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Research on semantic representation and citation recommendation of scientific papers with multiple semantics fusion

Author

Listed:
  • Yonghe Lu

    (Sun Yat-Sen University)

  • Meilu Yuan

    (Sun Yat-Sen University)

  • Jiaxin Liu

    (Sun Yat-Sen University)

  • Minghong Chen

    (Sun Yat-Sen University)

Abstract

With the growth in scientific papers, citation recommendation which enables researchers to find useful references efficiently and further to promote academic communication and cooperation has become increasingly important. However, little research has been done to explore how to recognize the semantically relevant references according to research scenarios and the context of the paper citation. Motivated by the research gap, the present study attempts to adopt SciBERT to represent text and expand its semantics through the fusion of WordNet knowledge. Further, core themes from references are automatically extracted by TextRank to solve the problem of incomplete content extraction. In this case, the model named SciBERT + DPCNN is constructed for semantic representation and citation recommendation of scientific papers. Afterwards, multiple experiments are designed and implemented in three parts to verify the effectiveness of the model. The first result is that the outcomes of SciBERT + DPCNN obtain the highest among all baseline models. Additionally, when the model performs in 1 WordNet fusion at the end of the sentence, the best outcomes are 84.72%, 84.80%, 84.72%, and 84.71% in terms of accuracy, precision, recall, and F1-score, respectively. Ultimately, for the classification results of the reference structure, the long text ‘title + abstract + TextRank full text (except the title and abstract)’ outperforms most short text ‘title + abstract’ without WordNet fusion. However, when WordNet is fused for the classification, the short text is mostly more accurate than the long text.

Suggested Citation

  • Yonghe Lu & Meilu Yuan & Jiaxin Liu & Minghong Chen, 2023. "Research on semantic representation and citation recommendation of scientific papers with multiple semantics fusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1367-1393, February.
  • Handle: RePEc:spr:scient:v:128:y:2023:i:2:d:10.1007_s11192-022-04566-5
    DOI: 10.1007/s11192-022-04566-5
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    References listed on IDEAS

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    1. Zafar Ali & Irfan Ullah & Amin Ul Haq & Asim Ullah Jan & Khan Muhammad, 2021. "Correction to: An overview and evaluation of citation recommendation models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8771-8771, October.
    2. Shutian Ma & Heng Zhang & Chengzhi Zhang & Xiaozhong Liu, 2021. "Chronological citation recommendation with time preference," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2991-3010, April.
    3. 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.
    4. Chanwoo Jeong & Sion Jang & Eunjeong Park & Sungchul Choi, 2020. "A context-aware citation recommendation model with BERT and graph convolutional networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1907-1922, September.
    5. Chanathip Pornprasit & Xin Liu & Pattararat Kiattipadungkul & Natthawut Kertkeidkachorn & Kyoung-Sook Kim & Thanapon Noraset & Saeed-Ul Hassan & Suppawong Tuarob, 2022. "Enhancing citation recommendation using citation network embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 233-264, January.
    6. Xi Chen & Huan-jing Zhao & Shu Zhao & Jie Chen & Yan-ping Zhang, 2019. "Citation recommendation based on citation tendency," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 937-956, November.
    7. 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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    1. Shicheng Tan & Tao Zhang & Shu Zhao & Yanping Zhang, 2023. "Self-supervised scientific document recommendation based on contrastive learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5027-5049, September.

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