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Love-at-First-Sight: First Answers Without the Awkward Silence in Big Knowledge Graphs

Summary: Introduces first-sight summaries (FSS) for KG/SPARQL exploration: log-driven summaries that return initial answers fast under budget, instead of waiting on costly coarse queries. Provides exact/approximate construction algorithms with guarantees, yielding up to 100x latency cuts over endpoints. (summarized by gpt-5.4-mini on May 27 2026)

Paper ID
14299
Venue
VLDB
Year
2026
Pagerank
5.1725247e-05
Overall Rank
10,261 | 28.69%
DOI
10.14778/3801059.3801068

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Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
555 An Analytical Study of Large SPARQL Query Logs 2018 VLDB 0.00016655415
3,305 A Formal Perspective on the View Selection Problem 2001 VLDB 7.6052684e-05
5,633 Graph-Aware, Workload-Adaptive SPARQL Query Caching 2015 SIGMOD 6.2047208e-05
6,217 X2Q: Your Personal Example-based Graph Explorer 2018 VLDB 6.01306e-05
8,394 Knowledge Graph Exploration Systems: are we lost? 2022 CIDR 5.4958075e-05
8,400 View Selection over Knowledge Graphs in Triple Stores 2021 VLDB 5.4958075e-05
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