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Article summary:
1. This paper presents a novel model for temporal knowledge graph completion, which combines static models with a diachronic entity embedding function.
2. The proposed embedding function is model-agnostic and can be combined with any static model.
3. Experiments indicate the superiority of the proposal compared to existing baselines.
Article analysis:
The article is generally reliable and trustworthy, as it provides evidence for its claims in the form of experiments that demonstrate the superiority of its proposed approach compared to existing baselines. The authors also provide a detailed description of their proposed approach, which makes it easy to understand and evaluate its potential effectiveness.
The article does not appear to have any major biases or one-sided reporting, as it provides an objective overview of the problem and presents both sides equally. It also does not contain any unsupported claims or missing points of consideration, as all claims are backed up by evidence from experiments. Furthermore, there is no promotional content or partiality present in the article, as it focuses solely on presenting the research findings objectively without attempting to promote any particular product or service.
Finally, possible risks are noted in the article, as it mentions that temporal KG completion can be used for applications such as recommendation systems and fraud detection where accuracy is critical and errors can have serious consequences. All in all, this article appears to be reliable and trustworthy overall.