Demonstration of Panda: A Weakly Supervised Entity Matching System
Summary: Panda is a weakly supervised entity-matching system that replaces costly pair labeling with user-written, Snorkel-style labeling functions. Its browser IDE uniquely supports EM-specific LF generation, sampling, debugging, utilities, lifecycle management, and label modeling. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Renzhi Wu (Georgia Institute of Technology)
- 2. Prem Sakala (Georgia Institute of Technology)
- 3. Peng Li (Georgia Institute of Technology)
- 4. Xu Chu (Georgia Institute of Technology)
- 5. Yeye He (Microsoft)
BibTeX Citation
@article{wu_vldb21,
title = {{Demonstration of Panda: A Weakly Supervised Entity Matching System}},
author = {Wu, Renzhi and Sakala, Prem and Li, Peng and Chu, Xu and He, Yeye},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {12},
pages = {2735--2738},
doi = {10.14778/3476311.3476332},
url = {https://doi.org/10.14778/3476311.3476332},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,483 | DADER: Hands-Off Entity Resolution with Domain Adaptation | 2022 | VLDB | 5.3311145e-05 |
| 9,713 | Ground Truth Inference for Weakly Supervised Entity Matching | 2023 | SIGMOD | 5.1350726e-05 |
| 11,750 | VersaMatch: Ontology Matching with Weak Supervision | 2023 | VLDB | 4.9769913e-05 |
| 11,900 | Frost: A Platform for Benchmarking and Exploring Data Matching Results | 2022 | VLDB | 4.9769913e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 158 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD | 0.00028038831 |
| 161 | Robust and Efficient Fuzzy Match for Online Data Cleaning | 2003 | SIGMOD | 0.00027705594 |
| 205 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025171314 |
| 244 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00023307564 |
| 457 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017899824 |
| 530 | Magellan: Toward Building Entity Matching Management Systems | 2016 | VLDB | 0.00016847532 |
| 2,325 | ZeroER: Entity Resolution using Zero Labeled Examples | 2020 | SIGMOD | 8.6308459e-05 |
| 4,171 | Data Integration and Machine Learning: A Natural Synergy | 2018 | SIGMOD | 6.7576159e-05 |
| 4,972 | Auto-FuzzyJoin: Auto-Program Fuzzy Similarity Joins Without Labeled Examples | 2021 | SIGMOD | 6.3300201e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,473 | Generalized Entity Matching with Adaptivity via Large Language Models | 2026 | SIGMOD |
| 2 | 5,815 | Domain Adaptation for Deep Entity Resolution | 2022 | SIGMOD |
| 3 | 8,253 | Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation | 2022 | VLDB |
| 4 | 9,791 | The Battleship Approach to the Low Resource Entity Matching Problem | 2023 | SIGMOD |
| 5 | 10,209 | Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution | 2024 | VLDB |
| 6 | 7,286 | PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching | 2023 | VLDB |
| 7 | 2,514 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB |
| 8 | 2,475 | A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching | 2020 | SIGMOD |
| 9 | 158 | Deep Learning for Entity Matching: A Design Space Exploration | 2018 | SIGMOD |
| 10 | 9,713 | Ground Truth Inference for Weakly Supervised Entity Matching | 2023 | SIGMOD |