TL;DR
This work proposes the joint entity and relation embedding network (JERENet) for multi-relational knowledge graph completion, which can effectively capture complex relations by leveraging multi-relational features among entities and neighborhood structure information.
Hyper-relational knowledge graphs (HKGs) extend knowledge graphs with qualifiers to represent complex n-ary facts. However, existing methods often (1) rely on relation-agnostic node aggregation that mixes heterogeneous multi-hop evidence, which can lead to entity-role ambiguity and representation collapse, and (2) fail...
A neural regression model (LitEm) that enables transductive knowledge graph embedding models to predict numerical attributes within knowledge graphs and a co-training framework that jointly trains state-of-the-art transductive knowledge graph embedding models with LitEm, which improves link prediction performance mainl...
These results demonstrate that collaborative modeling of instance-level and batch-level structural context can effectively enhance structure-aware entity representation and improve fine-grained entity prediction.
PEARL is proposed, a Path-Entity Aligned Relational Learning framework that models paths as context-conditioned reasoning signals and employs a dual-view contrastive objective that promotes representation consistency under stochastic contextual perturbations.
P PogRE (Pattern Over-Generalization Robust Embedding), a simple but effective method that utilizes dense linear transformations and compound operations for relation representation that effectively addresses the negative impact of over-generalization.
Inductive link prediction in knowledge graphs refers to the task of inferring known relations between entities unseen during training. Most existing approach-es are limited to predicting only known relations and struggle to generalize to un-seen relations, which restricts their utility in dynamic settings. To address t...
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