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Discovering related scientific literature beyond semantic similarity: a new co-citation approach

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Abstract

We propose a new approach to recommend scientific literature, a domain in which the efficient organization and search of information is crucial. The proposed system relies on the hypothesis that two scientific articles are semantically related if they are co-cited more frequently than they would be by pure chance. This relationship can be quantified by the probability of co-citation, obtained from a null model that statistically defines what we consider pure chance. Looking for article pairs that minimize this probability, the system is able to recommend a ranking of articles in response to a given article. This system is included in the co-occurrence paradigm of the field. More specifically, it is based on co-cites so it can produce recommendations more focused on relatedness than on similarity. Evaluation has been performed on the ACL Anthology collection and on the DBLP dataset, and a new corpus has been compiled to evaluate the capacity of the proposal to find relationships beyond similarity. Results show that the system is able to provide, not only articles similar to the submitted one, but also articles presenting other kind of relations, thus providing diversity, i.e. connections to new topics.

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Notes

  1. These proposals use the relationships to contruct a graph and then they apply algorithms for graphs, such as clustering or page rank.

  2. The corpus has been annotated by the authors, that have a long experience in working with research papers.

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Acknowledgements

This work has been partially supported by the Spanish Ministry of Science and Innovation within the projects PROSA-MED (TIN2016-77820-C3-2-R) and EXTRAE (IMIENS 2017).

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Authors and Affiliations

  1. Computational Reflection Research-Group, Universidad de Oviedo, Oviedo, Spain

    Oscar Rodriguez-Prieto

  2. Natural Language Processing and Information Retrieval Group, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal, 16, 28040, Madrid, Spain

    Lourdes Araujo & Juan Martinez-Romo

  3. IMIENS: Instituto Mixto de Investigación, Escuela Nacional de Sanidad, Monforte de Lemos 5, 28019, Madrid, Spain

    Lourdes Araujo & Juan Martinez-Romo

Authors
  1. Oscar Rodriguez-Prieto
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  2. Lourdes Araujo
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  3. Juan Martinez-Romo
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Correspondence to Lourdes Araujo.

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Rodriguez-Prieto, O., Araujo, L. & Martinez-Romo, J. Discovering related scientific literature beyond semantic similarity: a new co-citation approach. Scientometrics 120, 105–127 (2019). https://doi.org/10.1007/s11192-019-03125-9

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