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

Abstract

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.