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[PDF] Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques | Semantic Scholar
Source: semanticscholar.org
Article summary:
1. This article discusses the use of knowledge graph embedding (KGE) models to represent entities and relations in a knowledge graph.
2. It examines various techniques for parallel training of KGE models on large-scale knowledge graphs.
3. The article provides an overview of the current state of research in this area, as well as a comparison of different techniques for parallel training.
Article analysis:
The article is generally reliable and trustworthy, providing an overview of the current state of research in the field of knowledge graph embedding (KGE) models and their use for large-scale knowledge graphs. The author presents a comparison of different techniques for parallel training, which is supported by citations from other sources. The article does not appear to be biased or one-sided, as it presents both sides equally and does not make any unsupported claims or omit any points of consideration. Furthermore, there are no promotional elements or partiality present in the article, and all possible risks associated with KGE models are noted. In conclusion, this article can be considered reliable and trustworthy.