Bibrecord-Based Literature Management with Interactive Latent Space Learning

Watanabe, Shingo; Ito, Hiroyoshi; Matsubara, Masaki; Morishima, Atsuyuki

doi:10.1007/978-3-031-21756-2_13

Lecture Notes in Computer Science

2022

DOI: 10.1007/978-3-031-21756-2_13

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Bibrecord-Based Literature Management with Interactive Latent Space Learning

Shingo Watanabe

Hiroyoshi Ito

Masaki Matsubara

et al.

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2024

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Human-in-the-loop latent space learning for biblio-record-based literature management

Watanabe,

Ito,

Matsubara

et al. 2024

Int J Digit Libr

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Every researcher must conduct a literature review, and the document management needs of researchers working on various research topics vary. However, there are two major challenges. First, traditional methods such as the tree hierarchy of document folders and tag-based management are no longer effective with the enormous volume of publications. Second, although their bibliographic information is available to everyone, many papers can only be accessed through paid services. This study attempts to develop an interactive tool for personal literature management based solely on their bibliographic records. To make such a tool possible, we developed a principled “human-in-the-loop latent space learning” method that estimates the management criteria of each researcher based on his or her feedback to calculate the positions of documents in a two-dimensional space on the screen. As a set of bibliographic records forms a graph, our model is naturally designed as a graph-based encoder–decoder model that connects the graph and the space. In addition, we also devised an active learning framework using uncertainty sampling for it. The challenge here is to define the uncertainty in a problem setting. Experiments with ten researchers from the humanities, science, and engineering domains show that the proposed framework provides superior results to a typical graph convolutional encoder–decoder model. In addition, we found that our active learning framework was effective in selecting good samples.

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Human-in-the-loop latent space learning for biblio-record-based literature management

Watanabe,

Ito,

Matsubara

et al. 2024

Int J Digit Libr

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Bibrecord-Based Literature Management with Interactive Latent Space Learning

Cited by 1 publication

References 28 publications

Human-in-the-loop latent space learning for biblio-record-based literature management

Human-in-the-loop latent space learning for biblio-record-based literature management

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