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.
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.
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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
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 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
Aug 7
ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion
arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.
arXiv AI
Jun 3
Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation
arXiv:2606. 03307v1 Announce Type: cross Abstract: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference.
arXiv AI
Jun 18
Fully Geometric Multi-Hop Reasoning on Knowledge Graphs with Transitive Relations
arXiv:2505. 12369v2 Announce Type: replace Abstract: Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space.
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
Aug 26
Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge
arXiv:2601.10485v5 Announce Type: replace Abstract: Domain-specific knowledge graphs (DKGs) are critical yet often suffer from limited coverage compared to General Knowledge Graphs (GKGs). Existing t...
arXiv AI
Aug 26
FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs
FedV-KGQA is a framework for multi-hop question answering over knowledge graphs that are vertically partitioned across different organizations. It allows entities to be shared while each silo retains disjoint sets of relations, using local graph enrichment and knowledge graph embeddings so that raw triples and relation parameters never leave the silo. The system includes a topic entity anchoring mechanism to ground questions in the correct graph neighborhood without runtime inter-silo communication, and it achieves performance close to centralized systems on three benchmarks, including 3-hop reasoning and robustness to embedding perturbations.
arXiv AI
Sep 15
FedV-KGQA in Practice: Design Lessons and an Interactive Prototype
FedV-KGQA addresses multi‑hop question answering over vertically partitioned knowledge graphs where each silo holds disjoint relation types. The system trains local embeddings, concatenates silo‑specific entity views, anchors questions at a topic entity, and ranks candidates without sharing raw triples. Experiments show federated fusion nearly matches centralized accuracy, that anchoring and enrichment are more critical than embedding choice, and that the cheapest encoder depends on target accuracy.
arXiv Machine Learning
Sep 11
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.