Abstract
In the cognitive intelligence of machines, more and more researchers use knowledge graphs that represent the structure of entities. The basic idea of knowledge representation learning is to project knowledge (entities and relations) into a vector space. The entities and relationships embedded in the 2-D space use convolutional neural networks to obtain the mutual information between them. However, the feature extraction capabilities of neural networks are limited by the input feature information. Therefore, our model named knowledge graph embedding based on quaternion transformation and convolutional neural network (QCKGE), aims to solve the limitation of input features by riching entities and relations mutual features. Firstly, the entity and relation are expressed as a two-dimensional real number space. Then, these features are mapped to the quaternion space to increase the ability to express entities and relations. Finally, the convolutional neural networks to mine richer interactive information in entities and relationships for link prediction. Besides, this paper also uses multi-layer convolutional neural network to mine deeper information hidden in the features. On the datasets of WN18, FB15k, WN18RR, and FB15k-237, compared with the existing model, our design can show a better result.
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Gao, Y., Tian, X., Zhou, J., Zheng, B., Li, H., Zhu, Z. (2022). Knowledge Graph Embedding Based on Quaternion Transformation and Convolutional Neural Network. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_10
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