Exploring the Frontiers of Knowledge Graph Embeddings: Methods, Challenges, and Applications

Knowledge graphs (KGs), structured representations of entities and their relationships, are increasingly crucial for various Artificial Intelligence (AI) tasks. However, the complex structure and vast scale of KGs pose challenges for efficient reasoning and information retrieval. Knowledge graph embedding (KGE) is a powerful technique to bridge this gap by mapping entities and relations into low-dimensional vector spaces. This paper comprehensively surveys KGE methods, exploring diverse approaches, including point-wise embedding methods and complex vector spaces. We go deep into each method's underlying principles and strengths, highlighting their unique characteristics and providing insights into their effectiveness for various tasks. By exploring MuRP, AttH, and HypHKGE, we contribute to advancing graph embedding techniques and paving the way for more efficient knowledge graph analysis and reasoning.