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  4. 3D Learning and Reasoning in Link Prediction Over Knowledge Graphs
 
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2020
Journal Article
Title

3D Learning and Reasoning in Link Prediction Over Knowledge Graphs

Abstract
Knowledge Graph Embeddings (KGE) are used for representation learning in Knowledge Graphs (KGs) by measuring the likelihood of a relation between nodes. Rotation-based approaches, specially axis-angle 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 axis-angle rotation-based 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 axis-angle rotation-based approaches. In this article, we propose RodE, a new KGE model which employs an axis-angle representation for rotations based on Rodrigues' formula. RodE inherits the main advantages of 3-dimensional 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 state-of-the-art models on standard datasets.
Author(s)
Nayyeri, M.
Alam, M.M.
Lehmann, Jens  
Vahdati, Sahar
Journal
IEEE access  
Open Access
DOI
10.1109/ACCESS.2020.3034183
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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