First international workshop on
*Ordinal Methods for Knowledge Representation and Capture (OrMeKR)*
in conjunction with
*The Twelfth International Conference on Knowledge Capture (K-CAP 2023)*
December 5th, 2023, Pensacola, Florida, USA
*Submission Deadline: October 15th, 2023*
1.1 Abstract and Scope:
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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 measure to evaluate model
performance.
1.2 Topics of Interest
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• 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
1.3 Important Dates (all dates are AoE)
───────────────────────────────────────
• Submission: October 15, 2023
• Author Notification: October 29, 2023
• Camera Ready: November 12, 2023
1.4 Submission Guidlines 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:
<https://www.kde.cs.uni-kassel.de/ormekr2023/ceur.zip>
• 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]*
1.5 Organizing Committee
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• Tom Hanika
⁃ Institute for Computer Science, University of Hildesheim, Germany
⁃ Berlin School of Library and Information Science,
Humboldt-Universität zu Berlin, Germany
• Dominik Dürrschnabel
⁃ Knowledge & Data Engineering Group, University of Kassel, Germany
• Johannes Hirth
⁃ Knowledge & Data Engineering Group, University of Kassel, Germany
1.6 Program Committee
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• 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
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