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Maverick: A System for Discovering Exceptional Facts from Knowledge Graphs

Summary: Maverick employs a beam-search framework to discover exceptional facts about entities in knowledge graphs. Facts are context-subspace pairs (attributes, graph-context) with an end-to-end portal and a shared cache for cross-entity reuse, plus natural-language explanations and charts. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11866
Venue
VLDB
Year
2018
Pagerank
5.2929162e-05
Overall Rank
9,282 | 36.32%
DOI
10.14778/3229863.3236228

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhang_vldb18,
        title = {{Maverick: A System for Discovering Exceptional Facts from Knowledge Graphs}},
        author = {Zhang, Gensheng and Li, Chengkai},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {12},
        pages = {1934--1937},
        doi = {10.14778/3229863.3236228},
        url = {https://doi.org/10.14778/3229863.3236228},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
7,628 User Guidance for Efficient Fact Checking 2019 VLDB 5.5798467e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
4,033 Computational Journalism: A Call to Arms to Database Researchers 2011 CIDR 6.9449851e-05
6,919 Data In, Fact Out: Automated Monitoring of Facts by FactWatcher 2014 VLDB 5.7391146e-05
7,393 Maverick: Discovering Exceptional Facts from Knowledge Graphs 2018 SIGMOD 5.6261241e-05
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