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 …
HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation
In a hyper-relational knowledge graph (HKG), each fact is composed of a main triple
associated with attribute-value qualifiers, which express additional factual knowledge. The …
associated with attribute-value qualifiers, which express additional factual knowledge. The …