RankIE: Document Retrieval on Ranked Entity Graphs

Summary: RankIE retrieves documents by interpreting queries as ranked entity graphs and matching them to ontology-based indexes for entity-aware search. It enables top-k entity refinement with a faceted UI, achieving 83% precision for top-5 entities, vs 61% SOTA. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9930
Venue: VLDB
Year: 2009
Pagerank: 6.2824978e-05
Overall Rank: 4,296 | 70.15%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
12,267	ROXXI: Reviving witness dOcuments to eXplore eXtracted Information	2010	VLDB	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
2,156	Efficiently Linking Text Documents with Relevant Structured Information	2006	VLDB	9.416112e-05

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