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PUREsuggest:基于引用的文献检索与关键词控制排名的可视化探索

PUREsuggest: Citation-Based Literature Search and Visual Exploration with Keyword-Controlled Rankings.

作者信息

Beck Fabian

出版信息

IEEE Trans Vis Comput Graph. 2025 Jan;31(1):316-326. doi: 10.1109/TVCG.2024.3456199. Epub 2024 Nov 25.

DOI:10.1109/TVCG.2024.3456199
PMID:39255137
Abstract

Citations allow quickly identifying related research. If multiple publications are selected as seeds, specific suggestions for related literature can be made based on the number of incoming and outgoing citation links to this selection. Interactively adding recommended publications to the selection refines the next suggestion and incrementally builds a relevant collection of publications. Following this approach, the paper presents a search and foraging approach, PUREsuggest, which combines citation-based suggestions with augmented visualizations of the citation network. The focus and novelty of the approach is, first, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defined keywords, which reflect topics of interests. The system can be used to build new literature collections, to update and assess existing ones, as well as to use the collected literature for identifying relevant experts in the field. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.

摘要

引用有助于快速识别相关研究。如果选择多篇出版物作为种子,那么可以根据与该选择的入站和出站引用链接数量,给出相关文献的具体建议。通过交互式地将推荐出版物添加到选择中,可以完善下一个建议,并逐步构建相关的出版物集合。按照这种方法,本文提出了一种搜索与探索方法PUREsuggest,该方法将基于引用的建议与引用网络的增强可视化相结合。该方法的重点和新颖之处在于,其一,可视化解释排名的透明度;其二,该过程可以通过反映感兴趣主题的用户定义关键词来引导。该系统可用于构建新的文献集合、更新和评估现有文献集合,以及利用收集到的文献来识别该领域的相关专家。我们通过模拟会话评估了推荐方法,并进行了一项用户研究,调查界面支持的搜索策略和使用模式。

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引用本文的文献

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Exploring similarity patterns in a large scientific corpus.探索大型科学语料库中的相似性模式。
PLoS One. 2025 Apr 21;20(4):e0321114. doi: 10.1371/journal.pone.0321114. eCollection 2025.