Simplifying Access to Large-scale Structured Datasets by Meta-Profiling with Scalable Training Set Enrichment
Summary: Large-scale structured datasets with millions of tables and diverse schemas hinder topic-centric discovery and querying. A deep-learning driven, unsupervised training-set enrichment yields Meta-profile, a standardized topic interface enabling access to all relevant topical tables across ultra-large corpora. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Sophie Pavia
- 2. Rituparna Khan
- 3. Anna Pyayt
- 4. Michael Gubanov
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 108 | WebTables: Exploring the Power of Tables on the Web | 2008 | VLDB | 0.00048345996 |
| 188 | Applying Model Management to Classical Meta Data Problems | 2003 | CIDR | 0.00035935715 |
| 1,075 | Model Management 2.0: Manipulating Richer Mappings | 2007 | SIGMOD | 0.00014233255 |
| 12,332 | IBM UFO Repository: Object-Oriented Data Integration | 2009 | VLDB | 4.1905499e-05 |
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