Prof. Dr.-Ing. Ralf Schenkel

Office H 525

Email: schenkeluni-trierde | rschenkelacmorg

Tel.: +49 651 201-2040

ORCID: 0000-0001-5379-5191

Research

Ralf Schenkel works at the intersection of databases, information retrieval and semantic information systems. The focus of his research is on searching semistructured data, including the integration of heterogeneous information sources and the efficiency of large-scale search engines. More recently, he has worked on mining and searching complex argumentation structures and retrieval in digital libraries, especially dblp.

List of scientific publications from dblp and Google Scholar.

Academic Services

Roles at Trier University

from 2026Student advisor Master Data Science
since 2023Chair of the examination committee Master Data Science
since 2023Deputy member of the senate committee for scientific information provisioning and infrastructure
since 2019Member of the council of Faculty IV
since 2018Member of the examination committee Master Data Science
since 2017Member of the finance committee of Faculty IV
2019-2022Spokesperson of the Department Computer Sciences
2018-2022Member of the examination committee Business Informatics (since 2023 deputy member)
2018Chair of an appointment committee
2017-2024Representative of Trier University in the supervisory board of Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Supervised PhD students (finished)

2025Dr. Tobias ZeimetzHybrid and Quality Aware Federations of Linked Data Fragment Interfaces and Traditional RESTful Web APlsUniversität Trier
2022Dr. Lorik DumaniFrequency- and quality-driven methods for ranking and clustering argumentsUniversität Trier
2022Dr. Christin K. KreutzInformation systems on bibliographic metadata for researchersUniversität Trier
2014Dr. Steffen MetzgerUser-centric knowledge extraction and maintenanceUniversität des Saarlandes
2012Dr. Tom CreceliusSocially enhanced search and exploration in social tagging networksUniversität des Saarlandes
2012Dr. Andreas BroschartEfficient query processing and index tuning using proximity scoresUniversität des Saarlandes