Abstract
Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chao, G.: Human-computer interaction: process and principles of human-computer interface design. In: 2009 International Conference on Computer and Automation Engineering, pp. 230–233. IEEE (2009)
Dürrschnabel, D., Koyda, M., Stumme, G.: Attribute selection using contranominal scales. In: Braun, T., Gehrke, M., Hanika, T., Hernandez, N. (eds.) ICCS 2021. LNCS (LNAI), vol. 12879, pp. 127–141. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86982-3_10
Ganter, B., Hanika, T., Hirth, J.: Scaling dimension. In: Dürrschnabel, D., López Rodríguez, D. (eds.) ICFCA 2023. LNCS, vol. 13934, pp. 64–77. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35949-1_5
Ganter, B., Wille, R.: Formal concept analysis: mathematical foundations. Springer, Berlin; New York (1999). ISBN 3540627715 9783540627715. http://www.amazon.de/Formal-Concept-Analysis-Mathematical-Foundations/dp/3540627715/ref=sr_1_1?ie=UTF8 &qid=1417077494 &sr=8-1 &keywords=formal+concept+analysis+mathematical+foundations
Hanika, T., Hirth, J.: Conexp-Clj - a research tool for FCA. In: Cristea, D., Le Ber, F., Missaoui, R., Kwuida, L., Sertkaya, B., editors, ICFCA (Supplements), volume 2378 of CEUR Workshop Proceedings, pp. 70–75. CEUR-WS.org (2019)
Hanika, T., Hirth, J.: Knowledge cores in large formal contexts. Ann. Math. Artif. Intell. Apr (2022a). ISSN 1573–7470. https://doi.org/10.1007/s10472-022-09790-6
Hanika, T., Hirth, J.: On the lattice of conceptual measurements. Inf. Sci. 613, 453–468 (2022b). ISSN 0020–0255. https://doi.org/10.1016/j.ins.2022.09.005. https://www.sciencedirect.com/science/article/pii/S0020025522010489
Hanika, T., Hirth, J.: Quantifying the conceptual error in dimensionality reduction. In: Braun, T., Gehrke, M., Hanika, T., Hernandez, N., editors, Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20–22, 2021, Proceedings, volume 12879 of Lecture Notes in Computer Science, pp. 105–118. Springer (2021). https://doi.org/10.1007/978-3-030-86982-3_8
Hirth, J., Horn, V., Stumme, G., Hanika, T.: Ordinal motifs in lattices (2023). CoRR, arXiv. 2304.04827
Kulesza, T., Burnett, M., Wong, W.-K., Stumpf, S.: Principles of explanatory debugging to personalize interactive machine learning. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, pp. 126–137 (2015)
Ibne Mamun, T., Baker, K., Malinowski, H., Hoffman, R.R., Mueller, S.T.: Assessing collaborative explanations of AI using explanation goodness criteria. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 65, pp. 988–993. SAGE Publications Sage CA: Los Angeles, CA (2021)
Mueller, S.T., et al.: Explanation in human-AI systems: a literature meta-review, synopsis of key ideas and publications, and bibliography for explainable AI. arXiv preprint arXiv:1902.01876 (2019)
Mueller, S.T., et al.: Principles of explanation in human-AI systems. arXiv preprint arXiv:2102.04972 (2021)
Papenmeier, A., Englebienne, G., Seifert, C.: How model accuracy and explanation fidelity influence user trust. arXiv preprint arXiv:1907.12652 (2019)
Schwalbe, G., Finzel, B.: A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Min. Knowl. Discov., pp. 1–59 (2023)
Stevens, S.S.: On the theory of scales of measurement. Science 103(2684), 677–680 (1946). ISSN 0036–8075
Tintarev, N., Masthoff, J.: Designing and evaluating explanations for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 479–510. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_15
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hirth, J., Horn, V., Stumme, G., Hanika, T. (2023). Automatic Textual Explanations of Concept Lattices. In: Ojeda-Aciego, M., Sauerwald, K., Jäschke, R. (eds) Graph-Based Representation and Reasoning. ICCS 2023. Lecture Notes in Computer Science(). Springer, Cham. https://doi.org/10.1007/978-3-031-40960-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-40960-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40959-2
Online ISBN: 978-3-031-40960-8
eBook Packages: Computer ScienceComputer Science (R0)