Demonstration of SPARQL^ML: An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs
Summary: KGNet embeds graph-ML operators for node classification and link prediction directly in RDF engines via SPARQL^ML user-defined predicates. Its optimizer selects near-optimal models, enabling GML inference over knowledge graphs without separate scripting or ML expertise. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Hussein Abdallah (Concordia University)
- 2. Waleed Afandi (Concordia University)
- 3. Essam Mansour (Concordia University)
BibTeX Citation
@article{abdallah_vldb23,
title = {{Demonstration of SPARQL\^{}ML: An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs}},
author = {Abdallah, Hussein and Afandi, Waleed and Mansour, Essam},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3974--3977},
doi = {10.14778/3611540.3611599},
url = {https://doi.org/10.14778/3611540.3611599},
year = {2023}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 106 | The MADlib Analytics Library or MAD Skills, the SQL | 2012 | VLDB | 0.00033539462 |
| 2,540 | Multi-Objective Parametric Query Optimization | 2015 | VLDB | 8.45187e-05 |
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