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Found 16 papers, 3 papers with code
Knowledge Graph Representation Learning using Ordinary Differential Equations
Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models.
Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion
In this work, we strive to move beyond the complex or hypercomplex space for KGE and propose a novel geometric algebra based embedding approach, GeomE, which uses multivector representations and the geometric product to model entities and relations.
Box Embeddings for the Description Logic EL++
Recently, various methods for representation learning on Knowledge Bases (KBs) have been developed.
Trans4E: Link Prediction on Scholarly Knowledge Graphs
This is typical for KGs that categorize a large number of entities (e. g., research articles, patents, persons) according to a relatively small set of categories.
Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings
Representation learning approaches for knowledge graphs have been mostly designed for static data.
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
For example, instead of training a model one time with a large embedding size of 1200, we repeat the training of the model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing while the overall numbers of adjustable parameters are same (6*200=1200) and the total memory footprint remains the same.
Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations
To this end, we represent each relation (edge) in a KG as a vector field on a smooth Riemannian manifold.
Knowledge Graph Embeddings in Geometric Algebras
Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space.
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time.
5* Knowledge Graph Embeddings with Projective Transformations
Most of the embedding models designed in Euclidean geometry usually support a single transformation type - often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs.
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.
Toward Understanding The Effect of Loss Function on The Performance of Knowledge Graph Embedding
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules
We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph.
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Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
To tackle this problem, several loss functions have been proposed recently by adding upper bounds and lower bounds to the scores of positive and negative samples.
Soft Marginal TransE for Scholarly Knowledge Graph Completion
In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata.
