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)
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Authors
- 1. Hassan Abdallah (University of Tours)
- 2. Béatrice Markhoff (University of Tours)
- 3. Arnaud Soulet (University of Tours)
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}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,687 | Knowledge Graphs 2021: A Data Odyssey | 2021 | VLDB | 6.5620198e-05 |
| 5,278 | Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals | 2023 | VLDB | 6.2856584e-05 |
| 6,507 | Explaining Monotonic Ranking Functions | 2021 | VLDB | 5.8576814e-05 |
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