ALER: An Active Learning Hybrid System for Efficient Entity Resolution
Summary: ALER combines frozen bi-encoder embeddings, lightweight classifier training, sample/K-Means partitioning, and hybrid confident/confused querying for scalable active-learning ER. It avoids iterative deep-model retraining and NP-hard selection, reducing resolution latency 3.8×. (summarized by gpt-5.6-luna on Jul 09 2026)
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Authors
- 1. Dimitrios Karapiperis (International Hellenic University)
- 2. Leonidas Akritidis (International Hellenic University)
- 3. Panayiotis Bozanis (International Hellenic University)
- 4. Vassilios S. Verykios (Hellenic Open University)
BibTeX Citation
@article{karapiperis_vldb26,
title = {{ALER: An Active Learning Hybrid System for Efficient Entity Resolution}},
author = {Karapiperis, Dimitrios and Akritidis, Leonidas and Bozanis, Panayiotis and Verykios, Vassilios S.},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {8},
pages = {1782--1790},
doi = {10.14778/3811243.3811251},
url = {https://doi.org/10.14778/3811243.3811251},
year = {2026}
}
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