Felicitas Löffler

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.

Explorative Analysis of Heterogeneous, Unstructured, and Uncertain Data

We outline a blueprint for the development of new computer science approaches for the management and analysis of big data problems for biodiversity science. Such problems are characterized by a combination of different data sources each of which owns at least one of the typical characteristics of big data (volume, variety, velocity, or veracity). For these problems, we envision a solution that covers different aspects of integrating data sources and algorithms for their analysis on one of the following three layers: At the data layer, there are various data archives of heterogeneous, unstructured, and uncertain data. At the functional layer, the data are analyzed for each archive individually. At the meta-layer, multiple functional archives are combined for complex analysis.

Honey Bee Versus Apis Mellifera: A Semantic Search for Biological Data

While literature portals in the biomedical domain already enhance their search applications with ontological concepts, data portals offering biological primary data still use a classical keyword search. Similar to publications, biological primary data are described along meta information such as author, title, location and time which is stored in a separate file in XML format. Here, we introduce a semantic search for biological data based on metadata files. The search is running over 4.6 million datasets from GFBio - The German Federation for Biological Data (GFBio, https://www.gfbio.org), a national infrastructure for long-term preservation of biological data. The semantic search method used is query expansion. Instead of looking for originally entered keywords the search terms are expanded with related concepts from different biological vocabularies. Hosting our own Terminology Service with vocabularies that are tailored to the datasets, we demonstrate how ontological concepts are integrated into the search and how it improves the search result.

ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles

Title ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles Publication Type Journal Article Year of Publication 2017 Authors Sateli, B. [14], F. Löffler [15], B. König-Ries [16], and R. Witte [17] Refereed Designation Refereed Journal PeerJ Computer Science Volume 3 Pagination e121 Date Published 07/2017 ISSN 2376-5992