Towards establishing a research lineage via identification of significant citations

• Published in: Quantitative science studies 2022-02, Vol.2 (4), p.1511-1528
• Main Authors: Ghosal, Tirthankar, Tiwary, Piyush, Patton, Robert, Stahl, Christopher
• Format: Article
• Language: English
• Subjects: academic influence; Bibliometrics; citation classification; citation graph; citation significance detection; Citations; Classification; machine learning; research lineage; State of the art
• ISSN: 2641-3337; 2641-3337
• DOI: 10.1162/qss_a_00170

Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant previous work. It causes researchers to spend a considerable amount of time building up their literature list. Citations play a crucial role in discovering relevant literature. However, not all citations are created equal. The majority of the citations that a paper receives provide contextual and background information to the citing papers. In those cases, the cited paper is not central to the theme of citing papers. However, some papers build upon a given paper and further the research frontier. In those cases, the concerned cited paper plays a pivotal role in the citing paper. Hence, the nature of the citation that the former receives from the latter is . In this work, we discuss our investigations towards discovering of a given paper. We further show how we can leverage significant citations to build a research lineage via a . We demonstrate the efficacy of our idea with two real-life case studies. Our experiments yield promising results with respect to the current state of the art in classifying , outperforming the earlier ones by a relative margin of 20 points in terms of precision. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for a particular category of papers.