Semantic User Profiles: Learning Scholars' Competences by Analyzing their Publications

Semantic publishing generally targets the enhancement of scientific artifacts, such as articles and datasets, with semantic metadata. However, smarter scholarly applications also require a better model of their users, in order to understand their interests, tasks, and competences. These are generally captured in so-called user profiles. We investigate a number of existing linked open data (LOD) vocabularies and propose a description of scientists' competences in LOD format. To avoid the cold start problem, we suggest to automatically populate these profiles based on the publications (co-)authored by users, which we hypothesize reflect their research competences. Towards this end, we developed the first complete, automated workflow for generating semantic user profiles by analyzing full-text research articles through natural language processing. We evaluated our system with a user study on ten researchers from two different groups, resulting in mean average precision (MAP) of up to 92%. We also analyze the impact of semantic zoning of research articles on the accuracy of the resulting profiles. Finally, we demonstrate how these semantic user profiles can be applied in a number of use cases, including article ranking for personalized search and finding scientists competent in a topic — e.g., to find reviewers for a paper.