Call for Papers

Big Knowledge deals with fragmented knowledge from heterogeneous, autonomous information sources for complex and evolving relationships, in addition to domain expertise. 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 statistics, machine learning, pattern recognition, knowledge visualization, expert systems, high performance computing, World Wide Web, and big data analytics. 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 covering academic research and industrial applications into Big Knowledge will include, but not limited to:

  • Foundations, algorithms, and models of big knowledge processing

  • Knowledge engineering with big data

  • Machine learning 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 maps

  • 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