(2017) proposes KGE-LDA to incorporate embeddings of KGs into topic models to extract better topic representations for documents and shows promising performance.
kGE-LDA forces words and entities to have identical latent representations, which is a rather restrictive assumption that prevents the topic model from recovering correct underlying latent structures of the data, especially in scenarios where only partial kGs are available.