Open ghost opened 5 years ago
Hi Daria,
The code is currently not open-sourced but we can share the code under a non-commercial licence agreement. If this is something that interests you, please send us an email.
Hi Matthias,
Many thanks for your reply. I have sent an email to mathias.niepert@neclab.eu.
Kindest regards, Daria
Can I have the source code too for non commercial use? I am student at university with official email address.
Dear Alberto and Mathias,
If it possible, I would like to have the source code too. I am university student and this will be very helpful for my course work. I have sent an email to mathias.niepert@neclab.eu.
Thank you in advance!
Hello Alberto, and Mathias!
I'm Daniel Ayala, a researcher at the University of Seville. We are currently exploring and comparing embedding techniques that use information from attributes. Would it be possible to get the code under a non-commercial license?
If it is indeed possible, my email is dayala1@us.es
Thanks in advance.
Dear Alberto and Mathias,
If it possible, I would like to have the source code. I am university student and this will be very helpful for my course work. I have sent an email to mathias.niepert@neclab.eu.
My email is shuangliang@std.uestc.edu.cn
Thank you in advance!
Hello Mathias and Alberto! I’m He Ma , I have read your paper MMKG , and I am currently exploring the multi-modal entity alignment approaches, but I just took my first step in this research .If it possible, I would like to have the source code. Your source code I think is very groundbreaking for the developing of multi-modal. I am graduate student and this will be very helpful for my course work.
My email is mahe202104@163.com.
Thanks in advance!
Dear Alberto and Mathias,
Is it possible for you to share the code of the paper [1]? It seems that the current repository only stores the numerical KGs but not the code of embeddings?
Many thanks and best regards, Daria
[1] Alberto García-Durán, Mathias Niepert: KBlrn: End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features. UAI 2018: 372-381