The Data Science Chair researches semantic relations in the web and the real world. Relations exist for example between objects (the apple is in the basket), persons (Tom and Annie are friends) and general concepts (dogs are canines) and may be leveraged from different sources: link networks, hashtags, text, tags or user behaviour. We develop new methods to extract and classify semantic entities and relations and utilize them to build ontologies, which in turn can be applied to various tasks like language recognition or question answering systems.
We are currently working on the following projects:
Here is a list of selected publications. You can find the full list here.
ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings in Proceedings of International Workshop on Semantic Evaluation (SemEval-2018) (2018).
Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning in ISWC’17 (2017).
Extracting Semantics from Unconstrained Navigation on Wikipedia in KI (2016). 30(2) 163–168.
Discovering Shared Conceptualizations in Folksonomies in Web Semantics: Science, Services and Agents on the World Wide Web (2008). 6(1) 38–53.
Ontologies Improve Text Document Clustering in Proc. of the ICDM 03, The 2003 IEEE International Conference on Data Mining (2003). 541–544.