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Explaining Link Prediction Systems based on Knowledge Graph Embeddings

Summary: Introduces Kelpie, a model-agnostic explainability framework for embedding-based link prediction in knowledge graphs. It extracts necessary and sufficient explanations by tracing training facts, enabling interpretable predictions across LP models and outperforming baselines in experiments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6401
Venue
SIGMOD
Year
2022
Pagerank
5.9231159e-05
Overall Rank
6,295 | 56.82%
DOI
10.1145/3514221.3517887

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{rossi_sigmod22,
        title = {{Explaining Link Prediction Systems based on Knowledge Graph Embeddings}},
        author = {Rossi, Andrea and Firmani, Donatella and Merialdo, Paolo and Teofili, Tommaso},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517887},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517887},
        year = {2022}
}

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