Apr 23, 2019 · Quaternion embeddings, hypercomplex-valued embeddings with three imaginary components, are utilized to represent entities. Relations are modelled as rotations.

In this paper, we design a new knowledge graph embedding model which operates on the quaternion space with well-defined mathematical and physical meaning.

More specifically, quaternion embeddings, hypercomplex-valued embeddings with three imaginary components, are utilized to represent entities. Relations are ...

Hyper-parameters for reproducing the reported results are provided in the train_QuatE_dataset.py. How to run. Requirements: Pytorch 1.4+. STEP:.

Since you can multiply a quaternion by a quaternion, the quaternions act on themselves, so they indeed act on a 4D space. They can also act on a couple 3D ...

Oct 12, 2022 · Learning knowledge graph embeddings in the complex space C or quaternion space H has been proven to be a highly effective inductive bias,.

Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links.

We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance ...

This work moves beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and ...

To embrace a richer set of relational information, we propose a new method called dual quaternion knowledge graph embeddings (DualE), which introduces dual.