Embedding Based Entity Alignment

Embedding-based entity alignment (EA) aims to identify corresponding entities representing the same real-world object across different knowledge graphs (KGs) by representing entities as vectors and comparing their similarity. Current research focuses on improving the accuracy and interpretability of these methods, exploring architectures like graph neural networks (GNNs) and generative models (e.g., VAEs and GANs) to better leverage KG structure and semantics, and addressing scalability challenges for large datasets. This work is significant for facilitating knowledge graph integration and fusion, enabling more comprehensive and accurate data analysis across diverse sources with applications in various domains, including cybersecurity and multi-lingual knowledge bases.