Review Comment:
* Summary: The article describes an approach to convert vector geographic features extracted from multiple historical maps into contextualized Spatio-temporal KGs. The resulting graphs can be easily queried (GeoSPARQL) to understand the changes in different regions over time. The approach and its evaluation focus on linear and polygonal geographic features.
* Overall Evaluation (ranging from 0-100):
[Criterion 1]
[Q]+ Quality: 95
[R]+ Importance/Relevance: 85
[I]+ Impact: 90
[N]+ Novelty: 80
[Criterion 2]
[W]+ Clarity, illustration, and readability: 95
[Criterion 3]
[S]+ Stability: 100
[U]+ Usefulness: 90
[Perspective]
[P]+ Impression score: 90
* Dimensions for research contributions (ranging from 0-100):
(1) Originality (QRN): 87
(2) Significance of the results (ISU): 93
(3) Quality of writing (QWP): 93
* Overall Impression (1,2,3): 91
* Suggested Decision: [Accept]
* General comments:
The work is solid. The paper is easy to follow and understand.
There is a novelty in the proposed approach.
* Major points for improvements:
{
# all have been addressed
}
* About the data files and related software artifacts: (“Long-term stable URL for resources”)
(1) "data files are well organized and contain a README file which makes it easy to assess the data": [YES. CORRECTED]
(2) "the provided resources appear to be complete for replication of experiments": [YES. COMPLETE]
(3) "the chosen repository is appropriate for long-term repository discoverability": [YES. GitHub]
(4) "the provided data artifacts are complete": [YES]
* Minor corrections:
[A06] Indeed, this (unrotated) box is the required input to the reverse-geocoding service and poses a limitation for us. We have clarified it and described how the calculation is done (section 2.4, paragraph 5).
[R06] In the manuscript, you didn't mention that is a "limitation" of the reverse-geocoding service. Perhaps, it would be better to mention this part in section 5, paragraph 5, where you described "several limitations".
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