Towards establishing a research lineage via identification of significant citations

Abstract

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, further the research frontier. In those cases, the concerned cited paper plays a pivotal role in the citing paper. Hence, the nature of citation the former receives from the latter is significant. In this work, we discuss our investigations towards discovering significant citations of a given paper. We further show how we can leverage significant citations to build a research lineage via a significant citation graph. 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 significant citations, 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.

Exploring and evaluating the index for interdisciplinary breakthrough innovation detection

“…First, the reliance on the number of subject categories of references as the gold standard for distinguishing between interdisciplinary and non-interdisciplinary papers may have inherent limitations in evaluating the results. Second, as pointed out by Ghosal et al. (2022), the presence of inaccuracies in citation data or the inclusion of insignificant citations may introduce bias into the results.…”

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

“…We conjecture that different semantic main path networks will collectively provide a more comprehensive representation of an analyzed domain. Note that, there were also some recent studies relying on citation importance classification (Ghosal et al, 2022;Hassan et al, 2018). Essentially, these approaches weighted citation edges by 1 (important) or 0 (incidental), screened out unimportant citations, did not further processing for knowledge flow analysis.…”