arXiv AI
Jun 30
Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.
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.
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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.
Hugging Face Trending Papers
Jun 29
Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
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 AI
Jun 29
RelBall: Relation Ball with Quaternion Rotation for Knowledge Graph Completion
arXiv:2606. 27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts.
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
3d ago
Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods
arXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are...
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
Aug 28
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.
arXiv AI
Sep 1
PyKEEN-NSX: A Modular Framework for Static, Dynamic and Schema-Aware Negative Sampling in PyKEEN
arXiv:2608.30652v1 Announce Type: new Abstract: Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embeddi...
arXiv AI
Jun 30
Measuring Graph-to-Graph Semantic Similarity in Knowledge Graphs: An Empirical Evaluation of Knowledge Graph Embeddings
arXiv:2606. 29180v1 Announce Type: new Abstract: A Knowledge Graph (KG) represents facts as structured triples and is widely used to organize relational knowledge across diverse domains.
arXiv Machine Learning
Jun 17
Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models
arXiv:2606. 18001v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining.
arXiv AI
Sep 3
PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion
PEARL is a new framework for inductive knowledge graph completion that treats relational paths as context-conditioned reasoning signals. It builds a query‑specific contextual subgraph from the query entities’ neighborhoods and uses a large language model‑guided retriever to select semantically relevant paths. By constructing a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, and applying a dual‑view contrastive objective, PEARL adapts path embeddings to local and global structural evidence, achieving the best average Hits@10 on WN18RR, FB15k‑237, and NELL‑995.