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A semantic annotation framework for scientific publications

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  • Yuchul Jung

    (Korea Institute of Science and Technology Information (KISTI))

Abstract

Considering the growing volume of scientific literature, techniques that enable automatic detection of informational entities existing in scientific research articles may contribute to the extension of scientific knowledge and practical usages. Although there have been several efforts to extract informative entities from patent and biomedical research articles, there are few attempts in other scientific literatures. In this paper, we introduce an automatic semantic annotation framework for research articles based on entity recognition techniques. Our approach includes tag set modeling for semantic annotation, semi-automatic annotation tool, manual annotation for training data preparation, and supervised machine learning to develop entity type recognition module. For experiments, we choose two different domains, such as information and communication technology and chemical engineering due to their high usages. In addition, we provide three application scenarios of how our annotation framework can be used and extended further. It is to guide potential researchers who are willing to link their own contents with external data.

Suggested Citation

  • Yuchul Jung, 2017. "A semantic annotation framework for scientific publications," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1009-1025, May.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0369-3
    DOI: 10.1007/s11135-016-0369-3
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    References listed on IDEAS

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    1. Park, Han Woo & Leydesdorff, Loet, 2013. "Decomposing social and semantic networks in emerging “big data” research," Journal of Informetrics, Elsevier, vol. 7(3), pages 756-765.
    2. Cajaiba-Santana, Giovany, 2014. "Social innovation: Moving the field forward. A conceptual framework," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 42-51.
    3. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
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    Cited by:

    1. Pieter E. Stek, 2020. "Mapping high R&D city-regions worldwide: a patent heat map approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 279-296, February.

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