Papers by Chunxia Zhang
A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion (2024.findings-acl)
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| Challenge: | Existing knowledge graph completion methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, making it difficult to capture logic semantic between relations and global topological context information. |
| Approach: | They propose a joint approach with Topological Context learning and Rule Augmentation (TCRA) it uses a topological context learning mechanism and a relation rule context learning system . |
| Outcome: | The proposed approach performs better on three benchmark datasets and is widely used in knowledgeintensive applications. |
Constrained Tuple Extraction with Interaction-Aware Network (2023.acl-long)
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| Challenge: | Existing knowledge triples lack constraints for their authenticity due to spatial, temporal, or other constraints. |
| Approach: | They propose a constrained tuple extraction task to guarantee the validity of knowledge tifles by using an interaction-aware network to extract constrained text. |
| Outcome: | The proposed model outperforms existing models on the dataset and the public CaRB dataset. |