KG Embedding

KG: Knowledge Graph

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4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

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Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions
May 11, 2026Sayan Kumar ChakiGraph Representation Learning KG Embedding
Inria, Laboratoire Hubert Curien, Université Jean Monnet
Graph Neural Network based Hierarchy-Aware Embeddings of Knowledge Graphs: Applications to Yeast Phenotype Prediction
May 5, 2026Filip Kronström, Alexander H. Gower, Daniel Brunnsåker +2Graph Neural Networks Representation Learning
Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Sweden · Department of Life Sciences, Chalmers University of Technology, Sweden · Department of Industrial Biotechnology, KTH Royal Institute of Technology, Sweden +1
RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
Apr 28, 2026Guanglin Niu, Bo LiKG Embedding Multimodal KGs
School of Artificial Intelligence, Beihang University
BoxLitE: A Faithful Knowledge Base Embedding Based on Convex Optimization
Apr 27, 2026Bruno F. Lourenço, Hesham Morgan, Ana Ozaki +2Logical Reasoning KG Embedding
The Institute of Statistical Mathematics, Japan · TU Wien, Austria · University of Oslo, Norway +1
FixV2W: Correcting Invalid CVE-CWE Mappings with Knowledge Graph Embeddings
Apr 24, 2026Sevval Simsek, Varsha Athreya, David StarobinskiKG Embedding Cybersecurity
Boston University, Boston, USA
Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding
Apr 21, 2026Gerard Pons, Carlos Escolano, Besim Bilalli +1Graph Representation Learning KG Embedding
Data-intensive Technologies and Knowledge Systems (DTAK), Department of Service and Information System Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain · Intelligent Data Science and Artificial Intelligence Research Center (IDEAI), Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, 08034, Catalonia, Spain
OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation
Apr 7, 2026Dongying Lin, Yinan Liu, Shengwei tang +2KG Embedding Temporal Link Prediction
Northeastern University, Shenyang, China

May 11, 2026, cs.LGJ/K move · Enter open · S save

Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions

Sayan Kumar Chaki

Inria, Laboratoire Hubert Curien, Université Jean Monnet

Graph Representation Learning KG Embedding KG Completion

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Knowledge graph embedding (KGE) models typically represent each relation as an operator on entity embeddings. In this work, we identify three structural axioms that any principled relation operator must satisfy, linearity, trace preservation, and complete positivity, and show that they characterize a Kraus channel structure via the Kraus representation theorem. The completeness constraint defining this family is equivalent to these axioms, providing a principled foundation rather than an externally imposed condition. Under this formulation, most existing operator-based KGE models are recoverable as special cases with Kraus rank

κ= 1

under specific embedding choices. We further generalize this characterization to arbitrary metric geometries by introducing \mbox{w-Kraus} channels, which satisfy completeness by construction within their respective spaces. Building on this theory, we propose \textsc{KrausKGE}, a principled KGE model that naturally handles

1

-to-

N

and

N

-to-

N

relations, supports

k

-hop reasoning without requiring explicit path encoders, and eliminates the need for norm constraints on entity embeddings. Additionally, our framework yields the first theoretically grounded per-relation complexity measure in the KGE literature, with a provable lower bound in terms of the empirical relation matrix rank. Empirical evaluation demonstrates that \textsc{KrausKGE} consistently outperforms strong baselines on

N

-to-

N

relations, with performance gains that increase monotonically with relation fan-out, in alignment with theoretical predictions.

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