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Technology Dependency Graph (TDG): A Scientific Literature Mining Based Method for Technology Insight

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Big Scientific Data Management (BigSDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11473))

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Abstract

The increasingly massive amount and open access of literature provide a data foundation for technology insight based on big data analysis. This paper proposes a new technology insight framework based on the text mining-Technology Dependency Graph (TDG). Firstly, an adversarial multitask learning model and distantly-supervised learning model are applied to extract the technology entities and dependency relations with a little labeled sample. Then, a weighted directed graph, i.e., a TDG, is constructed with the technology entities as vertices and the dependency relations as edges. A TDG contains rich and valuable semantic information which represents the support, contribution or relying on relationship between technologies. At the same time, the social network properties of TDG allow researchers to analyze and mine hot topics, key technologies, and technology architecture by using network theories, methods and tools. In the case study, the TDG of DSSC (dye-sensitized solar cell) is constructed. Furthermore, the technology dependency architecture for DSSC is constructed according to a spanning tree out of the TDG, which provides a global perspective for the research of DSSC.

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Notes

  1. 1.

    https://github.com/FudanNLP/adversarial-multi-criteria-learning-for-CWS.

  2. 2.

    https://gephi.org/.

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Acknowledgement

This research was financially supported by the National Natural Science Foundation of China (Grant NO. 61602490).

Author information

Authors and Affiliations

  1. School of Computer, National University of Defense Technology, Changsha, China

    Hui Gao, Lin Gui & Ting Wang

  2. Information Research Center of Military Science, Beijing, China

    Hui Gao & Wei Luo

Authors
  1. Hui Gao
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  2. Wei Luo
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  3. Lin Gui
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  4. Ting Wang
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Corresponding author

Correspondence to Hui Gao .

Editor information

Editors and Affiliations

  1. CNIC, CAS, Beijing, China

    Jianhui Li

  2. Renmin University of China, Beijing, China

    Xiaofeng Meng

  3. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Ying Zhang

  4. CNIC, CAS, Beijing, China

    Wenjuan Cui

  5. Tsinghua University, Beijing, China

    Zhihui Du

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Gao, H., Luo, W., Gui, L., Wang, T. (2019). Technology Dependency Graph (TDG): A Scientific Literature Mining Based Method for Technology Insight. In: Li, J., Meng, X., Zhang, Y., Cui, W., Du, Z. (eds) Big Scientific Data Management. BigSDM 2018. Lecture Notes in Computer Science(), vol 11473. Springer, Cham. https://doi.org/10.1007/978-3-030-28061-1_19

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  • DOI: https://doi.org/10.1007/978-3-030-28061-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28060-4

  • Online ISBN: 978-3-030-28061-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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