<tincludetail><div style="font:Verdana normal 14px;color:#000;"><div style="position:relative;"><div dir="ltr"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><b><span lang="EN-US" style="font-size:14pt">ICKG-2022: IEEE International Conference on Knowledge Graph</span></b></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><b><span lang="EN-US" style="font-size:12pt">November 30-December 1, Orlando, FL, USA</span></b></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><b><span lang="EN-US" style="font-size:12pt">Website:</span></b><span lang="EN-US"> </span><b><span lang="EN-US" style="font-size:12pt"><a href="https://ickg2022.zhonghuapu.com/" target="_blank">https://ickg2022.zhonghuapu.com/</a></span></b></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><b><span lang="EN-US" style="font-size:12pt">----------------------------------------------------------------------------------------------</span></b></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><b><span lang="EN-US" style="font-size:12pt">Call For papers</span></b></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-size:12pt;font-family:Wingdings">l<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><b><span lang="EN-US" style="font-size:12pt">Aims and Scope</span></b></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US">Knowledge Graph deals with fragmented knowledge from heterogeneous, autonomous information sources for complex and evolving relationships, in addition to domain expertise. The IEEE International Conference on Knowledge Graph (ICKG), provides a premier international forum for presentation of original research results in Knowledge Graph opportunities and challenges, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of Knowledge Graph, including algorithms, software, platforms, and applications for knowledge graph construction, maintenance, and inference. ICKG 2022 draws researchers and application developers from a wide range of Knowledge Graph related areas such as knowledge engineering, big knowledge, big data analytics, statistics, machine learning, pattern recognition, data mining, knowledge visualization, high performance computing, and World Wide Web. By promoting novel, high quality research findings, and innovative solutions to challenging Knowledge Graph problems, the conference seeks to continuously advance the state-of-the-art in Knowledge Graph.</span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span style="font-family:"MS Gothic""></span><span lang="EN-US"></span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US">Accepted papers will be published in the conference proceedings by the IEEE Computer Society. Awards will be conferred at the conference on the authors of the best paper and the best student paper. High quality papers will be invited for a special issue of <a href="http://kais.bigke.org/" style="color:rgb(0,51,153)" target="_blank">Knowledge and Information Systems Journal </a> in an expanded and revised form.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">l<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><b><span lang="EN-US" style="font-size:12pt">Important Dates</span></b><span lang="EN-US"></span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US"><a href="https://wi-lab.com/cyberchair/2022/ickg22/scripts/submit.php?subarea=KG" style="color:rgb(0,51,153)" target="_blank">Paper submission (abstract and full paper)</a>: July 31, 2022</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Notification of acceptance/rejection: September 11, 2022</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Conference: November 30-December 1, 2022</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">l<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><b><span lang="EN-US" style="font-size:12pt">Topics of Interest</span></b><span lang="EN-US"></span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US">Foundations, algorithms, models, and theory of Knowledge Graph processing.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Knowledge engineering with big data.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Machine learning, data mining, and statistical methods for Knowledge Graph science and engineering.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Acquisition, representation and evolution of fragmented knowledge.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Fragmented knowledge modeling and online learning.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Knowledge graphs and knowledge maps.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Knowledge graph security, privacy and trust.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Knowledge graphs and IoT data streams.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Geospatial knowledge graphs.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Ontologies and reasoning.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Topology and fusion on fragmented knowledge.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Visualization, personalization, and recommendation of Knowledge Graph navigation and interaction.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Knowledge Graph systems and platforms, and their efficiency, scalability, and privacy.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Applications and services of Knowledge Graph in all domains including web, medicine, education, healthcare, and business.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Big knowledge systems and applications.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Crowdsourcing, deep learning and edge computing for graph mining.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Rule and relationship discovery in knowledge graph computing.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">l<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><b><span lang="EN-US" style="font-size:12pt">Track Topics</span></b><span lang="EN-US"></span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track01: Machine Learning and Knowledge Graphs.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track02: Reasoning with Knowledge Graphs.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track03: Knowledge Graph Analytics and Applications.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track04: Knowledge Graphs and NLP.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track05: Knowledge graphs for Explainable AI.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track06: Multimodal Knowledge Graphs.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track07: Social Network and Representation Learning.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track08: Knowledge Graphs for Cultural Heritage.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track09: Knowledge Graphs for Geospatial Information Systems.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track10: Domain Knowledge Graphs.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track11: Knowledge Graphs for Education.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-family:Wingdings">o<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><span lang="EN-US">Track12: Big Knowledge Systems.</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US"><br></span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US" style="font-size:12pt;font-family:Wingdings">l<span style="font-variant-numeric:normal;font-variant-east-asian:normal;font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman"">  </span></span><b><span lang="EN-US" style="font-size:12pt">Submission Guidelines</span></b></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US">Paper submissions should be no longer than 8 pages, in the IEEE 2-column format, including the bibliography and any possible appendices.Submissions longer than 8 pages will be rejected without review. All submissions will be reviewed by the Program Committee based on technical quality, relevance to Knowledge Graph, originality, significance, and clarity. You can choose to identify a Track Topic number in your submission title (e.g., your_paper_title-Track01) during submission.</span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US"> </span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US">All manuscripts are submitted as full papers and are reviewed based on their scientific merit. The reviewing process is confidential. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks. Manuscripts must be submitted electronically in online submission system. We do not accept email submissions.</span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US"> </span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US">More Information</span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US">More information about ICKG 2022 is at</span></p><p style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px;text-indent:0pt;margin-left:21pt"><span lang="EN-US"></span></p><p class="MsoNormal" style="color:rgb(0,0,0);font-family:Helvetica,"Microsoft Yahei",verdana;font-size:14px"><span lang="EN-US"><a href="https://ickg2022.zhonghuapu.com/" target="_blank">https://ickg2022.zhonghuapu.com/</a></span></p></div></blockquote></div>
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