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 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 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.
Original language | English |
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Pages (from-to) | 1511-1528 |
Number of pages | 18 |
Journal | Quantitative Science Studies |
Volume | 2 |
Issue number | 4 |
DOIs | |
State | Published - Feb 4 2022 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The views expressed in the article do not necessarily represent the views of the DOE or the U.S. government. The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://energy .gov/downloads/doe-public-access-plan). TG was sponsored by ORNL under the ORISE ASTRO Internship Program. TG also thanks the Oak Ridge Institute for Science and Education (ORISE) for sponsorship for the Advanced Short-Term Research Opportunity (ASTRO) program at the Oak Ridge National Laboratory (ORNL). The ASTRO program is administered by the Oak Ridge Institute for Science and Education (ORISE) for the U.S. Department of Energy. TG also acknowledges the Visvesvaraya PhD fellowship award VISPHD-MEITY-2518 from Digital India Corporation under Ministry of Electronics and Information Technology, Government of India.
Funders | Funder number |
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Digital India Corporation | |
U.S. Department of Energy | |
Oak Ridge National Laboratory | |
Ministry of Electronics and Information technology |
Keywords
- Academic influence
- Citation classification
- Citation graph
- Citation significance detection
- Machine learning
- Research lineage