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Ranking Indicator Discovery from Very Large Knowledge Graphs

Summary: Mine counting-graph SPARQL patterns on large KGs to discover ranking indicators that induce strict total orders. RIPM prunes search and ranks candidates by coverage & Gini to extract interpretable indicators validated by baselines and a user study. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
hd05b5b1b7690d638
Venue
VLDB
Year
2025
Pagerank
5.1708619e-05
Overall Rank
9,480 | 36.27%
DOI
10.14778/3717755.3717775

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{abdallah_vldb25,
        title = {{Ranking Indicator Discovery from Very Large Knowledge Graphs}},
        author = {Abdallah, Hassan and Markhoff, Béatrice and Soulet, Arnaud},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {4},
        pages = {1183--1195},
        doi = {10.14778/3717755.3717775},
        url = {https://doi.org/10.14778/3717755.3717775},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,815 CRAFT: Corpus Relatedness Analysis Using Fourier Transforms 2026 VLDB 4.9793485e-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
4,673 Knowledge Graphs 2021: A Data Odyssey 2021 VLDB 6.4755776e-05
5,353 Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals 2023 VLDB 6.1637765e-05
6,574 Explaining Monotonic Ranking Functions 2021 VLDB 5.7454021e-05
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