Automatic evaluation of complex alignments: an instance-based approach

My previous comments and concerns have been properly addressed.

Some suggestions:
- p4, col1, task-oriented. Since we are in related work, that would be good to add one reference.
I am thinking of https://doi.org/10.1007/978-3-540-68234-9_31 or https://doi.org/10.1007/978-3-642-04930-9_53 which was among of the first ones, but that may be others.
- p7, col 2: thus should be "An intance-based comparison", like the others.
- p10, To fully address my previous concern, at the beginning of 5.1, I suggest to indicate that "the population stage is very important as the chosen instances may influence the result of the evaluation".

This article has now gone through several rounds of revision. Reviewing all the comments and changes, I do see incremental improvement of the paper after each round. At the same time, I think some of the intrinsic limitations of the research remain, namely the fundamental difficulty of the instance-based evaluation approach that ultimately requires the instance set to be regular and easily comparable. While the authors did make an effort to provide a synthetic instance dataset in the framework of OAEI and their own evaluation, and have clarified the related approach, the paper remains vague on the usability of the instance-based approach over real-life use cases (e.g. when instance matching is hard) and realistic instance data. I consider this a weakness that could have been addressed by performing real-world case studies.

Nevertheless, acknowledging the contributions and the efforts by the authors through one major and two minor revision rounds, I suggest accepting the end result.

This paper has presented an evaluation benchmark for ontology so that complex correspondences can be evaluated. The presentation is clear with fine analysis. I recommend the acceptance of this paper after minor revision.

1. Computational complexity is an important evaluation criterion for complex alignments, the authors may describle more in this item.
2. One book and one paper are based on the ontology matching techniques, the authors may describe those ideas in the introduction section.
2.1 Xingsi Xue, Junfeng Chen, Jeng-Shyang Pan, "Evolutionary Algorithm based Ontology Matching Technique", Beijing: Science Press, 2018
2.2 Xingsi Xue, Jeng-Shyang Pan, “A segment-based approach for large-scale ontology matching”, Knowl. Inf. Syst. 52(2): 467-484, 2017