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.

Keywords

• Link prediction
• knowledge graph embedding
• 3D rotation
• learning and reasoning Rodrigues formula