I am a Ph.D. candidate in the focus group Web Data Mining of the Data and Web Science Group. I am supervised by Prof. Dr. Heiko Paulheim.
Research Interests
- X using Deep Learning
- Applicaton of X to Y
Teaching
- Web Data Integration (HWS2023)
- Data Mining (FSS2022)
Publications
- Peeters, R. and Bizer, C. (2023). Using ChatGPT for Entity Matching. In , New Trends in Database and Information Systems : ADBIS 2023 short papers, doctoral consortium and workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings (S. 221–230). Communications in Computer and Information Science, Springer: Cham.
- Peeters, R., Der, R. C. and Bizer, C. (2023). WDC products: A multi-dimensional entity matching benchmark. In , Proceedings 27th International Conference on Extending Database Technology (EDBT 2024), Paestum, Italy, March 25 – March 28 (S. 22–33). OpenProceedings, OpenProceedings.org: Konstanz.
- Korini, K., Peeters, R. and Bizer, C. (2022). SOTAB: The WDC Schema.org table annotation benchmark. In , SemTab 2022 : Proceedings of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, co-located with the 21st International semantic Web Conference (ISWC 2022), virtual conference, October 23–27, 2022 (S. 14–19). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Peeters, R. and Bizer, C. (2022). Cross-language learning for product matching. In , Companion Proceedings of the Web Conference 2022 (S. 236–238). , ACM: New York, NY.
- Peeters, R. and Bizer, C. (2022). Integrating product data using deep learning : Art.-Nr. 11. In , Proceedings of the 7th bwHPC Symposium (S. 59–62). , Universität Ulm: Ulm.
- Peeters, R. and Bizer, C. (2022). Supervised contrastive learning for product matching. In , Companion Proceedings of the Web Conference 2022 (S. 248–251). , ACM: New York, NY.
- Peeters, R. and Bizer, C. (2021). Dual-objective fine-tuning of BERT for entity matching. In , 47th International Conference on Very Large Data Bases (VLDB 2021) : Copenhagen, Denmark, August 16–20, 2021 (S. 1913-1921). Proceedings of the VLDB Endowment, Association of Computing Machinery: New York, NY.
- Peeters, R., Bizer, C. and Glavaš, G. (2020). Intermediate training of BERT for product matching. In , DI2KG 2020 : Proceedings of the 2nd International Workshop on Challenges and Experiences from Data Integration to Knowledge Graphs co-located with 46th International Conference on Very Large Data Bases (VLDB 2020), Tokyo, Japan, August 31, 2020 (S. 1–2). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Peeters, R., Primpeli, A., Wichtlhuber, B. and Bizer, C. (2020). Using schema.org annotations for training and maintaining product matchers. In , WIMS 2020: proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics, Biarritz, France, June 30 – July 3, 2020 (S. 195–204). , ACM: New York, NY.
- Zhang, Z., Bizer, C., Peeters, R. and Primpeli, A. (2020). MWPD2020: Semantic Web challenge on Mining the Web of HTML-embedded product data. In , MWPD 2020 : Proceedings of the Semantic Web Challenge on Mining the Web of HTML-embedded Product Data co-located with the 19th International Semantic Web Conference (ISWC 2020) Athens, Greece, November 5, 2020 (S. 2–18). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Bizer, C., Primpeli, A. and Peeters, R. (2019). Using the Semantic Web as a source of training data. Datenbank-Spektrum, 19, 127–135.
- Primpeli, A., Peeters, R. and Bizer, C. (2019). The WDC training dataset and gold standard for large-scale product matching. In , Companion Proceedings of The 2019 World Wide Web Conference (S. 381–386). , ACM: New York, NY, USA.