For bachelor students we offer German lectures on database systems in addition with paper- or project-oriented seminars. Within a one-year bachelor project students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, search engines and information retrieval enhanced by specialized seminars, master projects and advised master theses.
The Web Science group focuses on various topics related to the Web, such as Information Retrieval, Natural Language Processing, Data Mining, Knowledge Discovery, Social Network Analysis, Entity Linking, and Recommender Systems. The group is particularly interested in Text Mining to deal with the vast amount of unstructured and semi-structured information available on the Web.
Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our data sets and source code.
Tobias Schubotz: Online Temporal Summarization of News Articles (finished, 2014)
Mandy Roick: A Topic-Based Search for Microblog Posts (finished, 2014)
Thorben Lindhauer: A Content-Based Serendipity Model for News Recommendation (finished, 2014)
Which Answer is Best? Predicting Accepted Answers in MOOC Forums
Jenders, Maximilian; Krestel, Ralf; Naumann, Felix
Proceedings of the 25th International Conference Companion on World Wide Web
International World Wide Web Conferences Steering Committee
Massive Open Online Courses (MOOCs) have grown in reach and importance over the last few years, enabling a vast userbase to enroll in online courses. Besides watching videos, user participate in discussion forums to further their understanding of the course material. As in other community-based question-answering communities, in many MOOC forums a user posting a question can mark the answer they are most satisfied with. In this paper, we present a machine learning model that predicts this accepted answer to a forum question using historical forum data.