ABSTRACT Knowledge graph embedding (KGE) offers a more intuitive approach to discovering potential relations between known entities. However, current models are associated with challenges such as a large number of training parameters and low training efficiency and fail to provide in‐depth analysis of the impact of embedding dimensionality on entities and relations in link prediction performance. Therefore, we investigate the impact of entity and relation embedding dimensions on their interaction and assess how these dimensions affect the performance of KGE models. Based on these insights, we propose a novel dual encoder‐decoder model, NDcRE, which includes decoders MlpD and AttnMlpD, designed to capture long‐distance interactions and improve link prediction performance with fewer parameters. Evaluated on four benchmarks, WN18RR, FB15k‐237, DB100k, and YAGO3‐10, NDcRE significantly improves model efficiency by utilizing fewer parameters and dimensions, thereby enhancing both its utility and convenience. In particular, the AttnMlpD decoder further reduces the model's training parameters, enabling it to deliver strong performance even in environments with limited computational resources.

Knowledge Graph Embedding (KGE) is a powerful technique for predicting missing links in knowledge graphs. Current mainstream research primarily focuses on Transformer-based language pre-training models and graph neural network models. However, these models suffer from several issues, including a large number of parameters, low training efficiency, and dimensional explosion, which hinder their application in large-scale knowledge graphs. This paper explores the impact of entities and relations embedding dimensions on the interaction between them. Through experimental analysis, we examine how these dimensions affect the performance of KGE models. Based on these insights, we propose a novel dual encoder-decoder model called DcKE, which is designed to capture long-distance interactions and improve link prediction performance with less parameters. We conduct an extensive experimental evaluation on four widely-used datasets: WN18RR, FB15k-237, DB100k, and YAGO3-10. The results demonstrate that DcKE, by utilizing a small number of parameters and dimensions, significantly improves model efficiency, making it a promising method for large-scale knowledge graph embedding.

As an effective knowledge organizing and modeling technique, knowledge graph has become a key topic in graph research, but the practical application of KG is limited by its incompleteness. In recent years, many knowledge graph embedding(KGE) methods for knowledge graph completion(KGC) based on graph neural networks(GNN) have been proposed. However, most GNN-based KGC models are still suffer from the encoder-decoder structure of low efficiency in aggregating neighborhood information and the difficulty of model training. This paper present an optimized model that incorporates Neighborhood information into knowledge inference, to improve the performance of KGC models based on multilayer perceptron network(MLP), which is named N-MlpE. We generate an input sequence that includes the query triplet and its neighbor entities and relationships, and then feed it to an adaptive filter module to remove useless neighbors for the inference to improve the accuracy of the inference, and reduce the computational complexity of training the model. The filtered sequence is then fed into a weight calculation module and a feature extraction module simultaneously, the former is designed based on selfattention to model the relevant rule inference, which enhances the interpretability of KGE models, and the latter is based on MLP and used to capture the long-distance interactions between triplets, which can significantly improve the accuracy of inference. Extensive experiments are conducted on two standard KG datasets WN18RR and FB15k237 to verify the effectiveness of N-MlpE, the results show that the accuracy of N-MlpE model outperforms most GNN-based models.