Tutorial at K-CAP 2023: Ordinal Methods for Knowledge Representation and Capture (OrMeKR)

The concept of order (i.e., partial ordered sets) is predominant for perceiving and organizing our physical and social environment, for inferring meaning and explanation from observation, and for searching and rectifying decisions. Compared to metric methods, however, the number of (purely) ordinal methods for capturing knowledge from data is rather small, although in principle they may allow for more comprehensible explanations. The reason for this could be the limited availability of computing resources in the last century, which would have been required for (purely) ordinal computations. Hence, typically relational and especially ordinal data are first embedded in metric spaces for learning. Therefore, in this tutorial we want present and discuss several ordinal methods for representing, analyzing and capturing knowledge, their role in inference and explainability, and their possibilities for knowledge visualization and communication. We will reflect on these topics in a broad sense, i.e., as a tool to capture and compute ontological knowledge or concept hierarchies (e.g., through conceptual exploration) and as a feature for simplifying and visualizing knowledge representations.

Organizers

• Tom Hanika – Contact
  • Institute for Computer Science, University of Hildesheim, Germany
  • Berlin School of Library and Information Science, Humboldt-Universität zu Berlin, Germany
• Dominik Dürrschnabel – Contact
  • Knowledge & Data Engineering Group, University of Kassel, Germany
• Johannes Hirth – Contact
  • Knowledge & Data Engineering Group, University of Kassel, Germany

Tutorial Material

Coming soon.

Tutorial Slides

Tutorial slides will be uploaded here after the conference.