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Editor matching for academic journals through rich semantic network development

机译：通过丰富的语义网络开发来匹配学术期刊的编辑

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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.
机译：科学公共图书馆（PLOS）是学术期刊的主要发行者，馆藏超过200,000种科学期刊文章。随着图书馆的发展，PLOS面临的主要运营挑战是如何将提交的稿件与他们所合作的近6,000名学术编辑之一进行匹配。我们将此问题归结为减少网络中编辑器和文档之间距离的问题之一，在该网络中，编辑器和文档被建模为由共享功能连接的图形中的节点。我们应用自然语言处理（NLP）和文本挖掘方法（例如主题建模和单词嵌入）在网络中创建新的节点和边缘以减小其规模，从而缩短文档和编辑者之间的距离。这些新的节点和边缘形成了丰富的语义网络，也可以用来表示整个语料库的主题内容。我们的解决方案通过推荐过去曾经编辑过语义相似手稿的潜在编辑者，来迭代PLOS当前用于编辑器匹配的内部工具。编辑比赛质量的提高将提高编辑接受邀请参加手稿评估的速度。这将简化PLOS的内部审核流程，从而加快知识交付速度。

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