4 papers
GATHER: Convergence-Centric Hyper-Entity Retrieval for Zero-Shot Cell-Type Annotation
Zhonghui Zhang, Feng Jiang, Shaowei Qin +2
Zero-shot single-cell cell-type annotation aims to determine a cell's type from a given set of expressed genes without any training. Existing knowledge-graph-based RAG approaches r…
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
Rui Zhang, Yixin Su, Bayu Distiawan Trisedya +4
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-bas…
OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment
Zhishuo Zhang, Chengxiang Tan, Xueyan Zhao +2
Cross-lingual and cross-domain knowledge alignment without sufficient external resources is a fundamental and crucial task for fusing irregular data. As the element-wise fusion pro…
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment
Zhishuo Zhang, Chengxiang Tan, Haihang Wang +2
Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object…