Teaching Knowledge Graph for Knowledge Graphs Education

I appreciate the effort the authors have invested in addressing the comments from the previous review rounds. The manuscript has improved considerably, particularly through the expanded descriptions of the data collection process, pedagogical alignment, ontology design, and KG construction pipeline, which strengthen both the clarity and technical contribution of the work. Overall, the paper now presents a more coherent and well-balanced contribution that is better aligned with its stated objectives.

The remaining issues are relatively minor and primarily concern clarification and positioning. They don't affect the validity or overall contribution of the work, and I wouldn't recommend another revision cycle. The authors may nevertheless wish to consider the following points before publication:

1- The manuscript presents several contributions, including the educational KG methodology, pedagogical framework, ontology, KG population pipeline, SPARQL queries, validation constraints, populated KG, and user interface. While each component is valuable, the breadth of contributions makes it somewhat unclear what constitutes the primary scientific novelty. I encourage the authors to more explicitly distinguish between the novel research contributions, the reuse of existing methods and technologies, and the engineering or implementation aspects of the system. This would strengthen the paper's positioning and help readers better appreciate its core contribution.

2- A few sections, particularly within the methodology and ontology description, contain overlapping discussions of pedagogical alignment and scalability. Consolidating some of these repeated explanations would improve readability while preserving the technical content.

Third Review for Teaching Knowledge Graph for Knowledge Graphs Education

Previous Review concerns:
- Reviewer Concern: How exactly the system doesn't fail when simple SPARQL queries fail is not mentioned (similarity check etc)
- Comments on revision: Addressed. Fallback behavior when relevant content is not found or a SPARQL query fails returns an empty response structure, while the interface asks the user to provide clearer or different search terms or retry after a query error

- Reviewer Concern: No benchmark datasets are provided in the repo to validate the ontology and query performance.
- Comments on revision: Partially Addressed. revission includes additional validation and technical details, but concerns around the benchmark dataset thats been introduced specifically to evaluate ontology or query performance still remains relevant

- Reviewer Concern: How scalable is the ontology? What happens when hundreds of new courses/knowledge are added that can't conform to the same required field structure?
- Comments on revision: Addressed. Revision discuss mechanisms for ontology evolution, new data sources, incremental regeneration of the KG and the intended use of LOT4KG/OntoRipple for schema changes and IncRML for reducing regeneration costs

- Reviewer Concern: Regarding Table 1, the explanation lacks detail on how this information was presented to the experts and how it guided them in the data collection process. More context would help readers understand its role in guiding expert contributions.
How were the data collected from the Alan Turing Institute group?
- Comments on revision: Addressed. Revision explains that the template was provided through an online collaborative writing document and also explains that the data were collected through the group’s GitHub repository, and was promoted through meet-ups, symposiums, and the mailing list

- Reviewer Concern: Section 3.4. What “diverse formats” were collected? Please give examples. A short overview of the referenced paper [1] here would be helpful, along with a clear signpost to Table 5 for better readability. Line 11: What type of raw data is being referred to? Please specify. ​​The sentence "We implemented a data pipeline to enable..." would benefit from a signpost directing readers to Section 5 for further details.
- Comments on revision: Partially addressed. The manuscript now provides examples of educational resource formats, including PDF and Jupyter notebooks, and gives a brief explanation that CQs come from previous work. However, Section 3.4 still refers rather generally to “diverse formats” and the sentence introducing the data pipeline would still benefit from an explicit signposting

- Reviewer Concern: Section 5 has been significantly shortened, with much of the previously presented technical detail removed. While this change mitigates earlier concerns about reproducibility and unsupported technical claims, it also weakens the paper’s technical contribution. The authors should clarify whether the KG construction pipeline is still intended to be a core contribution of the paper or whether it should be regarded as an implementation detail supporting the conceptual framework and adjust the corresponding claims throughout the manuscript accordingly.
- Comments on revision: Addressed sufficiently. Section 5 contains a detailed construction pipeline, mapping examples, and a dedicated discussion of lifecycle and sustainability.

- Reviewer Concern: Page 18, line 15: What listing? Additionally, the authors should carefully check that all figures are explicitly and correctly referenced in the text.
- Comments on revision: Addressed sufficiently. Revision explicitly introduces and references Listings 1-3 in Section 5.1.1.

Additional review comments:
We kindly suggest that the authors carefully proofread the manuscript to ensure that all references to figures, tables, and sections are accurate and camera-ready. Additionally, please verify for consistent reference formatting (e.g, 57) and that some of the references also look like preprints (e.g., arxiv), even when published version of the same works exists. Hence we also suggest careful updates of references accordingly.