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
Heterogeneous information networks (HINs) play an indispensable role in a wide range of domain-specific applications, from recommender systems to conversational platforms. Textual heterogeneous information networks (HINs) are graphs with abundant textual information. Currently, most advanced approaches to mine textual features from HINs follow a “pre-training and fine-tuning” schema which may cause a “negative transfer” problem since there is a gap between pre-training tasks and downstream tasks. We propose a prompt-learning framework P-HIN that provides a new angle to align textual information and graph information, while narrowing down the gap between the pre-trained models and various downstream tasks. To the best of our knowledge, we are among the first to introduce and exploit the idea of prompt learning to align HIN features and textual features. The proposed framework P-HIN is composed of a text encoder and a graph encoder, and uses contrastive learning to align and fuse the graph-text pair. This pre-training operation naturally fits the few-shot learning setting. For the graph encoder, we introduce two graph pre-training tasks, masked node modeling and edge reconstruction, to exploit self-supervised information. During optimization, instead of handcrafted prompts, we use a learnable continuous text that enables more efficient and task-relevant transfer to downstream datasets. We consider a residual connection to use the context from the graph to prompt the text encoder. In experiments, P-HIN consistently and significantly outperforms state-of-the-art alternatives on all real-life datasets.
Yang Fang, Xiang Zhao, Daojian Zeng et al.· ACM Transactions on Informat...· 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
This work proposes SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation and establishes a scalable, agent-driven paradigm for computational biology.
Songhan Wang, Haoang Chi, He Li et al.· Proceedings of the 32nd ACM...· 1 citation
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