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Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques

Summary: Experimental study comparing parallel training methods for KG embeddings, re-implemented in a common framework for fair assessment. Reveals non-comparable evaluations; proposes stratification tweaks; shows random partitioning with sampling can suffice. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h42bb4275b293d0af
Venue
VLDB
Year
2022
Pagerank
6.1324806e-05
Overall Rank
5,432 | 63.48%
DOI
10.14778/3494124.3494144

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kochsiek_vldb22,
        title = {{Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques}},
        author = {Kochsiek, Adrian and Gemulla, Rainer},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {3},
        pages = {633--645},
        doi = {10.14778/3494124.3494144},
        url = {https://doi.org/10.14778/3494124.3494144},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank Cited Paper Year Venue Pagerank
2,795 Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study 2020 SIGMOD 8.0014963e-05
6,640 Dynamic Parameter Allocation in Parameter Servers 2020 VLDB 5.7251952e-05
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