Ralph Peeters

Researcher

B6, 26, Room C 1.04

Tel.: +49 621 181 2648

ralph.peeters (at) uni-mannheim.de

I am a Ph.D. candidate in the focus group Web-based Systems in the Data and Web Science Group under supervision of Prof. Dr. Christian Bizer.

Research Interests

Entity Matching using Deep Learning
Product Data Integration

Teaching

Data Mining (2019–2023)
Web Mining (FSS2023)
Web Data Integration (HWS19/22/23)
Large-Scale Data Integration Seminar (2020–2023)
Team Projects (HWS2020/2021)

Peeters, R., Brinkmann, A. and Bizer, C. (2024). The Web Data Commons Schema.org Table Corpora. In , WWW '24 companion : companion proceedings of the ACM Web Conference 2024 (S. 1079-1082). , Association for Computing Machinery: New York, NY, United States.
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.
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.

Bizer, C., Primpeli, A. and Peeters, R. (2019). Using the Semantic Web as a source of training data. Datenbank-Spektrum, 19, 127–135.