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The relation completion for knowledge graph requires expanding and enriching a knowledge graph by predicting the missing relation in a given triple which has known head and tail entities. In this paper, we propose a unified embedding-based relation completion framework which mainly includes two contributions. Firstly, based on embedding of triples generated by any embedding model, we utilize deep neural networks to learn feature representations of relations from head and tail entities. This allows us to propose a multi-dimensional feature prediction model for missing relations of triples. Based on the predictive features of missing relations, we match the best relation within the candidate relation set for relation completion. Secondly, to reduce the impact of noisy features and further improve the effectiveness of relation completion, we consider the extraction of key features as a submodular optimization problem by establishing a normalized, non-decreasing submodular function. Finally, testing on multiple public knowledge graph datasets, the results demonstrate that our proposed relation completion framework can significantly improve existing relation completion approaches. [ABSTRACT FROM AUTHOR]

Most existing methods for recommendation assume that all the items are of the same category. However, single-category recommendations are no longer sufficient to fulfill the diverse needs of users in the real world. Existing methods cannot be directly applied to recommend multiple categories of items due to the different properties and complex relationships among them. However, a drawback of these approaches is their assumption of a fixed and homogeneous data format, making them incapable of addressing the challenge of recommending multiple categories of items. Recommendation of multiple categories of items to users concurrently has become a challenging task. To tackle this problem, we design a novel method Knowledge Graph Transformer Network (KGTN) for explainable multi-category item recommendation, inspired by advances in knowledge graph in the field of recommendation. Knowledge graph and neural network methods have shown advantages in addressing recommendation problems in heterogeneous graph structures. This is because different categories of items have unique attributes and dimensions, which can be effectively represented and integrated by incorporating knowledge graph and graph neural network techniques. That is, our approach can handle heterogeneous collaborative knowledge graphs composed of users and items, and can mine hidden path relationships without defining original paths. In addition, its algorithmic process has interpretability. Specifically, graph transformer layer converts the heterogeneous input graph into a useful meta-path graph for recommended task; graph convolution layer learns the node representation on the new meta-path graph. Finally, we calculate the inner product of user and item vector representations to output the probability for the recommendation. At the same time, we use the critical path in the learned useful meta-path graph as the explanation. Comprehensive experiments on three datasets demonstrate that KGTN achieves state-of-the-art performance over existing baselines. [ABSTRACT FROM AUTHOR]