Multi Modal Entity Alignment

Multi-modal entity alignment (MMEA) seeks to identify corresponding entities across different knowledge graphs that incorporate multiple data types, such as text, images, and relational links. Current research emphasizes robust fusion of these diverse modalities, often employing transformer-based architectures and contrastive learning methods to generate effective entity representations, while addressing challenges like modality-specific noise and missing data. Improved MMEA techniques will facilitate more comprehensive and accurate knowledge graph integration, impacting various applications including cross-lingual information retrieval, knowledge base completion, and enhanced multi-modal large language models.