arXiv Machine Learning June 9, 2026 By Sooho Moon, Jian Kang, Yunyong Ko

Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses

Read the original on arXiv Machine Learning →

arXiv:2606. 08921v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to predict missing facts from an observed knowledge graph (KG), playing a crucial role in a wide range of real-world applications such as drug discovery, recommender systems, and retrieval-augmented generation (RAG).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 10

When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking

arXiv:2606. 10287v1 Announce Type: new Abstract: Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR, Hits$@$k, and Mean Rank, which often produce conflicting model orderings across datasets.

By Haji Gul, Ajaz Ahmad Bhat

benchmarks

arXiv Machine Learning

Jun 9

PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models

arXiv:2606. 08926v1 Announce Type: new Abstract: Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring different evaluation perspectives.

By Sooho Moon, Yunyong Ko

safety

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.

By Guillaume M\'erou\'e, Fabien Gandon, Pierre Monnin

rag benchmarks

arXiv Machine Learning

Jun 8

Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies

arXiv:2606. 07492v1 Announce Type: cross Abstract: The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.

By Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov, Ilya Ivanov, Daria Korovaitceva, Margarita Rusanova, Uliana Parkina, Alexander Derevyagin, Evgeny Frolov, Sergey Samsonov, Anton Lysenko

efficiency benchmarks

arXiv Machine Learning

Jun 29

Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings

arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.

By Rostislav Gusev, Alexey Zaytsev

nlp benchmarks

arXiv AI

Jun 16

Unifying Post-hoc Explanations of Knowledge Graph Completions

arXiv:2507. 22951v2 Announce Type: replace Abstract: Knowledge Graphs organize information as entity-relation-entity triples, enabling machine learning models to predict plausible missing triples in a task known as Knowledge Graph Completion (KGC).

By Alessandro Lonardi, Samy Badreddine, Tarek R. Besold, Pablo Sanchez Martin

safety

Related stories

When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking

PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models

Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings

Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies

Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings

Unifying Post-hoc Explanations of Knowledge Graph Completions