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Exploiting Latent Information in Relational Databases via Word Embedding and Application to Degrees of Disclosure

Summary: Textify relations and train word embeddings on database tokens to expose cross-attribute latent semantics (similarity, analogy, induction) to SQL via UDFs. Apply this cognitive DB for policy-driven degrees of disclosure and semantic sharing, noting engine-integration challenges and theoretical limits of embedding coding. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
330
Venue
CIDR
Year
2019
Pagerank
0.00013035649
Overall Rank
1,251 | 91.30%
DOI
10.1145/nnnnnnn.nnnnnnn

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

Rank Cited Paper Year Venue Pagerank
1,005 FREDDY: Fast Word Embeddings in Database Systems 2018 SIGMOD 0.00014692665
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