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Context sensitive article ranking with citation context analysis

Author

Listed:
  • Metin Doslu

    (Bogazici University)

  • Haluk O. Bingol

    (Bogazici University)

Abstract

It is hard to detect important articles in a specific context. Information retrieval techniques based on full text search can be inaccurate to identify main topics and they are not able to provide an indication about the importance of the article. Generating a citation network is a good way to find most popular articles but this approach is not context aware. The text around a citation mark is generally a good summary of the referred article. So citation context analysis presents an opportunity to use the wisdom of crowd for detecting important articles in a context sensitive way. In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and edge-labeled citation network based on the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network. We showed that this method successfully detects a good subset of most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles for a given search term even though these articles do not contain this search term. This technique can be used in other linked documents including web pages, legal documents, and patents as well as scientific papers.

Suggested Citation

  • Metin Doslu & Haluk O. Bingol, 2016. "Context sensitive article ranking with citation context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 653-671, August.
    • Handle: RePEc:spr:scient:v:108:y:2016:i:2:d:10.1007_s11192-016-1982-6
    DOI: 10.1007/s11192-016-1982-6
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    References listed on IDEAS

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    1. 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.
    2. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
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    Cited by:

    1. 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.
    2. Similo Ngwenya & Nelius Boshoff, 2022. "Different manifestations of ‘context’: examples from a bibliometric study of research in Zimbabwe in Southern Africa," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3911-3933, July.
    3. Hei-Chia Wang & Jen-Wei Cheng & Che-Tsung Yang, 2022. "SentCite: a sentence-level citation recommender based on the salient similarity among multiple segments," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2521-2546, May.
    4. 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.
    5. Toluwase Victor Asubiaro & Isola Ajiferuke, 2022. "Semantic similarity-based credit attribution on citation paths: a method for allocating residual citation to and investigating depth of influence of scientific communications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6257-6277, November.
    6. Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.

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