Cross Lingual Entity

Cross-lingual entity alignment (EA) focuses on identifying equivalent entities across different language knowledge graphs, enabling seamless integration of multilingual data. Current research emphasizes unsupervised and semi-supervised approaches, employing techniques like neighbor triple matching, bipartite matching, and textual entailment frameworks often leveraging pre-trained language models (PLMs) and multilingual encoders to generate robust entity representations. This work is crucial for building truly multilingual knowledge bases and improving applications such as multilingual named entity recognition, machine translation, and cross-lingual knowledge graph completion.