Provenance for Natural Language Queries
Summary: End-to-end NL QA system delivering provenance-enabled answers with explanations tied to query provenance. Introduces two NL provenance views—factorization and summarization—turning large provenance into readable NL, with experiments and a user study on quality and scalability. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Deutch
- 2. Nave Frost
- 3. Amir Gilad
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,286 | SMOKE: Fine-grained Lineage at Interactive Speed | 2018 | VLDB | 9.102574e-05 |
| 3,158 | Fine-Grained, Secure and Efficient Data Provenance on Blockchain Systems | 2019 | VLDB | 7.466958e-05 |
| 5,706 | Putting Things into Context: Rich Explanations for Query Answers using Join Graphs | 2021 | SIGMOD | 5.3633001e-05 |
| 7,360 | ExplainED: Explanations for EDA Notebooks | 2020 | VLDB | 4.7473724e-05 |
| 8,388 | FEDEX: An Explainability Framework for Data Exploration Steps | 2022 | VLDB | 4.525436e-05 |
| 9,043 | Query-Guided Resolution in Uncertain Databases | 2023 | SIGMOD | 4.3997447e-05 |
| 11,555 | Toward Pure Natural Language Interaction with Databases | 2020 | CIDR | 4.1905499e-05 |
| 11,740 | Provenance Summaries for Answers and Non-Answers | 2018 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,665 | Selective Provenance for Datalog Programs Using Top-K Queries | 2015 | VLDB | 4.9657158e-05 |
| 6,369 | DBMSs Should Talk Back Too | 2009 | CIDR | 5.0881732e-05 |
| 11,555 | Toward Pure Natural Language Interaction with Databases | 2020 | CIDR | 4.1905499e-05 |
| 2,987 | NL2SQL is a solved problem... Not! | 2024 | CIDR | 7.77529e-05 |
| 2,025 | From Natural Language Processing to Neural Databases | 2021 | VLDB | 9.7477788e-05 |
| 1,106 | Provenance for Aggregate Queries | 2011 | PODS | 0.00013976386 |
| 2,182 | Querying Data Provenance | 2010 | SIGMOD | 9.3596252e-05 |
| 653 | On the Provenance of Non-Answers to Queries over Extracted Data | 2008 | VLDB | 0.00018616975 |
| 6,974 | NLProveNAns: Natural Language Provenance for Non-Answers | 2018 | VLDB | 4.8725777e-05 |
| 9,622 | NLProv: Natural Language Provenance | 2016 | VLDB | 4.3121745e-05 |