Semantic Web-Based Latent Alignment and Label Propagation Model for Multi-Label Learning with Missing Labels

In the context of multi-label classification on the Semantic Web—where entities and resources are often associated with multiple, interdependent labels—traditional methods typically assume complete label information during training. However, due to the heterogeneous and incomplete nature of Semantic Web data, missing labels are a common challenge that significantly degrades model performance. To address this critical issue, We introduce SWLP-LAM, an integrated architectural framework that uniquely combines semantic web principles with multi-label learning techniques to address the challenge of missing labels. SWLP-LAM introduces an energy-constrained diffusion mechanism to effectively encode instance graphs, capturing global semantic consistency across latent structures. This mechanism ensures that semantic relationships between instances are preserved even when label information is incomplete, thereby mitigating the impact of missing annotations. Additionally, SWLP-LAM employs a Teacher-Student framework, where the Teacher module learns distinct modalities for each label class, constructing a composite latent space. This composite space guides the Student module in mapping instance features, thereby enhancing label prediction accuracy while reducing the dependency on complete label information during training. A key innovation of SWLP-LAM lies in its graph-based label propagation module. This module not only recovers missing label confidences but also explicitly leverages label dependencies, which are particularly prevalent in Semantic Web data. By modeling these dependencies, SWLP-LAM can infer missing labels more accurately, even in scenarios with high label sparsity. The propagation mechanism further ensures that semantic information flows through the graph structure, reinforcing the model's ability to handle incomplete annotations while preserving the integrity of interdependent label relationships. Extensive experiments conducted on five benchmark multi-label datasets, as well as a real-world Semantic Web-inspired dataset, JUSTICE, demonstrate the superiority of SWLP-LAM. The results show that SWLPLAM consistently outperforms state-of-the-art methods in scenarios with missing labels, achieving significant improvements in classification accuracy, F1-score, and robustness. These findings highlight the effectiveness of SWLP-LAM in addressing the challenges of multi-label learning with missing labels, positioning it as a promising approach for semantic annotation and knowledge discovery in incomplete and heterogeneous data environments.