Abstract
With the rapid growth of scientific literatures, it is very important to discover the implicit knowledge from the vast information accurately and efficiently. To achieve this goal, we propose a percolation approach to discovering emerging research topics by combining text mining and scientometrics methods based on Subject-Predication-Object (SPO) predications, which consist of a subject argument, an object argument, and the relation that binds them. Firstly, SPO predications are extracted and cleaned from content of literatures to construct SPO semantic networks. Then, community detection is conducted in the SPO semantic networks. Afterwards, two indicators of Research Topic Age (RTA) and Research Topic Authors Number (RTAN) combined by hypervolume-based selection algorithm (HBS) are chosen to identify potential emerging research topics from communities. Finally, scientific literatures of stem cells are selected as a case study, and the result indicates that the approach can effectively and accurately discover the emerging research topics.
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Acknowledgments
The work in this paper was supported by the Informationization Special Project of Chinese Academy of Sciences “E-Science Application for Knowledge Discovery in Stem Cells” (Grant No: XXH13506-203) and the Fundamental Research Funds for the Central Universities (Grant No. A0920502051815-69).
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Hu, Z., Zeng, RQ., Peng, L., Pang, H., Qin, X., Guo, C. (2019). Discovering Emerging Research Topics Based on SPO Predications. In: Uden, L., Ting, IH., Corchado, J. (eds) Knowledge Management in Organizations. KMO 2019. Communications in Computer and Information Science, vol 1027. Springer, Cham. https://doi.org/10.1007/978-3-030-21451-7_10
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