You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I didn't find the ‘ transE_emb.py ’ file in your code, so I would like to ask how the initialization vector of transE is represented in the training, or can you give me the ‘ transE_emb.py ’ file? At the same time, I have a question about KEQA. The vectorized representation obtained after KEQA is not in the same vector space as the embedded representation TransE, so when the Euclidean distance between the two is found, will there be an error match?
The text was updated successfully, but these errors were encountered:
Yes, you are right. It is not an error match, but an inaccurate match. Our goal is to make the predicted entity representation as close as possible to the representation of the correct head entity. The goal is NOT to recover exactly the same representation, but a close one, so that the learned vector could be used as a pointer to lead us to the current head entity.
I didn't find the ‘ transE_emb.py ’ file in your code, so I would like to ask how the initialization vector of transE is represented in the training, or can you give me the ‘ transE_emb.py ’ file? At the same time, I have a question about KEQA. The vectorized representation obtained after KEQA is not in the same vector space as the embedded representation TransE, so when the Euclidean distance between the two is found, will there be an error match?
The text was updated successfully, but these errors were encountered: