Papers by Dingqi Yang
HyperCL: A Contrastive Learning Framework for Hyper-Relational Knowledge Graph Embedding with Hierarchical Ontology (2024.findings-acl)
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| Challenge: | Existing studies neglect the ontology of knowledge Graph (KG) embeddings and suffer from the dominance issue of facts over ontologies. |
| Approach: | They propose a framework for hyper-relational KG embeddings that captures the hierarchical ontology and a concept-aware contrastive loss to alleviate the dominance issue. |
| Outcome: | The proposed framework improves on three real-world datasets and shows that it can integrate with other embedding methods and improve link prediction performance. |
HyperFM: Fact-Centric Multimodal Fusion for Link Prediction over Hyper-Relational Knowledge Graphs (2025.acl-long)
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| Challenge: | Existing link prediction techniques focus on learning the complex relationships between entities and relations while ignoring the multimodal information. |
| Approach: | They propose a fact-centric fusion technique that captures complex interactions between different data modalities while accommodating the hyper-relational structure of the KG in a facts-centric manner. |
| Outcome: | The proposed technique improves on two real-world KG datasets by 6.0-6.8% over baselines. |