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2019 | OriginalPaper | Chapter

Author-Topic Classification Based on Semantic Knowledge

Authors : José Segarra, Xavier Sumba, José Ortiz, Ronald Gualán, Mauricio Espinoza-Mejia, Víctor Saquicela

Published in: Knowledge Graphs and Semantic Web

Publisher: Springer International Publishing

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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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previous chapter Digital Repositories and Linked Data: Lessons Learned and Challenges

next chapter A General Process for the Semantic Annotation and Enrichment of Electronic Program Guides

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http://dbpedia.org/ontology/academicDiscipline.

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Title: Author-Topic Classification Based on Semantic Knowledge
Authors: José Segarra
Xavier Sumba
José Ortiz
Ronald Gualán
Mauricio Espinoza-Mejia
Víctor Saquicela
Publisher: Springer International Publishing
Book: Knowledge Graphs and Semantic Web
Print ISBN: 978-3-030-21394-7

Electronic ISBN: 978-3-030-21395-4

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-21395-4_5

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