SystemER: A Human-in-the-loop System for Explainable Entity Resolution
Summary: SystemER: a human-in-the-loop system for explainable entity resolution that learns human-comprehensible rules for transparency, verification, and domain customization. Active learning delivers high-quality ER with few labels, and it supports flat/semi-structured data with Spark-based scale-out. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kun Qian
- 2. Lucian Popa
- 3. Prithviraj Sen
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,965 | BEER: Blocking for Effective Entity Resolution | 2021 | SIGMOD | 5.7951166e-05 |
| 5,034 | Deep Indexed Active Learning for Matching Heterogeneous Entity Representations | 2022 | VLDB | 5.741974e-05 |
| 8,096 | Saga: A Scalable Framework for Optimizing Data Cleaning Pipelines for Machine Learning Applications | 2023 | SIGMOD | 4.583522e-05 |
| 11,390 | Frost: A Platform for Benchmarking and Exploring Data Matching Results | 2022 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 705 | Magellan: Toward Building Entity Matching Management Systems | 2016 | VLDB | 0.00017779048 |
| 2,036 | The return of JedAI: End-to-End Entity Resolution for Structured and Semi-Structured Data | 2018 | VLDB | 9.7066333e-05 |
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