With the further development of knowledge graphs, many weighted knowledge graphs (WKGs) have been published and greatly promote various applications. However, current deterministic knowledge graph embedding algorithms cannot encode weighted knowledge graphs well. This paper gives a promising framework WeExt that can extend deterministic knowledge graph embedding models to enable them to learn weighted knowledge graph embeddings. In addtion, we introduce weighted link prediction to evaluate the weighted knowledge graph embedding models’ performance on completing WKGs. Finally, we give concrete implementation of WeExt based on two translational distance models and two bilinear models. Our experimental results show the proposed framework achieves promising performance in link prediction, weight prediction, and weighted link prediction.

Named Entity Recognition and Relation Extraction are two crucial and challenging subtasks in the field of Information Extraction . Despite the successes achieved by the traditional approaches, fundamental research questions remain open. First, most recent studies use parameter sharing for a single subtask or shared features for both two subtasks, ignoring their semantic differences. Second, information interaction mainly focuses on the two subtasks, leaving the fine-grained informtion interaction among the subtaskspecific features of encoding subjects, relations, and objects unexplored. Motivated by the aforementioned limitations, we propose a novel model to jointly extract entities and relations. The main novelties are as follows: (1)We propose to decouple the feature encoding process into three parts, namely encoding subjects , encoding objects , and encoding relations . Thanks to this, we are able to use fine-grained subtask-specific features. (2)We propose novel inter-aggregation and intra-aggregation strategies to enhance the information interaction and construct individual fine-grained subtask-specific features, respectively. The experimental results demonstrate that our model outperforms several previous state-of-the-art models. Extensive additional experiments further confirm the effectiveness of our model.