arXiv:2606. 27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.
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:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.
arXiv:2606. 03307v1 Announce Type: cross Abstract: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference.
arXiv:2505. 12369v2 Announce Type: replace Abstract: Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space.
arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.