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From Natural Language Processing to Neural Databases

Summary: Neural databases use NLP transformers as localized query engines over natural-language facts. NeuralDB achieves SPJ over thousands of NL facts with high accuracy, yet faces scalability, set-based/aggregation gaps, motivating DB-NLP fusion research. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12294
Venue
VLDB
Year
2021
Pagerank
9.6624862e-05
Overall Rank
2,057 | 85.70%
DOI
10.14778/3447689.3447706

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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
102 The Case for Learned Index Structures 2018 SIGMOD 0.00049545203
206 Constructing an Interactive Natural Language Interface for Relational Databases 2015 VLDB 0.00034667032
221 Deep Entity Matching with Pre-Trained Language Models 2021 VLDB 0.00033121824
300 Deep Learning for Entity Matching: A Design Space Exploration 2018 SIGMOD 0.00028441466
1,198 Crossing the Structure Chasm 2003 CIDR 0.00013366708
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