Representation Learning with Ordered Relation Paths for Knowledge Graph Completion
This work addresses link prediction in knowledge graphs, offering an incremental improvement by focusing on the order and nonlinear contributions of relation paths.
The paper tackles the problem of knowledge graph incompleteness by proposing OPTransE, a method that incorporates ordered relation paths and nonlinear path features for link prediction, achieving better performance than state-of-the-art methods on two benchmark datasets.
Incompleteness is a common problem for existing knowledge graphs (KGs), and the completion of KG which aims to predict links between entities is challenging. Most existing KG completion methods only consider the direct relation between nodes and ignore the relation paths which contain useful information for link prediction. Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning. In addition, these path-based models always ignore nonlinear contributions of path features for link prediction. To solve these problems, we propose a novel KG completion method named OPTransE. Instead of embedding both entities of a relation into the same latent space as in previous methods, we project the head entity and the tail entity of each relation into different spaces to guarantee the order of relations in the path. Meanwhile, we adopt a pooling strategy to extract nonlinear and complex features of different paths to further improve the performance of link prediction. Experimental results on two benchmark datasets show that the proposed model OPTransE performs better than state-of-the-art methods.