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Mobility in Unsupervised Word Embeddings for Knowledge Extraction—The Scholars’ Trajectories across Research Topics

Author

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  • Gianfranco Lombardo

    (Department of Engineering and Architecture (DIA), University of Parma, 43100 Parma, Italy
    These authors contributed equally to this work.)

  • Michele Tomaiuolo

    (Department of Engineering and Architecture (DIA), University of Parma, 43100 Parma, Italy
    These authors contributed equally to this work.)

  • Monica Mordonini

    (Department of Engineering and Architecture (DIA), University of Parma, 43100 Parma, Italy
    These authors contributed equally to this work.)

  • Gaia Codeluppi

    (Department of Engineering and Architecture (DIA), University of Parma, 43100 Parma, Italy
    These authors contributed equally to this work.)

  • Agostino Poggi

    (Department of Engineering and Architecture (DIA), University of Parma, 43100 Parma, Italy)

Abstract

In the knowledge discovery field of the Big Data domain the analysis of geographic positioning and mobility information plays a key role. At the same time, in the Natural Language Processing (NLP) domain pre-trained models such as BERT and word embedding algorithms such as Word2Vec enabled a rich encoding of words that allows mapping textual data into points of an arbitrary multi-dimensional space, in which the notion of proximity reflects an association among terms or topics. The main contribution of this paper is to show how analytical tools, traditionally adopted to deal with geographic data to measure the mobility of an agent in a time interval, can also be effectively applied to extract knowledge in a semantic realm, such as a semantic space of words and topics, looking for latent trajectories that can benefit the properties of neural network latent representations. As a case study, the Scopus database was queried about works of highly cited researchers in recent years. On this basis, we performed a dynamic analysis, for measuring the Radius of Gyration as an index of the mobility of researchers across scientific topics. The semantic space is built from the automatic analysis of the paper abstracts of each author. In particular, we evaluated two different methodologies to build the semantic space and we found that Word2Vec embeddings perform better than the BERT ones for this task. Finally, The scholars’ trajectories show some latent properties of this model, which also represent new scientific contributions of this work. These properties include ( i ) the correlation between the scientific mobility and the achievement of scientific results, measured through the H-index; ( ii ) differences in the behavior of researchers working in different countries and subjects; and ( iii ) some interesting similarities between mobility patterns in this semantic realm and those typically observed in the case of human mobility.

Suggested Citation

  • Gianfranco Lombardo & Michele Tomaiuolo & Monica Mordonini & Gaia Codeluppi & Agostino Poggi, 2022. "Mobility in Unsupervised Word Embeddings for Knowledge Extraction—The Scholars’ Trajectories across Research Topics," Future Internet, MDPI, vol. 14(1), pages 1-21, January.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:1:p:25-:d:720733
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    References listed on IDEAS

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    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
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    Cited by:

    1. Esra Gündoğan & Mehmet Kaya & Ali Daud, 2023. "Deep learning for journal recommendation system of research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 461-481, January.

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