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Knowledge Graph Exploration Systems: are we lost?

Summary: Position paper surveying KG exploration approaches and arguing that mainstream KG data-management neglects exploration needs arising from large, heterogeneous, provenance-rich KGs. Identifies unmet requirements (interaction, summarization, provenance-aware views, explainability, scalable query/analytics) and proposes research directions toward systems that natively support exploration. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
452
Venue
CIDR
Year
2022
Pagerank
4.5435639e-05
Overall Rank
8,289 | 42.34%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,261 Love-at-First-Sight: First Answers Without the Awkward Silence in Big Knowledge Graphs 2026 VLDB 4.1945683e-05
10,564 PlanRGCN: Predicting SPARQL Query Performance 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

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

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