Knowledge representation combining quaternion path integration and depth-wise atrous circular convolution

Knowledge models endeavor to improve representation and feature extraction capabilities while keeping low computational cost. Firstly, existing embedding models in hypercomplex spaces of non-Abelian group are optimized. Then a method for fast quaternion multiplication is proposed with proof, with which path semantics are computed and further integrated with the attention mechanism based on the idea semantic extraction of relation sequences could be regarded as a multiple rotational blending problem. A depth-wise atrous circular convolution framework is set up for better feature extraction. Experiments including Link Prediction and Path Query are conducted on benchmark datasets verifying our model holds advantages over state-of-the-art models like Rotate3D. Moreover, the model is tested on a biomedical dataset simulating real-world applications. An ablation study is also performed to explore the effectiveness of different components.