Multiview Translation Learning for Knowledge Graph Embedding

Abstract

Recently, knowledge graph embedding methods have attracted numerous researchers’ interest due to their outstanding effectiveness and robustness in knowledge representation. However, there are still some limitations in the existing methods. On the one hand, translation-based representation models focus on conceiving translation principles to represent knowledge from a global perspective, while they fail to learn various types of relational facts discriminatively. It is prone to make the entity congestion of complex relational facts in the embedding space reducing the precision of representation vectors associating with entities. On the other hand, parallel subgraphs extracted from the original graph are used to learn local relational facts discriminatively. However, it probably causes the relational fact damage of the original knowledge graph to some degree during the subgraph extraction. Thus, previous methods are unable to learn local and global knowledge representation uniformly. To that end, we propose a multiview translation learning model, named MvTransE, which learns relational facts from global-view and local-view perspectives, respectively. Specifically, we first construct multiple parallel subgraphs from an original knowledge graph by considering entity semantic and structural features simultaneously. Then, we embed the original graph and construct subgraphs into the corresponding global and local feature spaces. Finally, we propose a multiview fusion strategy to integrate multiview representations of relational facts. Extensive experiments on four public datasets demonstrate the superiority of our model in knowledge graph representation tasks compared to state-of-the-art methods.

Knowledge graph embedding via the properties of mapping and dynamic matrix

“…The seminal TransE model is incorporated to the simple 1 to 1 relation and it faces the complexity for facing the relations such as N to N, 1 to N and N to 1. To rectify the complicated relation issue, a new TransH is developed that permit every entity with unique representation for every relation (7) . TransH architects the relation as a vector R on a hyperplane and the entity vectors such as HD and TL, where the entities are projected into a relation-specific hyperplanes (HD i⊥ and TL i⊥ ).…”

“…It is applicable to the scenario of zero-shot approach. The multiview translation learning (MvTransE) (7) learns the fact that is relation from the perspective of local and global. The relation in the knowledge is learned from the correlation and it depicts the low rank structure over the embedded relation matrix (14) .…”

Learning Model for Local Wisdom-based Prophet's Hadith Translation

“…The second is the interactive TL model according to (Yan, 2020) and (Marlianingsih et al, 2020); the third is the Multiview TL model according to (Bin et al, 2020); the fourth is the network-based TL model (Mu & Yang, 2020), and the fifth is online TL model TL according to (Su et al, 2021). According to (Yan, 2020) interactive TL model have a positive effect on learning outcomes.…”

“…In Indonesia, according to (Marlianingsih et al, 2020), translation learning is elearning-based with a virtual-ethnography-based translation method. According to (Bin et al, 2020), the Multiview TL model provides experience about relational facts from global-view and local-view perspectives. Meanwhile, according to (Mu & Yang, 2020)network and knowledge-based TL model translates learning more efficiently.…”