylor, J.: Exploring the notion of ‘Clutter’ in Euler diagrams. In: Barker-Plummer, D., Cox, R., Swoboda, N. (eds.) Diagrams 2006. LNCS (LNAI), vol. 4045, pp. 267–282. Springer, Heidelberg (2006). https://doi.org/10.1007/11783183_36
Liang, K.Y., Zeger, S.L.: Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22 (1986)
Matsin, L.: Short-term (working) memory. http://www.human-memory.net/types_short.html (2010)
Mineshima, K., Sato, Y., Takemura, R., Okada, M.: How diagrams can support syllogistic reasoning: an empirical study. J. Visual Lang. Comput. 25, 159–169 (2014)
Moody, D.: The “physics’’ of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Software Eng. 35(6), 756–779 (2009)
Rodgers, P., Zhang, L., Purchase, H.: Wellformedness properties in Euler diagrams: which should be used? IEEE Trans. Vis. Comput. Graphics 18(7), 1089–1100 (2012)
Sato, Y., Masuda, S., Someya, Y., Tsujii, T., Watanabe, S.: An fMRI analysis of the efficacy of Euler diagrams in logical reasoning. In: Visual Languages and Human-Centric Computing, pp. 143–151. IEEE (2015)
Sato, Y., Mineshima, K.: How diagrams can support syllogistic reasoning: an empirical study. J Logic, Lang. Inf. 24, 409–456 (2015)
Stapleton, G., Howse, J., Chapman, P., Delaney, A., Burton, J., Oliver, I.: Formalizing concept diagrams. In: International Conference on Distributed Multimedia Systems, pp. 182–187. KSI (2013)
Stenning, K., Cox, R., Oberlander, J.: Contrasting the cognitive effects of graphical and sentential logic teaching: Reasoning, representation and individual differences. Lang. Cognit. Process. 10, 333–354 (1995)
Touloumis, A., Agresti, A., Kateri, M.: Generalized estimating equations for multinomial responses using a local odds ratios parameterization. Biometrics 69(3), 633–640 (2013)
Treinish, L., Rogowitz, B.E.: Why should engineers and scientists be worried about color? IBM, 46, 2009 69(3), 633–640 (2013)
Ware, C.: Information Visualization: Perception for Design, chap. 6.1: Gestalt Laws, pp. 189–205, 2nd edn.. Morgan Kaufmann Pub. Inc. (2004)
Acknowledgements
This research was partially funded by a Leverhulme Trust Research Project Grant (RPG- 2016–082) for the project entitled Accessible Reasoning with Diagrams. Thanks to Eisa Alharbi for supplying experimental materials, associated with [2], on which some of our materials were based.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
McGrath, S. et al. (2022). Evaluating Colour in Concept Diagrams. In: Giardino, V., Linker, S., Burns, R., Bellucci, F., Boucheix, JM., Viana, P. (eds) Diagrammatic Representation and Inference. Diagrams 2022. Lecture Notes in Computer Science(), vol 13462. Springer, Cham. https://doi.org/10.1007/978-3-031-15146-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-15146-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15145-3
Online ISBN: 978-3-031-15146-0
eBook Packages: Computer ScienceComputer Science (R0)