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The IEEE International Conference on Big Knowledge (ICBK) provides a premier international forum for presentation of original research results in Big Knowledge opportunities and challenges, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of Big Knowledge, including algorithms, software, systems, and applications. ICBK draws researchers and application developers from a wide range of Big Knowledge related areas such as knowledge engineering, 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 Big Knowledge problems, the conference seeks to continuously advance the state-of-the-art in Big Knowledge. 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. A selected number of best papers will be invited for possible inclusion, in an expanded and revised form, in the Knowledge and Information Systems Journal. Topics of Interest Topics of interest include, but are not limited to: Foundations, algorithms, models, and theory of big knowledge processing. Knowledge engineering with big data. Machine learning, data mining, and statistical methods for big knowledge science and engineering. Acquisition, representation and evolution of fragmented knowledge. Fragmented knowledge modeling and online learning. Knowledge graphs and knowledge map. Topology and fusion on fragmented knowledge. Visualization, personalization, and recommendation of big knowledge navigation and interaction. Big knowledge systems and platforms, and their efficiency, scalability, and privacy. Applications and services of big knowledge in all domains including web, medicine, education, healthcare, and business. Big Health Care Decision Making Crowdsourcing Data-driven Granular Cognitive Computing Deep Learning Edge Computing in Medical Analyticsg Graph Mining Intelligent Computing for Big Knowledge Network and Knowledge Graph Representation Learning Rule and Relationship Discovery Safe Data Analytics Submission Guidelines 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 on the basis of technical quality, relevance to Big Knowledge, originality, significance, and clarity. 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.</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2018-05-20</div> </div> </div>  <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw3" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw3_c0">CCF</th><th id="yw3_c1">CORE</th><th id="yw3_c2">QUALIS</th><th id="yw3_c3">Short</th><th id="yw3_c4">Full Name</th><th id="yw3_c5">Submission</th><th id="yw3_c6">Notification</th><th id="yw3_c7">Conference</th></tr> </thead> <tbody> <tr class="odd"> <td></td><td></td><td></td><td>NCO</td><td><a href="/conference/3645">International Conference on Networks and Communications</a></td><td>2023-01-07</td><td>2023-02-17</td><td>2023-03-25</td></tr> <tr class="even"> 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