arXiv:2607. 02872v1 Announce Type: new Abstract: Dynamic knowledge graphs are ubiquitous in today's AI applications, as we represent molecular structures, social relationships, and language information using these graph models.
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:2610.19576v1 Announce Type: new Abstract: Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remain...
The paper introduces a time‑aligned evolving concept graph framework that jointly models semantic and structural changes in scientific literature. By treating dated papers as shared update events, it reconstructs both semantic and structural states from the same publication history for each prediction time, and fuses these states at the pair level to forecast co‑occurrence, relation formation, and conditional relation type. Experiments on a large graph of 187,848 papers and 270,687 concepts show that refreshing context with graph updates boosts mean relation AUPRC by 16.6% and raises mean relation AUROC from 0.9290 to 0.9722.
Forecasting scientific relations can guide discovery by identifying promising connections before they emerge. Existing approaches often model concept semantics and graph structure separately or summar...
arXiv:2610.19364v1 Announce Type: new Abstract: Dynamic graph learning models how entities and their relations evolve from historical observations to predict future graph states. Existing methods do...
arXiv:2608. 10668v1 Announce Type: new Abstract: Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary.
Temporal knowledge graphs are central to many uses of the Semantic Web, but existing completion methods assume the entities, relation names, and timestamps to be reasoned about are already known at training time, restricting each model to a single graph and vocabulary. We propose FITTER, the first fully-inductive structural model for temporal knowledge graph link prediction that supports cross-domain transfer: the inference graph may contain entirely unseen entities, relation names, and timestamps drawn from a different domain.
arXiv:2606. 27651v1 Announce Type: new Abstract: In recent years, with the emergence of Temporal Knowledge Graphs (TKGs), research on learning entity and relation representations in TKGs has attracted increasing attention, giving rise to a large number of TKG embedding methods.
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:2609.14279v1 Announce Type: cross Abstract: Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time. Many existing models use a single geo...
arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.
arXiv:2605. 07121v2 Announce Type: replace Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events.