PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples of triples. However, since KGs generally contain only positive assertions, negative samples are artificially generated through negative sampling strategies, ranging from simple random corruption to more sophisticated approaches that exploit structural, semantic, or embedding information. The design and implementation of advanced negative samplers remains challenging, as most pop
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 46%BaryGraph - knowledge graph where every relationship is its own embedded document (not an edge) [R] →
- FuzzySimilar title/name (fuzzy) · 87%modular/modular →
“Fuzzy title match (0.94): “PyKEEN-NSX: A Modular Framework for Static, Dynamic and Sche” ≈ “modular/modular””
- LinkedLinked via arxiv author · 85%Ivan Diliso →
“PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN”
- LinkedLinked via arxiv author · 85%Nicola Fanizzi →
“PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN”
- LinkedLinked via arxiv author · 85%Claudia d'Amato →
“PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN”