Review Comment:
# Summary/Description
The revised article describes MQALD, version 4 (01/04/2021) of a modified QALD dataset and a newly generated dataset that focuses on SPARQL operation modifiers.
Thanks to the authors again for answering our questions in the cover letter. The authors also managed to rework larger parts of the remarks. I also want to acknowledge the excellent work of the other two reviewers.
The remarks by R1 about the QA system capability are understandable, but so is the answer by the authors. I see this analysis as a minor research contribution outside of this resource paper, i.e., which QA system can answer what types of queries. This paper makes even more sense in the light of recent publications, such as CBench (https://arxiv.org/pdf/2105.00811).
# Short facts
Name: MQALD
URL: https://zenodo.org/record/4479876 , https://github.com/lsiciliani/MQALD (updated April 1, 2021)
Version date and number: 3.0., May 21, 2020
Licensing: GNU General Public License v3.0
Availability: guaranteed
Topic coverage: not applicable
Source for the data: The existing QALD - benchmark series https://github.com/ag-sc/QALD
Purpose and method of creation and maintenance: By extracting SPARQL queries containing modifiers and adding 100 novel questions.
Reported usage: From Zenodo - 109 (was 75 at the time of the last review) views and 36 (was 23) downloads at the time of review (18.05.2021)
Metrics and statistics on external and internal connectivity: None.
Use of established vocabularies (e.g., RDF, OWL, SKOS, FOAF): QALD JSON plus extension
Language expressivity: English, Italian, French, Spanish.
Growth: Small, based on community feedback.
5 star-data?: no.
# Quality and stability of the dataset - evidence must be provided
The dataset opens future research questions which are beneficial to the community. The dataset seems stable, given its availability via a PID.
There is an open issue at GERBIL QA (https://github.com/dice-group/gerbil/issues/381) for integrating MQALD. Thus, we can assume that the dataset will be available to the community.
The dataset is still relatively small but highly diverse and thus arguable valid to test but not to train a KGQA system.
# Usefulness of the dataset, which should be shown by corresponding third-party uses - evidence must be provided.
The dataset has already proven its usefulness for the KGQA community by evaluating three SOTA systems thoroughly.
# Clarity and completeness of the descriptions.
The paper is well-written, and the description is clear, which enables replication.
# Overall impression and Open Questions
MQALD can become a cornerstone for future research. Its description is helpful and will bring the KGQA community forwards.
# Minor issues
P5,l,25, something is wrong with the Listing numbering
Note, there is now a publication for TeBaQA: https://arxiv.org/abs/2103.06752
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