Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Gemulla, Rainer"'
Autor:
Kochsiek, Adrian, Gemulla, Rainer
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 can be integrated by retraining the model from scratch in principle,
Externí odkaz:
http://arxiv.org/abs/2310.11917
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 features of the KG, has small model size, and is scalable. To reach good
Externí odkaz:
http://arxiv.org/abs/2305.13059
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 sensitive to hyperparameter settings, however, and that suitable choices are
Externí odkaz:
http://arxiv.org/abs/2207.04979
Autor:
Renz-Wieland, Alexander, Kieslinger, Andreas, Gericke, Robert, Gemulla, Rainer, Kaoudi, Zoi, Markl, Volker
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 management can be highly inefficient. Advanced parameter management approache
Externí odkaz:
http://arxiv.org/abs/2206.00470
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 to KG link prediction and question answering over incomplete KGs (KGQ
Externí odkaz:
http://arxiv.org/abs/2203.10321
Parameter servers (PSs) facilitate the implementation of distributed training for large machine learning tasks. In this paper, we argue that existing PSs are inefficient for tasks that exhibit non-uniform parameter access; their performance may even
Externí odkaz:
http://arxiv.org/abs/2104.00501
Publikováno v:
Workshop on machine learning for engineering modeling, simulation and design @ NeurIPS 2020
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optionally depend on some input; making them perfectly suitable as a learna
Externí odkaz:
http://arxiv.org/abs/2010.07078
Publikováno v:
PVLDB, 13(11): 1877-1890, 2020
To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed
Externí odkaz:
http://arxiv.org/abs/2002.00655
Publikováno v:
In Proceedings of the Conference of Automatic Knowledge Base Construction (AKBC) 2019
Open information extraction (OIE) systems extract relations and their arguments from natural language text in an unsupervised manner. The resulting extractions are a valuable resource for downstream tasks such as knowledge base construction, open que
Externí odkaz:
http://arxiv.org/abs/1904.12324
We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patt
Externí odkaz:
http://arxiv.org/abs/1902.00898