arXiv AI
Jun 2
On the Theoretical Limitations of Embedding-based Link Prediction
arXiv:2506. 22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces.
Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
The paper introduces LitEm, a neural regression model that allows transductive knowledge graph embedding models to predict numerical attributes. LitEm achieves top or near‑top performance on most attributes across datasets such as FB15K‑237, YAGO15K, DB15K, and Mutagenesis. A co‑training framework further improves link prediction for bilinear models while enabling them to predict numerical attributes, demonstrating literal‑aware encoding of attribute information.
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arXiv AI
Jun 16
Model Graph Inductive Learning for Knowledge Graph Completion
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 Machine Learning
Jun 3
Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings
arXiv:2606. 03365v1 Announce Type: new Abstract: Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs.
arXiv Machine Learning
2d ago
Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs
The paper introduces a method that transforms knowledge graph facts into a fixed vocabulary representation, where each fact becomes a node linked to its subject, object, and relation type via six meta-relations. Using this representation, standard GNNs (e.g., GAT, GINE, GraphSAGE, R-GCN) trained on a single small graph can achieve zero‑shot link prediction on 40 inductive benchmarks, matching the performance of specialized foundation models like ULTRA. The approach also generalizes to relational databases, enabling foreign‑key prediction without cell values or schema text, and the authors provide code, checkpoints, and evaluation tools for all benchmarks.
arXiv AI
Sep 4
Pattern Over-Generalization of Knowledge Graph Embedding
arXiv:2609. 03487v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graphs (KGs) by projecting entities and relations into a low-dimensional vector space.
arXiv AI
Jun 3
ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models
arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.
arXiv Machine Learning
Jul 22
One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models
arXiv:2607. 19128v1 Announce Type: new Abstract: Vision-language models (VLMs) provide a unified representation space for textual and visual information, yet their potential as general-purpose backbones for graph-structured data remains largely unexplored.
arXiv AI
Aug 10
Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction
arXiv:2411. 00028v3 Announce Type: replace-cross Abstract: Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and supporting decision-making.
arXiv Machine Learning
Aug 11
Neural Message Passing on Structural Interaction Graphs for Fully-Inductive Graph Neural Networks
arXiv:2608. 08567v1 Announce Type: new Abstract: A central obstacle in building graph foundation models is the input heterogeneity in terms of feature space dimensionality, semantics, and structure.
arXiv AI
Aug 25
Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective
The article surveys neural-symbolic reasoning over knowledge graphs from a query perspective, highlighting the limitations of traditional symbolic methods when dealing with incomplete or noisy data. It discusses how the fusion of deep learning and symbolic reasoning—termed Neural Symbolic AI—offers interpretable and versatile solutions, and examines the role of large language models in advancing knowledge graph inference. The survey provides a comprehensive review of query types, classification of neural-symbolic approaches, and future directions for integrating LLMs with knowledge graph reasoning.
arXiv AI
Jun 17
Handling Feature Heterogeneity with Learnable Graph Patches
arXiv:2606. 17667v1 Announce Type: cross Abstract: In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model or Graph Foundation Model (GFM).
Hugging Face Trending Papers
Aug 11
FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs
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.