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

December 5, 2023

In conjuction with The Twelfth International Conference on Knowledge Capture

*** Abstract ***

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 workshop we want to collect and discuss ordinal methods for capturing and representing knowledge, their role in inference and explainability, and their possibilities for knowledge visualization and communication. We want to reflect on these topics in a broad sense, i.e., as a tool to arrange, compare and compute ontologies or concept hierarchies, as a feature in learning and capturing knowledge, and as a performance measure to evaluate model performance.

*** Topics of Interest ***

- Ordinal Aspects for Knowledge Representation and Knowledge Bases
- Knowledge Visualization using Order Relations
- Ordinal Representation and Analysis of Ontologies
- Data Fidelity and Reliability of Ordinal Methods
- Theory and Application of Order Dimension and Related Notions
- Ordinal Knowledge Spaces and Ordinal Exploration
- Scaling and Processing Ordinal Information
- Metric Structures in Order Relations
- Algorithms for querying Large Ordinal Data
- Knowledge Discovery in metric-ordinal Heterogeneous Representation
- Ordinal Pattern Structures and Motifs
- Methods for Representation Learning of Order Relations
- Drawing of Hierarchical Graphs and Knowledge Structures
- Non-Linear Ranking in Recommendation Applications
- Linear Ordered Knowledge and Learning
- Scheduling and Planning
- Applications of Ordinal Methods to Scientific Knowledge (e.g., from domains such as Biology, Physics, Social Sciences, Digital Humanities, etc.)
- Methodologically Related Fields such as Directed Graphs, Formal Concept Analysis, Conceptual Structures, Relational Data, - Recommendation, Lattice Theory, with a Clear Reference to Order Relations and Knowledge


*** Important Dates ***

Submission: October 15, 2023
Author Notification: October 29, 2023
Camera Ready: November 12, 2023
All submission deadlines are at 23:59:59 Anywhere on Earth (AoE)

*** Submission Guidelines and Conditions ***

OrMeKR will focus on contributions to the theory and application of ordinal methods in the realm of knowledge representation and capture. The workshop welcomes report papers (summaries of past work concerning ordinal methods), research papers (novel results), position papers (discussing issues concerning the usefulness of ordinal methods in KR), and challenge papers (describing limitations and open research questions).

Submissions should have a minimum of 5 pages and shall not exceed 8 pages
Submission must use the provided CEUR Template as provided here
The workshop is not double-blind, hence authors should list their names and affiliations on the submission

Accepted Papers will be published in CEUR Workshop Proceedings corresponding to K-CAP
Authors of accepted workshop papers will present their work in plenary sessions during the workshop on December 5th
Submissions should be emailed to ormekr2023@cs.uni-kassel.de

*** Organizing Committee ***

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

*** Program Committee ***

Agnès Braud, Université de Strasbourg, France
Diana Christea, Babes-Bolyai University, Romania
Pablo Cordero, University of Malaga, Spain
Bernhard Ganter, TU Dresden, Germany
Rokia Missaoui, University of Quebec in Outaouais, Canada
Robert Jäschke, Humboldt-Universität zu Berlin, Germany
Giacomo Kahn, Université Lumière Lyon 2, France
Léonard Kwuida, Bern University of Applied Sciences, Switzerland
Sebastian Rudolph, TU Dresden, Germany
Gerd Stumme, University of Kassel, Germany
Francisco J. Valverde-Albacete, Universidad Rey Juan Carlos, Spain