I hope this message finds you well. I am writing to you with great interest in your recent work titled "hire: Hierarchical Relational Learning for Few-shot KGC," which I found to be an intriguing contribution to the field of Knowledge Graph Completion. Your innovative approach to few-shot learning within the context of hierarchical relational data has caught my attention.
I am particularly impressed by the ideas presented in your paper and the potential impact they could have on advancing the field. As someone who is deeply involved in research related to knowledge representation and graph-based learning, I am keenly interested in exploring the practical implications of your work.
To that end, I am eager to delve into the details of your methodology and experiment with the proposed techniques. I believe that a deeper understanding of your code implementation would greatly aid in this endeavor. Therefore, I am writing to kindly inquire about the potential release date of the code associated with your "hire" project. Could you please provide me with an update on when the code might become publicly available?
Access to your codebase would undoubtedly facilitate further exploration and experimentation, allowing researchers like me to build upon your foundations and contribute to the advancement of the field. I understand that code preparation and documentation can be time-consuming, and I truly appreciate the effort that goes into making research reproducible.
Thank you very much for your time and consideration. I look forward to your response and hope to have the opportunity to engage with your work more closely.
I hope this message finds you well. I am writing to you with great interest in your recent work titled "hire: Hierarchical Relational Learning for Few-shot KGC," which I found to be an intriguing contribution to the field of Knowledge Graph Completion. Your innovative approach to few-shot learning within the context of hierarchical relational data has caught my attention.
I am particularly impressed by the ideas presented in your paper and the potential impact they could have on advancing the field. As someone who is deeply involved in research related to knowledge representation and graph-based learning, I am keenly interested in exploring the practical implications of your work.
To that end, I am eager to delve into the details of your methodology and experiment with the proposed techniques. I believe that a deeper understanding of your code implementation would greatly aid in this endeavor. Therefore, I am writing to kindly inquire about the potential release date of the code associated with your "hire" project. Could you please provide me with an update on when the code might become publicly available?
Access to your codebase would undoubtedly facilitate further exploration and experimentation, allowing researchers like me to build upon your foundations and contribute to the advancement of the field. I understand that code preparation and documentation can be time-consuming, and I truly appreciate the effort that goes into making research reproducible.
Thank you very much for your time and consideration. I look forward to your response and hope to have the opportunity to engage with your work more closely.
Best regards.