We propose a novel unsupervised two-phased classification model leveraging from semantic web technologies for discovering common research fields between researchers based on information available from a bibliographic repository and external resources. The first phase performs coarse-grained classification by knowledge disciplines using as reference the disciplines defined in the UNESCO thesaurus. The second phase provides a fine-grained classification by means of a clustering approach combined with external resources. The methodology was applied to the REDI (Semantic Repository of Ecuadorian researchers) project, with remarkable results and thus proving a valuable tool to one of the main REDI’s goals: discover Ecuadorian authors sharing research interests to foster collaborative research efforts.
CITATION STYLE
Segarra, J., Sumba, X., Ortiz, J., Gualán, R., Espinoza-Mejia, M., & Saquicela, V. (2019). Author-Topic Classification Based on Semantic Knowledge. In Communications in Computer and Information Science (Vol. 1029, pp. 56–71). Springer Verlag. https://doi.org/10.1007/978-3-030-21395-4_5
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