The expansion of image information using ontologies to enhancing the efficiency of data retrieval tasks

1. Overall evaluation
The revised manuscript shows that the authors have made a sincere and
thorough effort to address all the comments raised in the previous review round.
The quality of presentation has been improved, the methodology is clear, and the
paper's structure is more coherent. Notably, the academic writing style, including
the use of the pronoun “we”, has been reviewed and revised by the standards of
the Semantic Web Journal.
2. Strengths of the revision
- A comprehensive revision of the Related Work section, with a clearer
focus on ontology-based semantic and content-based image retrieval.
- Detailed presentation of the ontology design process and structure.
- Well-structured explanation of algorithms, including clear descriptions of
input and output.
- Clear experimental setup and integration of the application.
- Improved academic writing style and consistent use of English throughout
the manuscript.
- Adjustments and refinements made to figures and captions for better
clarity.
3. Minor remaining issues
- Some ontology outputs still appear in Slovak due to limitations from the
source data. However, this is acceptable and has been reasonably
explained.
4. Final recommendation
- I recommend the manuscript be accepted.

The manuscript proposes an ontology-based approach to enrich image metadata and improve retrieval by integrating domain-specific semantic structures and custom-built tooling. While the paper demonstrates practical integration of technologies (e.g., OWL ontologies, Python scraping, GUI), the overall contribution remains moderately original, with more emphasis on engineering implementation than theoretical or methodological innovation.
1) Originality: The paper presents a domain-specific ontology for image annotation and retrieval, along with a scraping tool and user interface. While the integration is competent, the novelty is limited: Ontology-based enrichment and semantic expansion are well-known techniques. Moreover, much of the explanation focuses on tools and file structures, not novel insight.
2)Significance of the Results: The system reports reasonably good retrieval and classification metrics (e.g., 88% extraction, 72% filtering), but these are not benchmarked against baselines or validated statistically. The experimental dataset seems quite modest and not clearly justified.
3) Quality of Writing: The manuscript is readable but verbose, with many low-impact sections (e.g., GUI design, code behaviour). Several figures contain Slovak text, reducing accessibility for an international audience. Ontology design explanations are overly generic and repeat well-known concepts.

The “long-term stable URL for resources” was not accessible at the time of review, the link provided refers to an institutional repository. Hence, without access to the data/code or a README, it is not possible to replicate the results.

The authors applied all comments:

(1) originality: the topic of enriching the image information using ontologies is already presented previously, and the authors mentioned that in the related work section.
But for example, they didn’t mention that there is a previous paper also constructed animal ontology for image retrieval:
https://link.springer.com/article/10.1007/s00530-007-0099-4
So, authors should mention clearly what are the differences between their work, and previously presented works.
*DONE*

(2) significance of the results: good
(3) quality of writing: good

Regarding the “Long-term stable URL for resources”:
(A) and (B) The data files are well organized but don’t contain a README file, which makes it difficult to assess the data, and replicate the experiment. Authors should add README file that includes detailed steps to how to replicate the experiment.
*Unable To Check*

(C) and (D) the authors added an offline copy of all files.

Writing comments for authors:
(1) The authors mentioned “deep learning techniques” in the abstract and introduction and didn’t use any of them through the implementation!!! Where is the section of using these techniques? If not used, authors should remove this word from abstract and introduction.
*DONE*

(2) Refer to your achieved results in the abstract.
*DONE*

(3) Add the reference the first time you mention it. For example protégé (page 2 – line 14), model (page 2 - line 50), and ontology definition (page 3 – line 35) ……..
*DONE*

(4) Explain what the semantic gab is (page 2 - line 39)
*DONE*

(5) meanings and meanings (page 3 - line 40)
*DONE*

(6) Better to rename section 4 to “design of proposed ontology”
*DONE*

(7) Add summary paragraph at the end of related work section to differentiate between proposed method and previous works.
*DONE*

(8) In section 4.3 mention the total number of individuals.
*DONE*