Intensional Conceptualization Model and Its Language for Open Distributed Environments

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The weaknesses that need improvement in this article cover several important aspects. First, the proposed model is still theoretical without validation or case studies. It is recommended to add simulations such as integration with IoT or multi-agent systems), or quantitative experiments to demonstrate the model's effectiveness compared to other approaches. Second, the article does not address the computational complexity of the proposed model. An analysis of algorithmic complexity could strengthen the research contribution. Third, many references used are from before 2015, making them less relevant to current trends. Adding references from published within the last five years, particularly those discussing knowledge representation in AI, the semantic web, and distributed systems, would enhance the literature review. Lastly, the article lacks an analysis of the model's limitations, which could be an important section to clarify the constraints and potential directions for future research development.

Author Response

Dear Reviewer,

I hope this email finds you well. Thank you for your time and the valuable feedback you provided on my manuscript.

Below is a summary of the major revisions made:

1. Updated References: Revised to include more recent and relevant sources, focusing on publications from the past five years.

2. Added Conceptual Model Example (Section 3.2.1): Introduced a university-based scenario to illustrate the structure and elements of the conceptualization model.

3. Enhanced Theoretical Validation (Section 3.4): Included a formal proof and complexity analysis to strengthen the model’s theoretical foundation.

4. New Section on Limitations and Future Work (Section 4): Clearly identifies current limitations and outlines directions for future research and development.

5. Language and Tense Review: Revised the manuscript for clarity, grammar, and consistency, removing unnecessary future tense.

6. Rewritten Introduction: Clarified the paper’s key contributions and relevance to semantic integration in open environments.

Please let me know if any additional revisions are needed.

Best regards,

Khaled Badawy

kkamalmo@uwo.ca

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

In the present paper, authors have introduced a new intensional conceptualization model (ICMOE) designed to address the challenges posed by open distributed environments. This model is presented as an extension and refinement of previous conceptualization theories, particularly those developed by Guarino and Bealer. The authors aim to offer a more flexible, comprehensive framework that can handle undefined entities and relationships in open environments, providing new insights into the dynamic and evolving nature of such settings.

The manuscript is well written, and all provided results seem correct.

Strengths of the manuscript: 

1. The paper makes a significant contribution by expanding the definition of conceptualization to encompass both extensional and intensional aspects, which is crucial for open environments. 

2. The introduction of concept types as a distinct element from actual world entities provides a critical semantic distinction that enhances the expressiveness of the model.

3.The comparison between ICMOE and the models of Guarino and Bealer is a useful for better understanding. This allows the reader to directly see the improvements and differences between the proposed model and prior work. The distinction between intensional relations and intensional concepts  is particularly noteworthy, as it adds another layer of semantics that was previously lacking in existing models.

4. The authors highlight how the model can be adapted to new situations by adding axioms and relations that allow for the inclusion of new conceptualization members. This is crucial for open environments, where changes are continuous, and flexibility is key to handling unanticipated entities and relationships.

 

Weaknesses of the manuscript:

1. The paper does not provide a detailed evaluation or empirical validation of the proposed model. While the theoretical comparison with Guarino and Bealer is useful, a more thorough examination of how the ICMOE performs in actual open environments would strengthen the paper. 

 

Overall, the work is a valuable step toward improving conceptualization frameworks for open environments, and with further refinement and validation, it has the potential to significantly impact the field.

 

Thanks 

Author Response

Dear Reviewer,

I hope this email finds you well. Thank you for your time and the valuable feedback you provided on my manuscript.

Below is a summary of the major revisions made:

1. Updated References: Revised to include more recent and relevant sources, focusing on publications from the past five years.

2. Added Conceptual Model Example (Section 3.2.1): Introduced a university-based scenario to illustrate the structure and elements of the conceptualization model.

3. Enhanced Theoretical Validation (Section 3.4): Included a formal proof and complexity analysis to strengthen the model’s theoretical foundation.

4. New Section on Limitations and Future Work (Section 4): Clearly identifies current limitations and outlines directions for future research and development.

5. Language and Tense Review: Revised the manuscript for clarity, grammar, and consistency, removing unnecessary future tense.

6. Rewritten Introduction: Clarified the paper’s key contributions and relevance to semantic integration in open environments.

Please let me know if any additional revisions are needed.

