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Multi-relational knowledge graph completion method with local information fusion | SpringerLink
Source: link.springer.com
Article summary:
1. Knowledge Graphs (KGs) are used to enhance search engine results with information from a variety of sources.
2. Multi-relational KGs have been constructed and applied in professional domains, but there are still unresolved tasks related to them.
3. A new model called Joint Graph Attention Networks (JGAN) has been proposed that takes into account the multiple relation features of knowledge graphs and the importance of different local directional relationships.
Article analysis:
The article is generally reliable and trustworthy, as it provides an overview of Knowledge Graphs (KGs), their applications, and the need for a new model to address unresolved tasks related to multi-relational KGs. The article also provides a detailed description of the proposed Joint Graph Attention Networks (JGAN) model, which is designed to take into account the multiple relation features of knowledge graphs and the importance of different local directional relationships. 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, the article does not contain any promotional content or partiality towards any particular viewpoint or opinion. Additionally, possible risks associated with using this model are noted in the article, such as potential errors due to incomplete data or incorrect assumptions about relationships between entities in a knowledge graph.