Ontology-based Knowledge Graphs (KGs) augment entity representation through additional semantic information, facilitating link prediction for unseen entities through predefined ontology libraries. While most existing knowledge graph representation learning methods predominantly focus on co-optimizing both entities and ontologies to leverage ontological contexts, the structural-semantic discrepancies in ontology-based KGs have been largely overlooked. Through graph structure analysis, we identify two fundamental limitations: (1) structural incompatibility between entity subgraph semantics and multi-ontology mappings (1-N redundancy), and (2) missing explicit ontology link in subgraph contexts (1-0 absence). To resolve these issues, we propose a structural empowered module built upon link prediction backbones. First, we develop a subgraph-aware semantic expansion module that coordinates $k$-hop neighborhood information with LLM-generated descriptions to alleviate structural sparsity. Subsequently, a contrastive ontology matching mechanism resolves structural inconsistencies by computing adaptive similarity metrics between ontology embeddings and subgraph-derived semantic prototypes. Experimental results demonstrate that our model outperforms fourteen state-of-the-art models, maintaining robust performance across varying benchmarks and subgraph density conditions.
Hao Li, K. Liang, Lingyuan Meng et al.· IEEE Transactions on Pattern...· 0 citations
Anchor-based methods have demonstrated significant success in multi-view clustering, particularly in handling large-scale datasets. However, existing approaches suffer from two critical limitations: (1) clustering performance is highly sensitive to the quality of initial anchors, and (2) multi-view interactions are often limited to the anchor graph construction stage, resulting in semantic misalignment among independently generated anchors. To address these challenges, we propose a unified paradigm based on Cross-View Semantic Propagation (CVSP), a plug-and-play framework that effectively improves anchor quality. Depending on whether the initial anchors are fixed or learnable, this strategy is instantiated in two variants, CVSP-F for fixed anchors and CVSP-U for learnable anchors with iterative updates. Specifically, the anchor alignment module first ensures cross-view consistency among the initial anchors. Then, the anchor refinement module, leveraging a cross-view graph, optimizes the anchor representations. Finally, a revised anchor graph construction module is introduced to further improve clustering robustness. Experimental results demonstrate that both CVSP-F and CVSP-U consistently outperform twelve state-of-the-art anchor-based methods across multiple datasets. Furthermore, extensive evaluations confirm the generalization capability of the anchor refinement module when integrated with various existing methods.
Suyuan Liu, Siwei Wang, Ke Liang et al.· IEEE Transactions on Pattern...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
