The objective of knowledge graph completion is to comprehend the structure and inherent relationships of domain knowledge, thereby providing a valuable foundation for knowledge reasoning and analysis. However, existing methods for knowledge graph completion face challenges. For instance, rule-based completion methods exhibit high accuracy and interpretability, but encounter difficulties when handling large knowledge graphs. In contrast, embedding-based completion methods demonstrate strong scalability and efficiency, but also have limited utilisation of domain knowledge. In response to the aforementioned issues, we propose a method of pre-training and inference for knowledge graph completion based on integrated rules. The approach combines rule mining and reasoning to generate precise candidate facts. Subsequently, a pre-trained language model is fine-tuned and probabilistic structural loss is incorporated to embed the knowledge graph. This enables the language model to capture more deep semantic information while the loss function reconstructs the structure of the knowledge graph. This enables the language model to capture more deep semantic information while the loss function reconstructs the structure of the knowledge graph. Extensive tests using various publicly accessible datasets have indicated that the suggested model performs better than current techniques in tackling knowledge graph completion problems.

In recent years, multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical applications. Despite the significant advances in multi-view clustering, two challenges still need to be addressed, i.e., how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for clustering. In this study, we propose a novel Two-level Weighted Collaborative Multi-view Fuzzy Clustering (TW-Co-MFC) approach to address the aforementioned issues. In TW-Co-MFC, a two-level weighting strategy is devised to measure the importance of views and features, and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering consistency. Then an iterative optimization objective function based on the maximum entropy principle is designed for multi-view clustering. Experiments on real-world datasets show the effectiveness of the proposed approach.