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<h1 align="center" style="text-align:center">Call for Papers</h1>
<h1 align="center" style="text-align:center">AAAI 2025 Workshop on</h1>
<h1 align="center" style="text-align:center">Translational
Institute for Knowledge Axiomatization (TIKA)</h1>
<p class="MsoNormal" align="center" style="text-align:center">Workshop
Website: <a href="https://sites.google.com/view/tika-2025/"
class="moz-txt-link-freetext">https://sites.google.com/view/tika-2025/</a></p>
<p class="MsoNormal" style="text-align:justify"> <a
href="https://www.youtube.com/live/LqwL3CFVG8I?t=3614s">Three
critical revolutions</a> are shaping the landscape of AI: deep
learning, knowledge graphs and automated reasoning. While deep
learning has unleashed powerful tools such as ChatGPT, knowledge
graphs and automated reasoning are providing essential backbone
services for major corporations through applications such as
business intelligence and automated verification.<span
style="mso-spacerun:yes"> </span>The National AI Research
Resource (<a
href="https://new.nsf.gov/focus-areas/artificial-intelligence/nairr">NAIRR</a>)
is addressing all aspects of deep learning.<span
style="mso-spacerun:yes"> </span>There is, however, no
similar resource pooling for knowledge graphs and automated
reasoning. The goal of this workshop is to catalyze a global
effort to fill this gap by creating a Translational Institute
for Knowledge Axiomatization (TIKA). TIKA is envisioned to serve
as a hub for research, education, training, and hosting of
repositories of open-source knowledge based on open data
sources. The institute would focus on "use-inspired" research,
addressing pragmatic issues in translating advances in knowledge
graphs and reasoning to practice.</p>
<h1>Topics of Interest</h1>
<p class="MsoNormal" style="text-align:justify">TIKA is envisioned
to consider explicit knowledge in all forms including structured
data, rules, controlled languages, mathematical formulas, etc.
While AI for science must incorporate domain intelligence, it
should also include knowledge to enable common sense reasoning
and deep inferential reasoning that commercial knowledge graphs
do not do today. The objectives of TIKA would be to pursue more
human like learning that would be much more efficient and would
not require as much training data and effort as the current
generation of deep learning. This form of learning has also been
referred to as "learning by being told" in the sense of
McCarthy's <a
href="http://jmc.stanford.edu/artificial-intelligence/slides/advice03-sli.pdf"
target="_blank">advice taker</a>. TIKA is to advance the
state-of-the-art in knowledge axiomatization by taking into
account experience from prior efforts such as <a
href="https://dl.acm.org/doi/abs/10.1145/219717.219745"
target="_blank">Cyc</a>, <a
href="https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/1783"
target="_blank">Project Halo</a>, <a
href="https://www.wolframalpha.com/" target="_blank">Wolfram
Alpha</a>, <a href="https://www.proto-okn.net/"
target="_blank">ProtoOKN</a> and the <a
href="https://www.ldc.upenn.edu/" target="_blank">Linguistic
Data Consortium</a>. As the goal of the workshop is to serve
as a launchpad for TIKA with input from the broader AI
community, we welcome submissions on topics that will
constructively advance this mission. Some (but not all) of the
topics include:</p>
<p class="MsoListParagraphCxSpFirst"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span>Use
Cases for Knowledge Axiomatization</p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span>Educational
Resources for TIKA</p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span>Technical
Resources for TIKA (e.g., computational representations,
ontologies, domain models, reasoners, etc.)</p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span>Approaches
to transition from domain-specific (little semantics) to
broader, more generalized systems (big semantics)</p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span>Semantic
Challenges in Enterprise Data Architectures</p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span><span
style="mso-spacerun:yes"> </span>Impact of Cyc and related
efforts including lessons for TIKA</p>
<p class="MsoListParagraphCxSpMiddle"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span>Technical
Roadmaps for TIKA including research challenges</p>
<p class="MsoListParagraphCxSpLast"
style="margin-left:38.25pt;mso-add-space:
auto;text-align:justify;text-indent:-.25in;mso-list:l0 level1 lfo1"><span
style="font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
Symbol"><span style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman""> </span></span></span><span
style="mso-spacerun:yes"> </span>Turing Tests for Knowledge
Axiomatization</p>
<h1>Workshop Format</h1>
<p class="MsoNormal" style="text-align:justify">The workshop will
take place over two days, featuring a mixture of invited talks,
panel discussions, and contributed presentations. </p>
<p class="MsoNormal" style="text-align:justify"><span
style="font-size:16.0pt;
line-height:107%;font-family:"Calibri Light",sans-serif;mso-ascii-theme-font:
major-latin;mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:
major-fareast;mso-hansi-theme-font:major-latin;mso-bidi-font-family:"Times New Roman";
mso-bidi-theme-font:major-bidi;color:#2F5496;mso-themecolor:accent1;mso-themeshade:
191">Attendance</span></p>
<p class="MsoNormal">Attendance is based on invitation.</p>
<h1>Submission Guidelines</h1>
<p class="MsoNormal" style="text-align:justify">We welcome
submissions of original research papers (8 pages) as well as
short papers (4-6 pages) focused on case studies,
work-in-progress, or visionary ideas. All submissions must
adhere to the AAAI author guidelines. Submissions should be made
through the workshop's <a
href="https://openreview.net/group?id=AAAI.org/2025/Workshop/TIKA">OpenReview.Net
portal</a>. For any questions, please write to us at <a
href="mailto:tika-2025@googlegroups.com"
class="moz-txt-link-freetext">tika-2025@googlegroups.com</a>.
</p>
<h1>Important Dates</h1>
<p class="MsoNormal" style="margin-bottom:0in;text-align:justify">Submission
Deadline: November 24, 2024</p>
<p class="MsoNormal" style="margin-bottom:0in;text-align:justify">Notification
of Acceptance: December 9, 2024</p>
<p class="MsoNormal" style="margin-bottom:0in;text-align:justify">Workshop
Date: March 3-4, 2025</p>
<h1>Organizing Committee</h1>
<p class="MsoNormal" style="margin-bottom:0in;text-align:justify">Vinay
Chaudhri (RelationalAI) - <a
class="moz-txt-link-abbreviated moz-txt-link-freetext"
href="mailto:vchaudhri@acm.org">vchaudhri@acm.org</a></p>
<p class="MsoNormal" style="margin-bottom:0in;text-align:justify">Chaitanya
Baru (National Science Foundation) - <a
class="moz-txt-link-abbreviated moz-txt-link-freetext"
href="mailto:cbaru@nsf.gov">cbaru@nsf.gov</a></p>
<p class="MsoNormal" style="margin-bottom:0in;text-align:justify">Michael
Genesereth (Stanford University) - <a
class="moz-txt-link-abbreviated moz-txt-link-freetext"
href="mailto:genesereth@stanford.edu">genesereth@stanford.edu</a></p>
<p class="MsoNormal" style="margin-bottom:0in;text-align:justify">Michael
Witbrock (University of Auckland) - <a
class="moz-txt-link-abbreviated moz-txt-link-freetext"
href="mailto:m.witbrock@auckland.ac.nz">m.witbrock@auckland.ac.nz</a></p>
<p class="MsoNormal" style="text-align:justify"> </p>
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