Abstract
We propose a method for improving access to scientific literature by analyzing the content of research papers beyond citation links and topic tracking. Our model relies on a typology of explicit semantic relations. These relations are instantiated in the abstract/introduction part of the papers and can be identified automatically using textual data and external ontologies. Preliminary results show a promising precision in unsupervised relationship classification.
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Acknowledgments
This work is part of the program “Investissements d’Avenir” overseen by the French National Research Agency, ANR-10-LABX-0083 (Labex EFL).
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Gábor, K., Zargayouna, H., Tellier, I., Buscaldi, D., Charnois, T. (2016). A Typology of Semantic Relations Dedicated to Scientific Literature Analysis. In: González-Beltrán, A., Osborne, F., Peroni, S. (eds) Semantics, Analytics, Visualization. Enhancing Scholarly Data. SAVE-SD 2016. Lecture Notes in Computer Science(), vol 9792. Springer, Cham. https://doi.org/10.1007/978-3-319-53637-8_3
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DOI: https://doi.org/10.1007/978-3-319-53637-8_3
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