Knowledge graph embeddings. Embedding of a knowledge graph.

Knowledge graph embeddings. These mathematically-inspired models are not only highly scalable for inference in large KGs, but also have many explainable advantages in modeling different relation patterns that can be validated through both formal Oct 12, 2024 · Reviewed a comprehensive survey paper on knowledge graph embedding, detailing key models, methods, and applications, while discussing challenges and future research directions in this evolving field. Jun 7, 2025 · knowledge graph embedding (KGE), as a crucial technique for link prediction [12], [13], [14], has improved significantly in recent years. These algorithms learn low-dimensional embeddings of entities and relations in a knowledge graph, allowing for efficient computation of similarity and inference tasks. This approach maps relations and entities into a low-dimensional space by generating vector embeddings, while striving to maintain as much semantic and structural information as feasible. Sep 21, 2023 · Many mathematical models have been leveraged to design embeddings for representing Knowledge Graph (KG) entities and relations for link prediction and many downstream tasks. In representation learning, knowledge graph embedding (KGE), also called knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph 's entities Apr 9, 2023 · Conclusion In conclusion, knowledge graph embedding algorithms have become a powerful tool for representing and reasoning about complex structured data. Feb 12, 2024 · Graph structural information is also considered in SimKGC to boost the score of entities that appear in the K-hop neighborhood. The vector representation of the entities and relations can be used for different machine learning applications. This article provides a comprehensive overview of . Knowledge graphs are large-scale, structured repositories of information that consist of entities and their relationships. KEPLER [111] uses the textual descriptions of head and tail entities as initialization for entity embeddings and uses TransE embedding as a decoder. Knowledge graph embeddings are a crucial technique used to represent these entities and relationships as dense vectors in a high-dimensional space, enabling efficient and scalable inference in large knowledge graphs. Embedding of a knowledge graph. veye xrrpmxmiu uoqtpd walxjd uzstnh myzgv ghouq uzfq uyjf lgfhik