44 citations · 113 across the 13 of their papers we have counts for
15 papers
RelICL: Training-free Relational Learning with Tabular Foundation Models
Simon Forbat, Rainer Gemulla
Tabular foundation models achieve state-of-the-art performance on single-table tasks without any training. Recent work suggests that they are also well-suited for relational learni…
A Benchmark for Semi-Inductive Link Prediction in Knowledge Graphs
Adrian Kochsiek, Rainer Gemulla
Semi-inductive link prediction (LP) in knowledge graphs (KG) is the task of predicting facts for new, previously unseen entities based on context information. Although new entities…
Friendly Neighbors: Contextualized Sequence-to-Sequence Link Prediction
Adrian Kochsiek, Apoorv Saxena, Inderjeet Nair +1
We propose KGT5-context, a simple sequence-to-sequence model for link prediction (LP) in knowledge graphs (KG). Our work expands on KGT5, a recent LP model that exploits textual fe…
Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
Adrian Kochsiek, Fritz Niesel, Rainer Gemulla
Knowledge graph embedding (KGE) models are an effective and popular approach to represent and reason with multi-relational data. Prior studies have shown that KGE models are sensit…
Good Intentions: Adaptive Parameter Management via Intent Signaling
Alexander Renz-Wieland, Andreas Kieslinger, Robert Gericke +3
Parameter management is essential for distributed training of large machine learning (ML) tasks. Some ML tasks are hard to distribute because common approaches to parameter managem…
Sequence-to-Sequence Knowledge Graph Completion and Question Answering
Apoorv Saxena, Adrian Kochsiek, Rainer Gemulla
Knowledge graph embedding (KGE) models represent each entity and relation of a knowledge graph (KG) with low-dimensional embedding vectors. These methods have recently been applied…