Towards Interpretable and Learnable Risk Analysis for Entity Resolution

Summary: Proposes an interpretable, learnable risk-analysis framework for entity resolution that ranks labeled pairs by mislabeling risk. Automatically derives interpretable risk features and trains a learnable model; experiments show higher accuracy than baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5847
Venue: SIGMOD
Year: 2020
Pagerank: 5.1685424e-05
Overall Rank: 10,049 | 31.06%
DOI: 10.1145/3318464.3380572

Incoming Non-self Citations Over Time

Authors

1. Zhaoqiang Chen (Northwestern Polytechnical University)
2. Qun Chen (Northwestern Polytechnical University)
3. Boyi Hou (Northwestern Polytechnical University)
4. Zhanhuai Li (Northwestern Polytechnical University)
5. Guoliang Li (Tsinghua University)

BibTeX Citation

@inproceedings{chen_sigmod20,
        title = {{Towards Interpretable and Learnable Risk Analysis for Entity Resolution}},
        author = {Chen, Zhaoqiang and Chen, Qun and Hou, Boyi and Li, Zhanhuai and Li, Guoliang},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3380572},
        url = {https://dl.acm.org/doi/10.1145/3318464.3380572},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,424	Splitting Tuples of Mismatched Entities	2023	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 20 of 20 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
112	HoloClean: Holistic Data Repairs with Probabilistic Inference	2017	VLDB	0.00032801121
176	Deep Learning for Entity Matching: A Design Space Exploration	2018	SIGMOD	0.00027191081
196	CrowdER: Crowdsourcing Entity Resolution	2012	VLDB	0.00025780596
439	Corleone: Hands-Off Crowdsourcing for Entity Matching	2014	SIGMOD	0.00018464913
489	Distributed Representations of Tuples for Entity Resolution	2018	VLDB	0.0001761456
528	On Active Learning of Record Matching Packages	2010	SIGMOD	0.00017100838
619	Reasoning about Record Matching Rules	2009	VLDB	0.00015707247
626	Entity Resolution: Theory, Practice & Open Challenges	2012	VLDB	0.00015656958
933	Question Selection for Crowd Entity Resolution	2013	VLDB	0.00013111293
1,101	KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing	2015	SIGMOD	0.00012168934
1,443	Crowdsourcing Algorithms for Entity Resolution	2014	VLDB	0.00010773106
2,626	Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning	2015	VLDB	8.3291889e-05
2,711	Progressive Approach to Relational Entity Resolution	2014	VLDB	8.2178865e-05
3,496	Waldo: An Adaptive Human Interface for Crowd Entity Resolution	2017	SIGMOD	7.3634471e-05
3,602	Online Entity Resolution Using an Oracle	2016	VLDB	7.2691521e-05
3,713	Generating Concise Entity Matching Rules	2017	SIGMOD	7.176496e-05
3,983	Crowd-Based Deduplication: An Adaptive Approach	2015	SIGMOD	6.9739851e-05
4,538	Cost-Effective Crowdsourced Entity Resolution: A Partial-Order Approach	2016	SIGMOD	6.6404776e-05
6,908	Cost-Effective Data Annotation using Game-Based Crowdsourcing	2019	VLDB	5.7415834e-05
7,493	Human-in-the-loop Data Integration	2017	VLDB	5.6046905e-05

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8	2,290	ZeroER: Entity Resolution using Zero Labeled Examples	2020	SIGMOD
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