Abstract
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.
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Acknowledgement
This manuscript was funded by the project “Repositorio Ecuatoriano de Investigadores” of the “Corporación Ecuatoriana para el Desarrollo de la Investigación y la Academia” (https://www.cedia.edu.ec/) (CEDIA, Spanish Acronym).
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Segarra, J., Sumba, X., Ortiz, J., Gualán, R., Espinoza-Mejia, M., Saquicela, V. (2019). Author-Topic Classification Based on Semantic Knowledge. In: Villazón-Terrazas, B., Hidalgo-Delgado, Y. (eds) Knowledge Graphs and Semantic Web. KGSWC 2019. Communications in Computer and Information Science, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-21395-4_5
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