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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]

Link prediction in attributed networks has attracted increasing attention recently due to its valuable real-world applications. Various related methods have been proposed, but most of them cannot effectively utilize community structure, neither can they well fuse attribute information and link information to improve the performance. Inspired by our empirical observations on how community structure affects the generation of links, we propose a novel Community Preserving Adaptive Graph Convolutional Networks (CPAGCN) method to tackle the link prediction task in attributed networks. Specifically, CPAGCN is composed of two core modules: network embedding and link prediction. Network embedding module utilizes AGCN to seamlessly fuse link information and attribute information to obtain node representations, which are simultaneously driven to preserve community structure via an appropriate community detection model. Taking these node representations as the input, link prediction module applies multilayer perception (MLP) to directly learn the prediction scores for potential links. Through combining the graph reconstruction loss with the prediction loss to train AGCN and MLP jointly, CPAGCN can learn node representations that are more beneficial to predicting links. To verify the effectiveness of CPAGCN, we conduct extensive experiments on six real-world attributed networks. The results demonstrate that CPAGCN performs better than several strong competitors in link prediction. The source code is available at https://github.com/GDM-SCNU/CPAGCN. • We investigate the impact of community structure on links by empirical observations. • An end-to-end method CPAGCN for link prediction in attributed networks is proposed. • CPAGCN can seamlessly fuse attribute and structural proximities. • CPAGCN uses community preserving network embedding to boost link prediction. • CPAGCN outperforms state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Semantic similarity is a fundamental task in natural language processing that determines the similarity between two concepts within a taxonomy. For example, a pair of words (e.g., car and bike) appear similar because they share the same category (e.g., vehicle). Numerous computation methods, such as distance-based and feature-based approaches, are proposed to precisely depict this similarity. As knowledge graphs become heterogeneous (e.g., DBpedia), existing methods have limitations on utilizing multi-view features (e.g., abstract, structure, and categories). On the one hand, some features are incomplete for various reasons, reducing the effectiveness of embedding methods. On the other hand, the hidden connections among multi-view features are omitted by existing approaches. To address the problems mentioned above, we first extract three subgraphs from a heterogeneous knowledge graph and then combine various embedding approaches to capture the global semantics of each concept. Next, we offer subgraph-based feature fusion models that improve concept representation by fusing multi-view features. Finally, we devise mixed computation methods to calculate the semantic similarity between the two concepts. Experiment results show that multi-view features, particularly the abstract feature, can effectively improve the performance of the proposed methods. Compared to existing approaches, our methods significantly improve the Pearson correlation coefficient by about 7%. The source code of this paper is available at: https://github.com/fiego/SubgraphSS. [ABSTRACT FROM AUTHOR]

Most existing recommendation methods assume that all the items are provided by separate producers rather than users. However, it could be inappropriate in some recommendation tasks since users may generate some items. Considering the user–item generation relation may benefit recommender systems that only use implicit user–item interactions. However, it may suffer from a dramatic imbalance. The number of user–item generation relations may be far smaller than the number of user–item interactions because each item is generated by at most one user. At the same time, this item can be interacted with by many users. To overcome the challenging imbalance issue, we propose a novel Deep Interaction-Attribute-Generation (DIAG) model. It integrates the user–item interaction relation, the user–item generation relation, and the item attribute information into one deep learning framework. The novelty lies in the design of a new item–item co-generation network for modeling the user–item generation information. Then, graph attention network is adopted to learn the item feature vectors from the user–item generations and the item attribute information by considering the adaptive impact of one item on its co-generated items. Extensive experiments conducted on two real-world datasets confirm the superiority of the DIAG method. [ABSTRACT FROM AUTHOR]