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<h1></h1>
<p><b>Call for Papers:</b><br>
</p>
<h2> Joint Workshop on Knowledge Diversity and Cognitive Aspects of
KR (KoDis/CAKR) </h2>
<p class="lead">Website: <a
href="https://kodis-cakr24.krportal.org/">https://kodis-cakr24.krportal.org/<br>
</a>Co-located with the <a href="https://kr.org/KR2024/">21st
International Conference on Principles of Knowledge
Representation and Reasoning (KR 2024),</a> November 2 – 8, 2024
in Hanoi, Vietnam </p>
<h3>Overview</h3>
<p> This workshop is the joint continuation of the previous Workshop
on Cognitive Aspects of KR (CAKR) and of the Workshop on Knowledge
Diversity (KoDis). In view of partial overlap of topics and target
audience, we organise the KoDis and CAKR workshops jointly this
year. </p>
<p> The KoDis workshop intends to create a space of confluence and a
forum for discussion for researchers interested in knowledge
diversity in a wide sense, including diversity in terms of
diverging perspectives, different beliefs, semantic heterogeneity
and others. The importance of understanding and handling the
different forms of diversity that manifest between knowledge
formalisations (ontologies, knowledge bases, or knowledge graphs)
is widely recognised and has led to the proposal of a variety of
systems of representation, tackling overlapping aspects of this
phenomenon. </p>
<p> Besides understanding the phenomenon and considering formal
models for the representation of knowledge diversity, we are
interested in the variety of reasoning problems that emerge in
this context, including joint reasoning with possibly conflicting
sources, interpreting knowledge from alternative viewpoints,
consolidating the diversity as uncertainty, reasoning by means of
argumentation between the sources and pursuing knowledge
aggregations among others. </p>
<p> A non-exhaustive list of topics of interest for the KoDis
workshop is given below. </p>
<ul>
<li>Philosophical and cognitive analysis of knowledge diversity.</li>
<li>Formal models for the representation of knowledge diversity.</li>
<li>Ontological approaches capturing multiple perspectives and
viewpoints.</li>
<li>Context and concept formation in such systems.</li>
<li>Consistency (or not) in multi-perspective systems; assessment
and mitigation of inconsistencies.</li>
<li>Communication between knowledge-diverse systems.</li>
<li>Argumentation-based approaches for dealing with inconsistency.</li>
<li>Aggregation of diverse or inconsistent knowledge; judgement
aggregation.</li>
<li>Uncertainty in the context of knowledge diversity.</li>
<li>Applications of formal models of knowledge diversity.</li>
</ul>
<p></p>
<p> The CAKR workshop deals with cognitively adequate approaches to
knowledge representation and reasoning. Knowledge representation
is a lively and well-established field of AI, where knowledge and
belief are represented declaratively and suitable for machine
processing. It is often claimed that this declarative nature makes
knowledge representation cognitively more adequate than e.g.
sub-symbolic approaches, such as machine learning. This cognitive
adequacy has important ramifications for the explainability of
approaches in knowledge representation, which on its turn is
essential for the trustworthiness of these approaches. However,
exactly how cognitive adequacy is ensured has been often left
implicit, and connections with cognitive science and psychology
are only recently being taken up. </p>
<p> The goal of the CAKR workshop is to bring together experts from
fields including artificial intelligence, psychology, cognitive
science and philosophy to discuss important questions related to
cognitive aspects of knowledge representation, such as: </p>
<ul>
<li>How can we study the cognitive adequacy of approaches in AI?</li>
<li>Are declarative approaches cognitively more adequate than
other approaches in AI?</li>
<li>What is the connection between cognitive adequacy and
explanatory potential?</li>
<li>How to develop benchmarks for studying cognitive aspects of
AI?</li>
<li>Which results from psychology are relevant for AI?</li>
<li>What is the role of the normative-descriptive distinction in
current developments in AI?</li>
</ul>
<p></p>
<h3>Call for Papers</h3>
<p> To encourage submissions of both mature and preliminary work, we
invite both long and short papers, as well as reports on recently
published papers in reputed venues. Submissions will be
peer-reviewed to ensure quality and relevance to the workshop. The
workshop will include time for audience discussion of the
presentation allowing the group to have a better understanding of
the issues, challenges, and ideas being presented. We plan to At
least one author of each accepted paper will be required to attend
the workshop to present the contribution. </p>
<p> Submissions should be of one of the following types: </p>
<ul>
<li>long papers reporting unpublished research (10–12 pages
excluding references),</li>
<li>short papers reporting unpublished research (5–6 pages
excluding references), or</li>
<li>extended abstracts (up to 3 pages including references)
presenting work relevant to the workshop already published in
other conferences or journals. Such an abstract should summarize
the contributions of the article and its relevance for the
workshop, as well as include bibliographic details of the
article and a link to the article.</li>
</ul>
<p></p>
<h3>Publication</h3>
<p> We plan to publish informal proceedings in the <a
href="http://ceur-ws.org/">CEUR Workshop Proceedings</a>. </p>
<h3>Important Dates</h3>
<p> All dates are given Anywhere on Earth (AoE). </p>
<ul>
<li>Papers due: July 17, 2024</li>
<li>Notification to authors: August 21, 2024</li>
<li>Camera ready version due: September 18, 2024</li>
<li>Workshop date: November 2, 3, or 4, 2024</li>
</ul>
<p></p>
<h3>Organizing Committee</h3>
<p> </p>
<ul>
<li><b>Lucía Gómez Alvarez</b>, Inria UGA, France</li>
<li><b>Jonas Haldimann</b>, FernUniversität in Hagen, Germany</li>
<li><b>Jesse Heyninck</b>, OpenUniversiteit, the Netherlands;
University of Cape Town and CAIR, South Africa</li>
<li><b>Srdjan Vesic</b>, CRIL CNRS Univ. Artois, France</li>
</ul>
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