arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formulated as triplet prediction, with link prediction as the dominant paradigm.
arXiv:2606. 27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts.
arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.
arXiv:2607. 03154v1 Announce Type: cross Abstract: Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs.
arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.
arXiv:2606. 03365v1 Announce Type: new Abstract: Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs.