does WikiCorpus remove stop_words in gensim?
How did you train a box-embedding, and why did you think it would offer good most_similar() results?
From a (very) quick glance at the 'BoxE' paper by Abboud et al (2020), it appears to require training based on a knowledge base representation – not the raw text that'd come from WikiCorpus. (So I'd not necessarily expect a properly-trained BoxE embedding would have 'stop words' in it at all.)
And, BoxE appears to be optimized for evaluating potential facts – not more general most_similar rankings. So I'd not expect a simple most_similar listing from it to necessarily be expressive.
In usual word2vec, removing stop-words isn't very important and plenty of published work doesn't bother doing so. The downsampling of highly-frequent words already tends to ignore many stop-word occurrences – and their highly diverse usage contexts mean they are likely to get weak word-vectors not especially close to other more-narrow-meaning word-vectors.
So in usual word2vec, stop-words  aren't very likely to be in the top-neighbors, by cosine-similarity, of other more-specific entity words.
I built a box-embedding model on the latest wikipedia articles dump and i need to compare it with the word2vec model in gensim. I saw that if i generate the corpus data as a txt file using get_texts() method in class WikiCorpus there are a lot of stop words, so this make me think that WikiCorpus doesn't delete stop words isn't it?. Now once trained my box model on the wiki corpus txt i notice that calling the "most similar" function that i create appositely for box embedding prints very often stop words, instead the same word passed to the most similar function of word2vec model trained on the same corpus txt produce best results. Can someone suggest me why Word2vec model fit so well despite the corpus txt have a lot of stop words instead my box model on the same corpus not?