Editor matching for academic journals through rich semantic network development

摘要

The Public Library of Science (PLOS) is a major publisher of academic journals with a collection of over 200,000 scientific journal articles. As the library grows, a key operational challenge for PLOS is the matching of a submitted manuscript with one of the nearly 6,000 academic editors with whom they work. We frame this problem as one of reducing distance between editors and documents in a network, where editors and documents are modeled as nodes in a graph connected by shared features. We apply Natural Language Processing (NLP) and text mining methods, such as topic modeling and word embedding, to create new nodes and edges in the network to reduce its scale, thus reducing the distance between documents and editors. These new nodes and edges form a rich semantic network that may also be used to represent the thematic content of the corpus as a whole. Our solution iterates on PLOS's current internal tool for editor matching, PLOS Match, by recommending potential editors who have edited semantically similar manuscripts in the past. Improved quality of editor matches will increase the rate at which editors accept an invitation to participate in manuscript evaluation. This will streamline PLOS's internal review process and hence speed knowledge delivery.