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Building text-based temporally linked event network for scientific big data analytics

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Abstract

Events formulate the world of the human being and could be regarded as the semantic units in different granularities for information organization. Extracting events and temporal information from texts plays an important role for information analytics in big data because of the wide use of multilingual texts. This paper surveys existing research work on text-based event temporal resolution and reasoning including identification of events, temporal information resolutions of events in English and Chinese texts, the rule-based temporal relation reasoning between events and relevant temporal representations. For the scientific big data analytics, we point out the shortcomings of existing research work and give the argument about the future research work for advancing identification of events, establishment of temporal relations and reasoning of temporal relations.

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Acknowledgments

This research work was partially supported by National Natural Science of China (Grant Nos. 71503240 and 61371185), Humanities and Social Sciences of Ministry of Education Planning Fund (Grant No. 16YJA710007) and the National Key Technology R&D Program (Grant No. 2015BAH25F01).

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Authors and Affiliations

  1. Institute of Scientific and Technical Information of China, Beijing, 100038, China

    Junsheng Zhang & Changqing Yao

  2. Business School, Beijing Normal University, Beijing, 100875, China

    Yunchuan Sun

  3. College of Journalism and Communication, Beijing Normal University, Beijing, 100875, China

    Zengquan Fang

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  1. Junsheng Zhang
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  2. Changqing Yao
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  3. Yunchuan Sun
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  4. Zengquan Fang
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Correspondence to Yunchuan Sun.

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Zhang, J., Yao, C., Sun, Y. et al. Building text-based temporally linked event network for scientific big data analytics. Pers Ubiquit Comput 20, 743–755 (2016). https://doi.org/10.1007/s00779-016-0940-x

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