Can we consider Meta-features of a datasets as its embeddings?

While reading some works on meta-learning. I had this doubt. Can we consider meta-features of a dataset as it's embedding ? Given the meta-feature is a lower dimensional representation which also try to retain properties of a dataset. Embeddings are essentially low dimension representation of some high dimensional concept.

Is it fair to use embeddings instead of meta-features ? or can we use representation instead of meta-features