arXiv Artificial Intelligence

Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

1 hour ago 30.09.2026 07:00 👁 1 views
Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

Quick summary

arXiv:2605.27023v4 Announce Type: replace Abstract: Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very incomplete. To perform zero-shot knowledge graph completion in unseen KGs, which have different relational vocabularies from those used for pre-training, KG foundation models (KGFMs) receive a wide range of attention. Existing KGFMs often perform training using random negative triples, which are constructed by replacing the head or tail entity of a positive triple w

Key takeaways

  • arXiv:2605.27023v4 Announce Type: replace Abstract: Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems.
  • However, despite all this, KGs are often very incomplete.
  • To perform zero-shot knowledge graph completion in unseen KGs, which have different relational vocabularies from those used for pre-training, KG foundation models (KGFMs) receive a wide range of attention.

Why it matters

“Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