This article proposes several ways to enhance the MAKG, including author name disambiguation, field of study classification and entity embedding. This is a valuable contribution to the literature and provides important outlooks for future work to improve knowledge graph data.

Following the first round of reviews, the authors have made various changes in response to my comments on the initial submission. I am satisfied that the authors have address all major issues that were raised.

I only have a few minor suggestions for the final editing of the article in preparation for publication:
1. I have raised the issue with the ordering of Asian names (surname-name vs name-surname) in my first review. And, I am still not in complete agreement with the authors about the problem. The problem is well documented in the literature (see for example, Chen et al., 2021, Kim et al., 2021, and Teixeira da Silva, 2020). In the list of ten most common author names provided in the current article, “Wang Wei” and “Wei Wang” both appears, and are treated to distinct blocks as far as I can tell. However, it is possible that at least some of them represent the same author, considering also that “Wang” is a common surname, while “Wei” is much less so. I do, however, recognise that this is a difficult problem that is not solved. I do suggest at least discussing this as a limitation to the current work.
2. Please check the figures given in the abstract (i.e., 254 million authors, etc.) as they do not match with what is provided in the main text.
3. In Section 3 (Author Name Disambiguation), the letter “n” is used to denote two different things, i.e., number of authors and number of features. I suggest using a different variable for one of them.

Apart from the above, I have no further queries. Hence, I have no objections to accepting the article for publication if the editor and the other reviewers are happy to do so.

Again, I appreciate the opportunity for reviewing a very interesting article.

References:
• Chen Y, Jiang Z, Gao J, Du H, Gao L, Li Z. (2021). A supervised and distributed framework for cold-start author disambiguation in large-scale publications. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-05684-y
• Kim J, Kim J, Kim J. (2021). Effect of Chinese characters on machine learning for Chinese author name disambiguation: A counterfactual evaluation. Journal of Information Science. https://doi.org/10.1177/01655515211018171
• Teixeira da Silva, JA (2020). Chinese Names in the Biomedical Literature: Suggested Bibliometric Standardization. Publishing Research Quarterly 36, 254-257. https://doi.org/10.1007/s12109-020-09725-1