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.
Cite
CITATION STYLE
Gábor, K., Zargayouna, H., Tellier, I., Buscaldi, D., & Charnois, T. (2016). A typology of semantic relations dedicated to scientific literature analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9792 LNCS, pp. 26–32). Springer Verlag. https://doi.org/10.1007/978-3-319-53637-8_3
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