TL;DR
This work presents an approach that uses semantically meaningful, bimodal random walks on real-world heterogeneous networks to extract correlations between nodes and bring together nodes with shared or similar attributes.
Representation learning on hyper-relational numeric knowledge graphs (HNKGs), which incorporate numeric entities and facts consisting of a primary triple augmented by attribute-value qualifiers, is crucial for advanced reasoning. However, most existing methods employ discretization or binning strategies, struggling to...
This paper introduces LaGR, a novel approach for integrating global information in knowledge graph reasoning that compresses the graph into a fixed, compact set of latent summaries and applies exact self-attention within this latent space, resulting in scale-invariant attention and stable performance across diverse dat...
A novel self-supervised framework for relational reasoning over RDBs that treats context sparsity as a controllable curriculum variable and leverages it to induce a progressive shift from semantic-dominant inference to structure-aware relational reasoning.
Inductive link prediction is the problem of inferring about new entities and relations present in dynamic knowledge graphs. Traditional approaches typically depend on Personalized PageRank to select subgraphs but do not consider the directional nature of graphs and cannot take care of asymmetrical links. Also, traditio...
Multimodal graph predictors combine text, images, and relations to classify connected entities. How much of this input is needed to preserve their predictions? We study budgeted representation selection, which chooses a subset of candidate text and image vectors under a separate capacity for each modality. Predictions...
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