Chuangtao Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Chuangtao Ma has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Arijit Khan
Aalborg University Denmark
2 shared publications - Haofen Wang
Tongji University CHINA
1 shared publication - Paul Groth
University of Amsterdam
1 shared publication - Sebastian Schelter
BIFOLD & TU Berlin, Berlin, Germany
1 shared publication - Tianxing Wu
Southeast University
1 shared publication - Yongrui Chen
School of Computer Science and Engineering Southeast University China
1 shared publication
Latest Publications
- Open Access
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration
Retrieval-augmented generation (RAG) enhances LLM reasoning in knowledge-intensive tasks, but existing RAG pipelines incur substantial retrieval and generation overhead when applied to large-scale entity matching. To address this limitation, we introduce CE-RAG4EM, a cost-efficient RAG architecture that reduces computation through blocking-based batch retrieval and generation. We also present a unified framework for analyzing and evaluating RAG systems for entity matching, focusing on blocking-aware optimizations and retrieval granularity. Extensive experiments suggest that CE-RAG4EM can achieve comparable or improved matching quality while substantially reducing end-to-end runtime relative to strong baselines. Our analysis further reveals that key configuration parameters introduce an inherent trade-off between performance and overhead, offering practical guidance for designing efficient and scalable RAG systems for entity matching and data integration.
Open AccessCost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Exploration
Retrieval-augmented generation (RAG) enhances LLM reasoning in knowledge-intensive tasks, but existing RAG pipelines incur substantial retrieval and generation overhead when applied to large-scale entity matching. To address this limitation, we introduce CE-RAG4EM, a cost-efficient RAG architecture that reduces computation through blocking-based batch retrieval and generation. We also present a unified framework for analyzing and evaluating RAG systems for entity matching, focusing on blocking-aware optimizations and retrieval granularity. Extensive experiments suggest that CE-RAG4EM can achieve comparable or improved matching quality while substantially reducing end-to-end runtime relative to strong baselines. Our analysis further reveals that key configuration parameters introduce an inherent trade-off between performance and overhead, offering practical guidance for designing efficient and scalable RAG systems for entity matching and data integration.
- Top Cited PapersOpen Access
- 1 January 2025
- Published by Association for Computational Linguistics (ACL)
- p. 24589-24608
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
Chuangtao Ma, Yongrui Chen, Tianxing Wu, Arijit Khan, Haofen Wang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.
- 1 January 2025
- Published by Association for Computational Linguistics (ACL)
- p. 24589-24608
Top Cited PapersOpen AccessLarge Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
Chuangtao Ma, Yongrui Chen, Tianxing Wu, Arijit Khan, Haofen Wang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.