DBScholar

Back to papers

Effective Entity Augmentation By Querying External Data Sources

Summary: A progressive framework for augmenting dataset entities through keyword-only external sources, addressing sparse relevance and cross-source naming variation. Learns query strategies from end-user feedback, minimizing expert effort while rapidly delivering accurate entity-relevant information. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13362
Venue
VLDB
Year
2023
Pagerank
5.3462535e-05
Overall Rank
8,835 | 39.60%
DOI
10.14778/3611479.3611535

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{buss_vldb23,
        title = {{Effective Entity Augmentation By Querying External Data Sources}},
        author = {Buss, Christopher and Mousavi, Jasmin and Tokarev, Mikhail and Termehchy, Arash and Maier, David and Lee, Stefan},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {11},
        pages = {3404--3417},
        doi = {10.14778/3611479.3611535},
        url = {https://doi.org/10.14778/3611479.3611535},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
8,080 Falcon: Fair Active Learning using Multi-armed Bandits 2024 VLDB 5.4784205e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers