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The Battleship Approach to the Low Resource Entity Matching Problem
Summary: Battleship-inspired active learning for low-resource entity matching; uses space-aware, distributed tuple-pair representations to gauge informativeness. Outperforms top active-learning baselines with fewer labels, approaching fully trained models.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6728
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 4.3324933e-05
- Overall Rank
- 9,462 | 34.24%
- DOI
-
10.1145/3626711
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 34 |
Similarity Search in High Dimensions via Hashing |
1999 |
VLDB |
0.00076824554 |
| 219 |
Deep Entity Matching with Pre-Trained Language Models |
2021 |
VLDB |
0.00033354456 |
| 293 |
Deep Learning for Entity Matching: A Design Space Exploration |
2018 |
SIGMOD |
0.00028661817 |
| 705 |
Magellan: Toward Building Entity Matching Management Systems |
2016 |
VLDB |
0.00017779048 |
| 740 |
Distributed Representations of Tuples for Entity Resolution |
2018 |
VLDB |
0.00017358024 |
| 1,821 |
Synthesizing Entity Matching Rules by Examples |
2018 |
VLDB |
0.00010406856 |
| 2,449 |
Comparative Analysis of Approximate Blocking Techniques for Entity Resolution |
2016 |
VLDB |
8.7909961e-05 |
| 2,758 |
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching |
2020 |
SIGMOD |
8.1668285e-05 |
| 2,968 |
Raha: A Configuration-Free Error Detection System |
2019 |
SIGMOD |
7.7964476e-05 |
| 3,020 |
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning |
2015 |
VLDB |
7.6987905e-05 |
| 3,057 |
ZeroER: Entity Resolution using Zero Labeled Examples |
2020 |
SIGMOD |
7.6458287e-05 |
| 3,749 |
Learning Expressive Linkage Rules using Genetic Programming |
2012 |
VLDB |
6.7868937e-05 |
| 5,034 |
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations |
2022 |
VLDB |
5.741974e-05 |
| 5,214 |
Dual-Objective Fine-Tuning of BERT for Entity Matching |
2021 |
VLDB |
5.6236713e-05 |
| 5,974 |
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond |
2021 |
SIGMOD |
5.2458154e-05 |
| 6,624 |
ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks |
2021 |
SIGMOD |
4.9841936e-05 |
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