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Framework for Author Name Disambiguation in Scientific Papers Using an Ontological Approach and Deep Learning

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Knowledge Graphs and Semantic Web (KGSWC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1686))

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Abstract

The aim of this paper is to solve the problem of disambiguation of authors’ names in scientific papers. In particular, it focuses on the problem of synonyms and homonyms. Thus, we often find two or more names written in different forms denoting the same person. Moreover, there may be several authors using the same name. To address both the synonym and homonym problems in scientific papers, we propose a framework that uses a hybrid approach of an ontological model and a deep learning model. First, we describe the design of the ontology model, the automatic ontology creation process, and the construction of a weighted co-author network through a set of semantic rules and queries. Second, the selected features are preprocessed during the attribute engineering process to measure the similarity indicator for each feature. Third, the similarity indicators are reduced to a vector space model and used as input to the Deep Learning-based author name disambiguation method to model different types of features. Fourth, the proposed framework is tested on smaller groups of the gold standard large dataset of scientific papers from several international databases named LAGOS-AND and achieves promising results compared to other similar solutions proposed in the literature.

This work is partially supported by Project 3 “ICT supporting the educational processes and the knowledge management in higher education (ELINF)” of the NETWORK University Cooperation “Strengthening of the role of ICT in Cuban Universities for the development of the society”. We thank Carlos Alberto Morell for his useful suggestions and ideas and the team of Li Zhang, Wei Lu and Jinqing Yang for providing the corpus used to train the Doc2Vec model of the gold standard dataset LAGOS-AND.

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Notes

  1. 1.

    http://xmlns.com/foaf/0.1/.

  2. 2.

    http://www.w3c.org/2004/02/skos/.

  3. 3.

    http://www.w3c.org/2003/01/geo/.

  4. 4.

    https://purl.org/ontology/bibo/.

  5. 5.

    https://bioportal.bioontology.org/ontologies/VIVO.

  6. 6.

    https://d-nb.info/standards/elementset/gnd.

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Authors and Affiliations

  1. Universidad Central “Marta Abreu” de Las Villas (UCLV), Carretera a Camajuaní Km 5 1/2, Santa Clara, Cuba

    Lisandra Díaz-de-la-Paz, Leonardo Concepción-Pérez, Jorge Armando Portal-Díaz, Alberto Taboada-Crispi & Amed Abel Leiva-Mederos

  2. UCLV, Centro de Investigaciones de la Informática, Santa Clara, Cuba

    Lisandra Díaz-de-la-Paz, Leonardo Concepción-Pérez, Alberto Taboada-Crispi & Amed Abel Leiva-Mederos

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  1. Lisandra Díaz-de-la-Paz
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  2. Leonardo Concepción-Pérez
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  3. Jorge Armando Portal-Díaz
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Correspondence to Lisandra Díaz-de-la-Paz .

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Editors and Affiliations

  1. EY wavespace/UNIR, Madrid, Spain

    Boris Villazón-Terrazas

  2. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodriguez

  3. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. University of Alcalá, Alcalá de Henares, Spain

    Miguel-Angel Sicilia

  5. Universidad Camilo José Cela, Madrid, Spain

    David Martín-Moncunill

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Díaz-de-la-Paz, L., Concepción-Pérez, L., Portal-Díaz, J.A., Taboada-Crispi, A., Leiva-Mederos, A.A. (2022). Framework for Author Name Disambiguation in Scientific Papers Using an Ontological Approach and Deep Learning. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, MA., Martín-Moncunill, D. (eds) Knowledge Graphs and Semantic Web . KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham. https://doi.org/10.1007/978-3-031-21422-6_16

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