Robust Link Prediction over Noisy Hyper-Relational Knowledge Graphs via Active Learning
Modern Knowledge Graphs (KGs) are inevitably noisy due to the nature of their construction
process. Existing robust learning techniques for noisy KGs mostly focus on triple facts, where …
process. Existing robust learning techniques for noisy KGs mostly focus on triple facts, where …
HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology
Abstract Knowledge Graph (KG) embeddings are essential for link prediction over KGs.
Compared to triplets, hyper-relational facts consisting of a base triplet and an arbitrary …
Compared to triplets, hyper-relational facts consisting of a base triplet and an arbitrary …
HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution Detection
With the progressive advancements in deep graph learning, out-of-distribution (OOD)
detection for graph data has emerged as a critical challenge. While the efficacy of auxiliary …
detection for graph data has emerged as a critical challenge. While the efficacy of auxiliary …