arXiv Machine Learning By Rupesh Sapkota, Louis Mozart Kamdem Teyou, Moshood Yekini, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs

Read the original on arXiv Machine Learning →

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.

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arXiv AI
Sep 30

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.

By Alberto Bernardi, Luca Costabello, Christophe Gueret
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.

By Camille Pradel