Sebastian Krause
- Artificial Intelligence top 10%
- Information Systems
- Molecular Biology
- General Dentistry top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Eric MalmiFeiyu XuHans UszkoreitAliaksei SeverynSascha RotheDaniil MirylenkaDirk WeissenbornReinhard Hickel
- Topics
- Natural Language Processing Techniques (15 papers)Topic Modeling (14 papers)Semantic Web and Ontologies (7 papers)
- Partner nations
- GermanyItalySwitzerland
In The Last Decade
Sebastian Krause
19 papers receiving 176 citations
Peers
Comparison fields: 5 of 37
- Artificial Intelligence 167
- Information Systems 22
- Molecular Biology 17
- General Dentistry 11
- Computer Vision and Pattern Recognition 11
Countries citing papers authored by Sebastian Krause
This map shows the geographic impact of Sebastian Krause's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sebastian Krause with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastian Krause more than expected).
Fields of papers citing papers by Sebastian Krause
This network shows the impact of papers produced by Sebastian Krause. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sebastian Krause. The network helps show where Sebastian Krause may publish in the future.
Co-authorship network of co-authors of Sebastian Krause
This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Krause. A scholar is included among the top collaborators of Sebastian Krause based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sebastian Krause. Sebastian Krause is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 77 | |
| 2 | Automatic prediction of discourse connectives | 9 |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 2 | |
| 6 | TEG-REP: A corpus of Textual Entailment Graphs based on Relation Extraction Patterns. | 2 |
| 7 | Creating linked data morphological language resources with MMoOn: the Hebrew morpheme inventory | 7 |
| 8 | 1 | |
| 9 | 16 | |
| 10 | 13 | |
| 11 | 19 | |
| 12 | 3 | |
| 13 | 7 | |
| 14 | 6 | |
| 15 | 4 | |
| 16 | Language Resources and Annotation Tools for Cross-Sentence Relation Extraction | 1 |
| 17 | Annotating Relation Mentions in Tabloid Press | 2 |
| 18 | 51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop | 7 |
| 19 | Boosting Relation Extraction with Limited Closed-World Knowledge | 14 |
About Sebastian Krause
Sebastian Krause is a scholar working on General Dentistry, Artificial Intelligence and Family Practice, having authored 19 papers that have together received 197 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (15 papers), Topic Modeling (14 papers) and Semantic Web and Ontologies (7 papers). The work is most often cited by research in General Dentistry (11 citations), Artificial Intelligence (167 citations) and Periodontics (11 citations). Sebastian Krause has collaborated with scholars based in Germany, Italy and Switzerland. Frequent co-authors include Eric Malmi, Feiyu Xu, Hans Uszkoreit, Aliaksei Severyn, Sascha Rothe, Daniil Mirylenka, Dirk Weissenborn, Reinhard Hickel, Hong Li and Vinay Pitchika. Their work appears in journals such as Language Resources and Evaluation, Journal of Dental Education and Journal of Web Semantics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.