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.
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.
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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 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 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 AI
3d ago
ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction
The paper introduces ImbalancE, an inference‑time latent search method that mitigates degree imbalance bias in Knowledge Graph Embedding models. It targets the problematic prediction of target entities with much lower degrees than anchor entities, a common issue in recommender systems and other applications. Experiments on benchmark datasets show that ImbalancE improves predictions on the most imbalanced triples compared to conventional methods.
arXiv AI
Jul 7
Conditional Diffusion Guided Knowledge Transfer for Multi-Domain Knowledge Graph Completion
arXiv:2607. 03154v1 Announce Type: cross Abstract: Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs.
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.
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
Jun 5
Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion
arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.
arXiv Machine Learning
Sep 10
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 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 AI
Sep 1
LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information
The paper introduces CoSC, a method for knowledge graph completion that merges discrete structural coding with information from similar entities. An LLM first produces a candidate ranking based on structural codes, then refines this ranking using data from entities with comparable structures. Experiments on FB15k-237 demonstrate that CoSC achieves higher MRR and Hits@10 than existing baselines while staying competitive on Hits@1.