DBScholar

Back to papers

Inferray: fast in-memory RDF inference

Summary: Inferray delivers fast in-memory RDFS, ρ_df, and RDFS-Plus inference via vertically partitioned RDF storage, sequential sort-merge joins, optimized 64-bit integer sorting, and dedicated closure storage. It substantially outperforms prior systems, especially for transitive closure. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11525
Venue
VLDB
Year
2016
Pagerank
5.4211759e-05
Overall Rank
8,462 | 41.95%
DOI
10.14778/2904121.2904123

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{subercaze_vldb16,
        title = {{Inferray: fast in-memory RDF inference}},
        author = {Subercaze, Julien and Gravier, Christophe and Chevalier, Jules and Laforest, Frederique},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {6},
        doi = {10.14778/2904121.2904123},
        url = {https://doi.org/10.14778/2904121.2904123},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
9,850 Materializing Knowledge Bases via Trigger Graphs 2021 VLDB 5.2094004e-05
11,944 Stylus: A Strongly-Typed Store for Serving Massive RDF Data 2018 VLDB 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 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