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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 10-17
Zhaoan Dong , DEKE, MOE and School of Information, Renmin University of China, Beijing, China
Jiaheng Lu , Department of Computer Science, University of Helsinki, Finland
Tok Wang Ling , Department of Computer Science, School of Computing, National University of Singapore, Singapore
Scientific literatures contain some academic knowledge which is interesting or valuable but previously unknown. For instance, an algorithm A proposed in one article might have association with algorithm B in another article, while algorithm B is designed based on the definition of C in a third article. Thus we can deduce the relationship A-C based on A-B and B-C. There are also other kinds of academic knowledge such as association between two research communities, historical evolvement of a research topics, etc. But with the exponential growth of research articles that usually published in Portable Document Format (PDF), to discover and acquire potential knowledge poses many practical challenges. Existing algorithmic methods can hardly extend to handle diverse journals and layouts, nor scale up to process massive documents. As crowdsourcing has become a powerful paradigm for problem-solving especially for tasks that are difficult for computer to resolve solely, we state the problem of academic knowledge discovery and acquisition using an hybrid framework, integrating the accuracy of human workers and the speed of automatic algorithms. We briefly introduce a Platform for Academic kNowledge Discovery and Acquisition (PANDA), our current system implementation, as well as some preliminary achievements and promising future directions.
Crowdsourcing, Portable document format, Computer architecture, Knowledge discovery, Layout, Context, Microprocessors

Z. Dong, J. Lu and T. W. Ling, "PANDA: A platform for academic knowledge discovery and acquisition," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 10-17.
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