Reviewer has chosen to be Anonymous

Overall Impression: Good

Content:
Technical Quality of the paper: Good
Originality of the paper: Yes, but limited
Adequacy of the bibliography: Yes

Presentation:
Adequacy of the abstract: Yes
Introduction: background and motivation: Good
Organization of the paper: Needs improvement
Level of English: Satisfactory
Overall presentation: Good

Detailed Comments:

I would like to thank the authors for all the edits to the paper. My comments have been answered and most of my suggestions have been taken into consideration.

I still believe the paper is trying to do too many things in one single paper and we limited space; there are different levels of specificity in the presentation and the balance between the survey and vision paper is still not clear. Nonetheless, I think the manuscript was improved: I appreciate the changes in the introduction and the more focused discussion sections (and also for citing all the datasets).

Reviewer has chosen to be Anonymous

Overall Impression: Good

Content:
Technical Quality of the paper: Good
Originality of the paper: Yes
Adequacy of the bibliography: Yes

Presentation:
Adequacy of the abstract: Yes
Introduction: background and motivation: Good
Organization of the paper: Satisfactory
Level of English: Satisfactory
Overall presentation: Good

Detailed Comments:

The authors have addressed most of the issues mentioned in the last review.

Some minor issue:

1. It seems Table 1 misses list role composition axioms.

2. Line 49: their score function --> their score functions.

3. Line 12 - 14 in in Page 7: I think this is not a specific limitation of LLMs for KGC. Almost all the ML-based KGC models have this limitation.