Abstract
Motivation Scientists increasingly rely on intelligent information systems to help them in their daily tasks, in particular for managing research objects, like publications or datasets. The relatively young research field of Semantic Publishing has been addressing the question how scientific applications can be improved through semantically rich representations of research objects, in order to facilitate their discovery and re-use. To complement the efforts in this area, we propose an automatic workflow to construct semantic user profiles of scholars, so that scholarly applications, like digital libraries or data repositories, can better understand their users’ interests, tasks, and competences, by incorporating these user profiles in their design. To make the user profiles sharable across applications, we propose to build them based on standard semantic web technologies, in particular the Resource Description Framework (RDF) for representing user profiles and Linked Open Data (LOD) sources for representing competence topics. To avoid the cold start problem, we suggest to automatically populate these profiles by analyzing the publications (co-)authored by users, which we hypothesize reflect their research competences. Results We developed a novel approach, ScholarLens, which can automatically generate semantic user profiles for authors of scholarly literature. For modeling the competences of scholarly users and groups, we surveyed a number of existing linked open data vocabularies. In accordance with the LOD best practices, we propose an RDF Schema (RDFS) based model for competence records that reuses existing vocabularies where appropriate. To automate the creation of semantic user profiles, we developed a complete, automated workflow that can generate semantic user profiles by analyzing full-text research articles through various natural language processing (NLP) techniques. In our method, we start by processing a set of research articles for a given user. Competences are derived by text mining the articles, including syntactic, semantic, and LOD entity linking steps. We then populate a knowledge base in RDF format with user profiles containing the extracted competences.We implemented our approach as an open source library and evaluated our system through two user studies, resulting in mean average precision (MAP) of up to 95%. As part of the evaluation, 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. Availability All software and datasets presented in this paper are available under open source licenses in the supplements and documented at http://www.semanticsoftware.info/semantic-user-profiling-peerj-2016-supplements. Additionally, development releases of ScholarLens are available on our GitHub page: https://github.com/SemanticSoftwareLab/ScholarLens.
Highlights
Researchers increasingly leverage intelligent information systems for managing their research objects, like datasets, publications, or projects
Since social media offers an abundance of user information, we report on implicit profiling approaches using data from social networks that are targeted at general users, rather than only scholars
A subset of the topics located in the rhetorical zones of an article are better candidates? Previously in Sateli & Witte (2015), we investigated the detection of Rhetorical Entities (REs) as sentences in a scholarly document that convey its authors findings or argumentations, like their Claims or Contributions
Summary
Researchers increasingly leverage intelligent information systems for managing their research objects, like datasets, publications, or projects. -called user profiles (Brusilovsky & Millán, 2007) have been adopted in domains like e-learning, recommender systems or personalized news portals (we provide a brief background on user profiling in the ‘Background’). They receive more and more attention in scientific applications, such as expertise retrieval systems. Constructing user profiles requires collecting user information over an extended period of time This gathering process is called user profiling and distinguishes between explicit and implicit user feedback (Gauch et al, 2007). Personalized applications nowadays mainly employ implicit profiling techniques, since they are less intrusive from a user’s perspective
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