Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. 3D Learning and Reasoning in Link Prediction Over Knowledge Graphs
 IEEE access 8 (2020), S.196459196471 ISSN: 21693536 

 Englisch 
 Zeitschriftenaufsatz, Elektronische Publikation 
 Fraunhofer IAIS () 
Abstract
Knowledge Graph Embeddings (KGE) are used for representation learning in Knowledge Graphs (KGs) by measuring the likelihood of a relation between nodes. Rotationbased approaches, specially axisangle representations, were shown to improve the performance of many Machine Learning (ML)based models in different tasks including link prediction. There is a perceived disconnect between the topics of KGE models and axisangle rotationbased approaches. This is particularly visible when considering the ability of KGEs to learn relational patterns such as symmetry, inversion, implication, equivalence, composition, and reflexivity considering axisangle rotationbased approaches. In this article, we propose RodE, a new KGE model which employs an axisangle representation for rotations based on Rodrigues' formula. RodE inherits the main advantages of 3dimensional rotation from angle, orientation and distance preservation in the embedding space. Thus, the model efficiently captures the similarity between the nodes in a graph in the vector space. Our experiments show that RodE outperforms stateoftheart models on standard datasets.