86 citations · 157 across the 11 of their papers we have counts for
4 papers · 1 filter
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
Peru Bhardwaj, John Kelleher, Luca Costabello +1
We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models,…
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
Peru Bhardwaj, John Kelleher, Luca Costabello +1
Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poison…
Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
Sumit Pai, Luca Costabello
Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, r…
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties
Lisa Schut, Oscar Key, Rory McGrath +4
Counterfactual explanations (CEs) are a practical tool for demonstrating why machine learning classifiers make particular decisions. For CEs to be useful, it is important that they…