Kelpie: an Explainability Framework for Embedding-based Link Prediction Models

Summary: Kelpie offers explainability for embedding-based link prediction models, addressing opacity in knowledge-graph completion. Model-agnostic, it provides necessary and sufficient explanations and demonstrates effectiveness across architectures on five major datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12833
Venue: VLDB
Year: 2022
Pagerank: 4.7484027e-05
Overall Rank: 7,352 | 48.91%
DOI: 10.14778/3554821.3554845

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,648	eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules	2025	VLDB	4.1905499e-05
10,994	Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability	2024	SIGMOD	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
6,404	Explaining Link Prediction Systems based on Knowledge Graph Embeddings	2022	SIGMOD	5.0714769e-05

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