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PGE: Robust Product Graph Embedding Learning for Error Detection

Summary: PGE introduces a noise-tolerant end-to-end embedding framework for product graphs, jointly exploiting text and structure to detect bad triples. It handles free-text attributes and noisy triples, delivering robust error detection on real-world product graphs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12639
Venue
VLDB
Year
2022
Pagerank
4.1905499e-05
Overall Rank
11,371 | 20.98%
DOI
10.14778/3514061.3514074

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
8,747 Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact 2023 VLDB 4.4520434e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
62 Freebase: A Collaboratively Created Graph Database For Structuring Human Knowledge 2008 SIGMOD 0.00064239035
1,197 The LLUNATIC Data-Cleaning Framework 2013 VLDB 0.00013373177
1,340 HoloDetect: Few-Shot Learning for Error Detection 2019 SIGMOD 0.00012492795
1,625 Sampling the Repairs of Functional Dependency Violations under Hard Constraints 2010 VLDB 0.0001109361
3,000 BigDansing: A System for Big Data Cleansing 2015 SIGMOD 7.7447724e-05
3,478 Functional Dependencies for Graphs 2016 SIGMOD 7.0554625e-05
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