Entity Alignment

Entity alignment (EA) aims to identify equivalent entities across different knowledge graphs (KGs), facilitating knowledge integration and data fusion. Current research heavily focuses on developing robust and scalable methods, employing techniques like graph neural networks (GNNs), embedding-based approaches, and increasingly, the integration of large language models (LLMs) to leverage semantic information and improve alignment accuracy. This field is crucial for advancing knowledge representation and reasoning, with significant implications for diverse applications including cross-lingual information retrieval, knowledge base completion, and multi-modal data integration.