Modeling path information for knowledge graph completion

Knowledge graphs (KGs) store real-world information in the form of graphs consisting of relationships between entities and have been widely used in the Semantic Web community since it is readable by machines. However, most KGs are known to be very incomplete. The issues of structure sparseness and noise paths in large-scale KGs create a substantial barrier to representation learning. In this paper, we propose an Attribute-embodied neural Relation Path Prediction (ARPP) model to predict missing relations between entities in a KG. The ARPP framework leverages both structural information and textual information from the KG to enrich the representation learning and aid in learning more valuable information from noise paths for relation prediction. To handle the overlooked equal path weight distribution issue which hinders the performance of KG completion, our method evaluates the information propagation for the path by mining neighboring nodes. In order to verify the benefits of incorporating structural information and textual information and the effectiveness of path weight re-distribution, we conduct experiments from various aspects to evaluate the quantitative results for link prediction and entity prediction task, the accuracy change caused by the ablation studies, the effectiveness of the entity attribute and entity/sequence attention, the applicability of the proposed method on Knowledge Graph Completion task, and case study. Results demonstrate that the ARPP model significantly outperforms the state-of-the-art methods.