The 8th IEEE International Conference on Big Knowledge

August 9 -10, 2017

Hefei, China


Accepted Papers

(BK237) Towards Question Improvement on Knowledge Sharing Platforms: A Stack Overflow Case Study by Rishabh Gupta and P. Krishna Reddy

(BK253) Keyphrase Extraction Using Sequential Pattern Mining and Entropy by Qingren Wang and Victor Sheng

(BK270) Multi-layered Big Knowledge Visualization Scheme for Comprehending Neoplasm Ontology Content by Ling Zheng, Christopher Ochs, James Geller, Hao Liu, Yehoshua Perl, and Sherri de Coronado

(BK233) Subpopulation-Wise Conditional Correlation Modeling and Analysis by Guozhu Dong and Sanjeev Bhatta

(BK265) Privacy-Preserving Pattern Mining on Online Density Estimates by Michael Geilke and Stefan Kramer

(BK263) Multi-Label Classification using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity by Sophie Burkhardt and Stefan Kramer

(BK239) ST-LDDM: An effective model for urban air quality prediction by Zheyun Xiao, Yang Mu, and Wei Ding

(BK272) Self-learning and Embedding Based Entity Alignment by Saiping Guan, Xiaolong Jin, Yantao Jia, Yuanzhuo Wang, Huawei Shen, and Xueqi Cheng

(BK269) Learning Question Similarity with Recurrent Neural Networks by Borui Ye, Guangyu Feng, Anqi Cui, and Ming Li

(BK213) Analyzing Structure of Terrorist Networks by Using Graph Metrics by M Serkan CINAR, Burkay GENC, Hayri SEVER, and Vijay V RAGHAVAN

(BK225) Iteratively Multiple Projections Optimization for Product Quantization in Nearest Neighbor Search by Jin Li, Xuguang Lan, and Nanning Zheng

(BK214) A Novel Three-way Clustering Algorithm for Mixed-type Data by Hong Yu, Zhihua Chang, and Bing Zhou

(BK219) Granger Causality for Multivariate Time Series Classification by Dandan Yang, Huanhuan Chen, Yinlong Song, and Zhichen Gong

(BK220) Factored Proximity Models for Top-N Recommendations by Athanasios N. Nikolakopoulos, Vassilis Kalantzis, Efstratios Gallopoulos, and John Garofalakis

(BK223) A New Online Feature Selection Method Using Neighborhood Rough Set by Peng Zhou, Xuegang Hu, and Peipei Li

(BK221) Revisit Word Embeddings With Semantic Lexicons for Modeling Lexical Contrast by Jiawei Liu, Zhenyu Liu, and Huanhuan Chen

(BK207) TDN: Twice-least-square Double-parallel Neural Networks by Guoqiang Li and Keith C.C. Chan

(BK227) A Global Flight Networks Analysis Approach using Markov Clustering and PageRank by Kecheng Xu, Teng Long, Shaojie Qiao, Yating Zheng, and Nan Han

(BK224) Exploiting Nonlinear Relationships for Top-N Recommender Systems by Zhao Kang, Chong Peng, Ming Yang, and Qiang Cheng

(BK204) Visual Analytics as a tool to assist the preprocess by Glauber Cini and João F. Valiati

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

Important Dates

  • Paper submission: March 15, 2017  April 7, 2017

  • Notification of acceptance/rejection: May 18, 2017

  • Camera-Ready Papers: June 16, 2017

  • Conference: August 9-10, 2017

All deadlines are 11:59PM, UTC-12; thus submissions are allowed as long as the deadline date is not past anywhere in the world.

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 triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to Big Knowledge, originality, significance, and clarity. 

The following sections give further information for authors.  

1.      Triple blind submission guidelines

ICBK will adopt a triple blind submission and review policy for all submissions. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. 

2.      What is triple blind reviewing?

The traditional blind paper submission hides the referee name from the authors. The triple-blind paper submission and review, in addition, also hides the authors¡¯ names from the referees, and the referees¡¯ names during review discussions. The names of authors and referees remain known only to the PC Chairs, and the author names are disclosed only after the ranking and acceptance of submissions are finalized. Although there is much debate on the merits and perceived benefits of triple blind reviewing, these are not discussed here. Our main purpose is to implement this policy in ICBK toward understanding the influence of the authors¡¯ identity, whether conscious or unconscious, on the reviewer¡¯s attitude toward a submission. Hence it is imperative that all authors of ICBK submissions work on concealing their identity in the content of the paper. It does not suffice to simply remove the authors¡¯ names from the first page.

3.      How to prepare your submissions

  • The authors shall omit their names from the submission. For formatting templates with author and institution information, simply replace all these information in the template by ¡°Anonymous¡±.

  • In the submission, the authors¡¯ should refer to their own prior work like the prior work of any other author, and include all relevant citations. This can be done either by referring to their prior work in the third person or referencing      papers generically. For example, if your name is Jack and you have worked on classification, instead of saying ¡°We extend our earlier classification model (Gordan 2010),¡± you may say ¡°We extend Gordan¡¯s (Gordan 2010) earlier classification model.¡±

  • The authors shall exclude citations to their own work which is not fundamental to understanding the paper, including prior versions (e.g., technical reports, unpublished internal documents) of the submitted paper. They should reference only necessary work using point (2). Hence, do not write: ¡°In our previous work [5]¡± as it reveals that citation 5 is written by the current authors.

  • The authors shall remove mention of funding sources, personal acknowledgments, and other such auxiliary information that could be related to their identities. These can be reinstituted in the camera-ready copy once the paper is accepted for publication.

  • The authors shall make statements on well-known or unique systems that identify an author, as vague in respect to identifying the authors as possible.

  • The submitted files shall be named with care to ensure that authors¡¯ anonymity is not compromised by the file name. E.g., do not name your submission ¡°.pdf¡±, instead give it a name that is descriptive of the title of your paper, such as ¡°NewApproachForClassfication.pdf¡± (or a shorter version of the same).

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.

LaTeX and Word Templates

To help ensure correct formatting, please use the style files for U.S. Letter as template for your submission. These include LaTeX and Word.

Violations of any of the above paper specifications may result in rejection of your paper. Please note that the Latex template does not allow for keywords. If you are using the Latex template, do not include keywords in your paper.


For any issue with ICBK 2017, please email: