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

Author

Listed:
  • Oscar Rodriguez-Prieto

    (Universidad de Oviedo)

  • Lourdes Araujo

    (Universidad Nacional de Educación a Distancia (UNED)
    Escuela Nacional de Sanidad)

  • Juan Martinez-Romo

    (Universidad Nacional de Educación a Distancia (UNED)
    Escuela Nacional de Sanidad)

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.

Suggested Citation

  • Oscar Rodriguez-Prieto & Lourdes Araujo & Juan Martinez-Romo, 2019. "Discovering related scientific literature beyond semantic similarity: a new co-citation approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 105-127, July.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:1:d:10.1007_s11192-019-03125-9
    DOI: 10.1007/s11192-019-03125-9
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    References listed on IDEAS

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    1. Ying Ding & Erjia Yan & Arthur Frazho & James Caverlee, 2009. "PageRank for ranking authors in co‐citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2229-2243, November.
    2. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    3. Kim, Ha Jin & Jeong, Yoo Kyung & Song, Min, 2016. "Content- and proximity-based author co-citation analysis using citation sentences," Journal of Informetrics, Elsevier, vol. 10(4), pages 954-966.
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    Cited by:

    1. Kamal Sanguri & Atanu Bhuyan & Sabyasachi Patra, 2020. "A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 233-269, October.
    2. Tianshuang Qiu & Chuanming Yu & Yunci Zhong & Lu An & Gang Li, 2021. "A scientific citation recommendation model integrating network and text representations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9199-9221, November.
    3. Wei Cheng & Dejun Zheng & Shaoxiong Fu & Jingfeng Cui, 2024. "Closer in time and higher correlation: disclosing the relationship between citation similarity and citation interval," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4495-4512, July.
    4. Zafar Ali & Irfan Ullah & Amin Khan & Asim Ullah Jan & Khan Muhammad, 2021. "An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4083-4119, May.

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