RecSOI:利用无知声明推荐研究方向

IF 1.6 3区 工程技术 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Adrien Bibal, Nourah M. Salem, Rémi Cardon, Elizabeth K. White, Daniel E. Acuna, Robin Burke, Lawrence E. Hunter
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引用次数: 0

摘要

科学越进步,问题就越多。这种复合式增长会让人难以跟上当前的研究方向。此外,对于初级研究人员来说,如果他们进入的领域已经有大量潜在的富有成效的研究途径,那么这种困难就会更加严重。在本文中,我们提出了一个新颖的任务和研究方向推荐系统 RecSOI,它借鉴了研究文献中的无知声明 (SOI)。通过基于文本元素建立研究人员档案,RecSOI 可根据研究人员的兴趣生成个性化的潜在研究方向推荐。此外,RecSOI 还为推荐的 SOIs 提供上下文,以便用户快速评估研究方向与自己的相关性。在本文中,我们概述了 RecSOI 的功能、实施和评估情况,展示了它在引导研究人员浏览大量潜在研究方向方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RecSOI: recommending research directions using statements of ignorance
The more science advances, the more questions are asked. This compounding growth can make it difficult to keep up with current research directions. Furthermore, this difficulty is exacerbated for junior researchers who enter fields with already large bases of potentially fruitful research avenues. In this paper, we propose a novel task and a recommender system for research directions, RecSOI, that draws from statements of ignorance (SOIs) found in the research literature. By building researchers’ profiles based on textual elements, RecSOI generates personalized recommendations of potential research directions tailored to their interests. In addition, RecSOI provides context for the recommended SOIs, so that users can quickly evaluate how relevant the research direction is for them. In this paper, we provide an overview of RecSOI’s functioning, implementation, and evaluation, demonstrating its effectiveness in guiding researchers through the vast landscape of potential research directions.
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来源期刊
Journal of Biomedical Semantics
Journal of Biomedical Semantics MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
4.20
自引率
5.30%
发文量
28
审稿时长
30 weeks
期刊介绍: Journal of Biomedical Semantics addresses issues of semantic enrichment and semantic processing in the biomedical domain. The scope of the journal covers two main areas: Infrastructure for biomedical semantics: focusing on semantic resources and repositories, meta-data management and resource description, knowledge representation and semantic frameworks, the Biomedical Semantic Web, and semantic interoperability. Semantic mining, annotation, and analysis: focusing on approaches and applications of semantic resources; and tools for investigation, reasoning, prediction, and discoveries in biomedicine.
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