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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
- 6727
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 4.3366491e-05
- Overall Rank
- 9,460 | 34.19%
- 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 |
0.00076637636 |
| 221 |
Deep Entity Matching with Pre-Trained Language Models |
2021 |
VLDB |
0.00033121824 |
| 300 |
Deep Learning for Entity Matching: A Design Space Exploration |
2018 |
SIGMOD |
0.00028441466 |
| 712 |
Magellan: Toward Building Entity Matching Management Systems |
2016 |
VLDB |
0.00017732426 |
| 754 |
Distributed Representations of Tuples for Entity Resolution |
2018 |
VLDB |
0.00017117211 |
| 1,831 |
Synthesizing Entity Matching Rules by Examples |
2018 |
VLDB |
0.00010384082 |
| 2,514 |
Comparative Analysis of Approximate Blocking Techniques for Entity Resolution |
2016 |
VLDB |
8.6139012e-05 |
| 2,767 |
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching |
2020 |
SIGMOD |
8.1513883e-05 |
| 2,968 |
Raha: A Configuration-Free Error Detection System |
2019 |
SIGMOD |
7.7985097e-05 |
| 3,118 |
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning |
2015 |
VLDB |
7.5379338e-05 |
| 3,140 |
ZeroER: Entity Resolution using Zero Labeled Examples |
2020 |
SIGMOD |
7.4841763e-05 |
| 3,744 |
Learning Expressive Linkage Rules using Genetic Programming |
2012 |
VLDB |
6.7932071e-05 |
| 5,282 |
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations |
2022 |
VLDB |
5.5864206e-05 |
| 5,533 |
Dual-Objective Fine-Tuning of BERT for Entity Matching |
2021 |
VLDB |
5.4544359e-05 |
| 5,978 |
Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond |
2021 |
SIGMOD |
5.2453012e-05 |
| 6,625 |
ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks |
2021 |
SIGMOD |
4.9889819e-05 |
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