IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v98y2014i2d10.1007_s11192-013-1108-3.html
   My bibliography  Save this article

Semantic linkages in research information systems as a new data source for scientometric studies

Author

Listed:
  • Sergey Parinov

    (Russian Academy of Sciences)

  • Mikhail Kogalovsky

    (Russian Academy of Sciences)

Abstract

A growing number of research information systems use a semantic linkage technique to represent in explicit mode information about relationships between elements of its content. This practice is coming nowadays to a maturity when already existed data on semantically linked research objects and expressed by this scientific relationships can be recognized as a new data source for scientometric studies. Recent activities to provide scientists with tools for expressing in a form of semantic linkages their knowledge, hypotheses and opinions about relationships between available information objects also support this trend. The study presents one of such activities performed within the Socionet research information system with a special focus on (a) taxonomy of scientific relationships, which can exist between research objects, especially between research outputs; and (b) a semantic segment of a research e-infrastructure that includes a semantic interoperability support, a monitoring of changes in linkages and linked objects, notifications and a new model of scientific communication, and at last—scientometric indicators built by processing of semantic linkages data. Based on knowledge what is a semantic linkage data and how it is stored in a research information system we propose an abstract computing model of a new data source. This model helps with better understanding what new indicators can be designed for scientometric studies. Using current semantic linkages data collected in Socionet we present some statistical experiments, including examples of indicators based on two data sets: (a) what objects are linked and (b) what scientific relationships (semantics) are expressed by the linkages.

Suggested Citation

  • Sergey Parinov & Mikhail Kogalovsky, 2014. "Semantic linkages in research information systems as a new data source for scientometric studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 927-943, February.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:2:d:10.1007_s11192-013-1108-3
    DOI: 10.1007/s11192-013-1108-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-013-1108-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-013-1108-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Henry Small, 2011. "Interpreting maps of science using citation context sentiments: a preliminary investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 373-388, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rey-Long Liu, 2017. "A new bibliographic coupling measure with descriptive capability," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 915-935, February.
    2. Ding, Ying & Liu, Xiaozhong & Guo, Chun & Cronin, Blaise, 2013. "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, Elsevier, vol. 7(3), pages 583-592.
    3. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    4. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.
    5. Kai Li & Jason Rollins & Erjia Yan, 2018. "Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 1-20, April.
    6. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    7. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
    8. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    9. Hajar Sotudeh & Mojgan Houshyar, 2018. "Comparing discrimination powers of text and citation-based context types," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 229-251, January.
    10. Hu, Zhigang & Chen, Chaomei & Liu, Zeyuan, 2013. "Where are citations located in the body of scientific articles? A study of the distributions of citation locations," Journal of Informetrics, Elsevier, vol. 7(4), pages 887-896.
    11. Rey-Long Liu, 2015. "Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    12. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
    13. Shengbo Liu & Chaomei Chen & Kun Ding & Bo Wang & Kan Xu & Yuan Lin, 2014. "Literature retrieval based on citation context," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1293-1307, November.
    14. Maryam Yaghtin & Hajar Sotudeh & Mahdieh Mirzabeigi & Seyed Mostafa Fakhrahmad & Mehdi Mohammadi, 2019. "In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 987-1008, May.
    15. Imran Ihsan & M. Abdul Qadir, 2021. "An NLP-based citation reason analysis using CCRO," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4769-4791, June.
    16. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    17. Min Song & Keun Young Kang & Tatsawan Timakum & Xinyuan Zhang, 2020. "Examining influential factors for acknowledgements classification using supervised learning," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    18. Iman Tahamtan & Lutz Bornmann, 2019. "What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1635-1684, December.
    19. Tomaz Bartol & Karmen Stopar, 2015. "Nano language and distribution of article title terms according to power laws," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 435-451, May.
    20. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2023. "Context-aware citation recommendation of scientific papers: comparative study, gaps and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4243-4268, August.

    More about this item

    Keywords

    Research information system; Scientific information objects; Semantic linkages; New data source; Scientometric studies;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:98:y:2014:i:2:d:10.1007_s11192-013-1108-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.