<!DOCTYPE html>
<html data-lt-installed="true">
<head>
<meta http-equiv="content-type" content="text/html; charset=UTF-8">
</head>
<body style="padding-bottom: 1px;">
<p>***** 1st CALL FOR PAPERS*****:<br>
<br>
First international workshop on </p>
<p>*Ordinal Methods for Knowledge Representation and Capture
(OrMeKR)*</p>
<p>in conjunction with </p>
<p>*The Twelfth International Conference on Knowledge Capture (K-CAP
2023)*<br>
</p>
<p>December 5th, 2023, Pensacola, Florida, USA<br>
</p>
<p>*Submission Deadline: October 15th, 2023*<br>
<br>
1.1 Abstract and Scope:<br>
───────────────────────<br>
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.<br>
<br>
<br>
1.2 Topics of Interest<br>
──────────────────────<br>
• Ordinal Aspects for Knowledge Representation and Knowledge
Bases<br>
• Knowledge Visualization using Order Relations<br>
• Ordinal Representation and Analysis of Ontologies<br>
• Data Fidelity and Reliability of Ordinal Methods<br>
• Theory and Application of Order Dimension and Related Notions<br>
• Ordinal Knowledge Spaces and Ordinal Exploration<br>
• Scaling and Processing Ordinal Information<br>
• Metric Structures in Order Relations<br>
• Algorithms for querying Large Ordinal Data<br>
• Knowledge Discovery in metric-ordinal Heterogeneous
Representation<br>
• Ordinal Pattern Structures and Motifs<br>
• Methods for Representation Learning of Order Relations<br>
• Drawing of Hierarchical Graphs and Knowledge Structures<br>
• Non-Linear Ranking in Recommendation Applications<br>
• Linear Ordered Knowledge and Learning<br>
• Scheduling and Planning<br>
• Applications of Ordinal Methods to Scientific Knowledge (e.g.,
from domains <br>
such as Biology, Physics, Social Sciences, Digital
Humanities, etc.)<br>
• Methodologically Related Fields such as Directed Graphs,
Formal<br>
Concept Analysis, Conceptual Structures, Relational Data,<br>
Recommendation, Lattice Theory, with a Clear Reference to
Order<br>
Relations and Knowledge<br>
<br>
<br>
1.3 Important Dates (all dates are AoE)<br>
───────────────────────────────────────<br>
• Submission: October 15, 2023<br>
• Author Notification: October 29, 2023<br>
• Camera Ready: November 12, 2023<br>
<br>
<br>
1.4 Submission Guidlines and Conditions<br>
───────────────────────────────────────<br>
OrMeKR will focus on contributions to the theory and application
of<br>
ordinal methods in the realm of knowledge representation and<br>
capture. The workshop welcomes *report papers* (summaries of
past work<br>
concerning ordinal methods), *research papers* (novel results),<br>
*position papers* (discussing issues concerning the usefulness
of<br>
ordinal methods in KR), and *challenge papers* (describing
limitations<br>
and open research questions).<br>
<br>
• Submissions should have a minimum of 5 pages and shall not
exceed 8<br>
pages.<br>
• Submission must use the provided CEUR Template:<br>
<a class="moz-txt-link-rfc2396E"
href="https://www.kde.cs.uni-kassel.de/ormekr2023/ceur.zip"
moz-do-not-send="true"><https://www.kde.cs.uni-kassel.de/ormekr2023/ceur.zip></a><br>
• The workshop is not double-blind, hence authors should list
their<br>
names and affiliations on the submission.<br>
• Accepted Papers will be published in CEUR Workshop Proceedings<br>
corresponding to K-CAP.<br>
• Authors of accepted workshop papers will present their work in<br>
plenary sessions during the workshop on December 5th.<br>
• Submissions should be emailed to: *[<a
class="moz-txt-link-abbreviated moz-txt-link-freetext"
href="mailto:ormekr2023@cs.uni-kassel.de" moz-do-not-send="true">ormekr2023@cs.uni-kassel.de</a>]*<br>
<br>
<br>
1.5 Organizing Committee<br>
────────────────────────<br>
• Tom Hanika<br>
⁃ Institute for Computer Science, University of Hildesheim,
Germany<br>
⁃ Berlin School of Library and Information Science,<br>
Humboldt-Universität zu Berlin, Germany<br>
<br>
• Dominik Dürrschnabel<br>
⁃ Knowledge & Data Engineering Group, University of
Kassel, Germany<br>
<br>
• Johannes Hirth<br>
⁃ Knowledge & Data Engineering Group, University of
Kassel, Germany<br>
<br>
<br>
1.6 Program Committee<br>
─────────────────────<br>
• Agnès Braud, Université de Strasbourg, France<br>
• Diana Christea, Babes-Bolyai University, Romania<br>
• Pablo Cordero, University of Malaga, Spain<br>
• Bernhard Ganter, TU Dresden, Germany<br>
• Rokia Missaoui, University of Quebec in Outaouais, Canada<br>
• Robert Jäschke, Humboldt-Universität zu Berlin, Germany<br>
• Giacomo Kahn, Université Lumière Lyon 2, France<br>
• Léonard Kwuida, Bern University of Applied Sciences,
Switzerland<br>
• Sebastian Rudolph, TU Dresden, Germany<br>
• Gerd Stumme, University of Kassel, Germany<br>
• Francisco J. Valverde-Albacete, Universidad Rey Juan Carlos,
Spain<br>
</p>
<lt-container></lt-container>
</body>
</html>