Proper citation is of great importance in academic writing for it enables
knowledge accumulation and maintains academic integrity. However, citing
properly is not an easy task. For published scientific entities, the
ever-growing academic publications and over-familiarity of terms easily lead to
missing citations. To deal with this situation, we design a special method
Citation Recommendation for Published Scientific Entity (CRPSE) based on the
cooccurrences between published scientific entities and in-text citations in
the same sentences from previous researchers. Experimental outcomes show the
effectiveness of our method in recommending the source papers for published
scientific entities. We further conduct a statistical analysis on missing
citations among papers published in prestigious computer science conferences in
2020. In the 12,278 papers collected, 475 published scientific entities of
computer science and mathematics are found to have missing citations. Many
entities mentioned without citations are found to be well-accepted research
results. On a median basis, the papers proposing these published scientific
entities with missing citations were published 8 years ago, which can be
considered the time frame for a published scientific entity to develop into a
well-accepted concept. For published scientific entities, we appeal for
accurate and full citation of their source papers as required by academic
standards.

本文提出了一种基于已发布的科学实体和其文本引用语境中同句中的共现关系的引文推荐方法（CRPSE），并进行了实验验证。 在 2020 年公认的计算机科学会议中发表的 12,278 篇论文中，发现了 475 个缺失引用的计算机科学和数学领域的已出版科学实体，并呼吁准确和完整地引用它们的来源论文。

检测和分析已发表科学实体的缺失引用

Detecting and analyzing missing citations to published scientific entities

Citation networks have been widely used to study the evolution of science
through the lenses of the underlying patterns of knowledge flows among academic
papers, authors, research sub-fields, and scientific journals. Here we focus on
citation networks to cast light on the salience of homophily, namely the
principle that similarity breeds connection, for knowledge transfer between
papers. To this end, we assess the degree to which citations tend to occur
between papers that are concerned with seemingly related topics or research
problems. Drawing on a large data set of articles published in the journals of
the American Physical Society between 1893 and 2009, we propose a novel method
for measuring the similarity between articles through the statistical
validation of the overlap between their bibliographies. Results suggest that
the probability of a citation made by one article to another is indeed an
increasing function of the similarity between the two articles. Our study also
enables us to uncover missing citations between pairs of highly related
articles, and may thus help identify barriers to effective knowledge flows. By
quantifying the proportion of missing citations, we conduct a comparative
assessment of distinct journals and research sub-fields in terms of their
ability to facilitate or impede the dissemination of knowledge. Findings
indicate that knowledge transfer seems to be more effectively facilitated by
journals of wide visibility, such as Physical Review Letters, than by
lower-impact ones. Our study has important implications for authors, editors
and reviewers of scientific journals, as well as public preprint repositories,
as it provides a procedure for recommending relevant yet missing references and
properly integrating bibliographies of papers.

通过对 1893 年至 2009 年美国物理学会期刊上发表的文章进行数据分析，研究发现相似主题或相似研究问题的论文之间相互引用的概率随着它们的相似度的增加而增大，并且相似度高的论文之间可能存在遗漏的引用，这有助于破除知识流传的障碍。研究显示，广为人知、影响广泛的物理评论快报等知名期刊更能促进知识传递。