Novel translation knowledge graph completion model based on 2D convolution

The knowledge graph completion task involves predicting missing entities and relations in a knowledge graph. Many models have achieved good results, but they have become increasingly complex. In this study, we propose a simple translation-based model that relies on the fact that the multiplication of subjects and relations is approximately equal to the object. First, we utilize embeddings to represent entities and relations. Second, we perform vector multiplication on subject embedding and relation embedding to generate a 2D matrix and achieve full fusion of embedding at the element level. Third, we adopt a convolutional neural network on the 2D matrix. Thereafter, we can generate feature maps, which are then spliced into a 1D feature vector. The feature vector is transformed into predicted object embedding through a fully connected operation. Finally, we use the scoring function to score the candidate triples. Experimental results strongly demonstrate that the translation knowledge graph completion model based on 2D convolution achieves state-of-the-art results compared with the baseline.