Best regards,

Khaled Badawy

kkamalmo@uwo.ca

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Overall Evaluation:

This paper theoretically proposes an innovative model that extends and improves upon the limitations of intensional conceptualization in open distributed environments. It also designs a corresponding language model and interpretation function. The theoretical framework is rigorous, with detailed formal descriptions, providing a certain theoretical foundation for subsequent research in semantic integration and knowledge representation.

 

Main Weaknesses:

1.Insufficient practical examples and experimental validation, making it difficult to assess the feasibility and actual effectiveness of the model.

2.The formal descriptions are overly lengthy and partially repetitive, affecting the fluency and comprehension of the paper.

3.Inadequate discussion on the model’s scalability, computational complexity, and compatibility with existing Semantic Web languages.

4.Presence of detail-level errors (e.g., spelling mistake of the Guarino model in the comparison table), which undermines the overall professionalism.

 

Suggestions:

• Practical application cases or experimental validation must be added to demonstrate the actual performance of the model in open environments. If experiments cannot be designed, at the very least, high-quality and complete mathematical proofs should be provided.

• Streamline the formalization section, supplemented with examples and illustrations, to enhance the readability of the paper.

• Discuss in detail the scalability and computational complexity of the model in large-scale open environments, and explore its integration with mainstream Semantic Web technologies.

• Carefully proofread the entire paper to ensure the accuracy of terminology and citations.

Comments on the Quality of English Language

English quality can be improved.

Author Response

Dear Reviewer,

I hope this email finds you well. Thank you for your time and the valuable feedback you provided on my manuscript.

Below is a summary of the major revisions made:

1. Updated References: Revised to include more recent and relevant sources, focusing on publications from the past five years.

2. Added Conceptual Model Example (Section 3.2.1): Introduced a university-based scenario to illustrate the structure and elements of the conceptualization model.

3. Enhanced Theoretical Validation (Section 3.4): Included a formal proof and complexity analysis to strengthen the model’s theoretical foundation.

4. New Section on Limitations and Future Work (Section 4): Clearly identifies current limitations and outlines directions for future research and development.

5. Language and Tense Review: Revised the manuscript for clarity, grammar, and consistency, removing unnecessary future tense.

6. Rewritten Introduction: Clarified the paper’s key contributions and relevance to semantic integration in open environments.

Please let me know if any additional revisions are needed.

Best regards,

Khaled Badawy

kkamalmo@uwo.ca

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The authors present a theoretically motivated framework to address semantic interoperability challenges in open and heterogeneous computing environments. The authors propose the ICMOE model, which is as authors claim is intensional conceptualization framework designed to accommodate dynamic domains, evolving relations, and undefined entities, especially relevant in decentralized systems such as the Internet of Things and distributed agent networks. The main idea of ICMOE is intellectually compelling. It claims to extend previous intensional models by allowing conceptual structures to evolve, rather than remaining fixed as in traditional ontology-based systems. The manuscript aims to contribute to the foundational understanding of conceptualization in open environments. However, despite the strength of the theoretical formulation, the manuscript in its current form has weaknesses. Therefore there is the room of improvement. 

The introduction does not clearly articulate the central goal of the research or what the paper specifically sets out to achieve. The motivation for a new model is implied rather than stated directly, and the reader is left to infer the intended contribution. 

It must be mentioned that, the related work section relies heavily on foundational literature from the 1990s and early 2000s. While this grounding is appropriate for a conceptual paper, the lack of engagement with more recent research significantly limits the relevance of the discussion. The contemporary work must be analyzed.

The formal structure of the ICMOE model is interesting but very abstract. Many of thecomponents, such as concepts, concept types, relations, rules, and interpretation functions, are introduced without illustrative examples. This makes it difficult for readers to grasp how the model would work in practice or how one would use it to represent real-world domains.

Also, the proposed language for ICMOE, including its vocabulary, predicates, and commitments, is described at a purely theoretical level. The paper does not discuss how this language might be implemented, parsed, or used within an actual information system. For readers from applied backgrounds, this makes it difficult to assess the potential utility of the model.

The conclusion reiterates the theoretical value of the ICMOE model but does not offer a critical reflection on its limitations or indicate directions for future research. There is no discussion of how the model might scale in real systems, how its semantics might be computed, or what implementation challenges might arise. A forward-looking conclusion would increase the practical value of the work.

Below are specific suggestions for improving the manuscript:

1. Rewrite the introduction to clearly state the research problem, articulate the need for a new model, and explain what the paper contributes.

2. Update the literature review by including more recent research (especially from the last five years) on dynamic ontologies, semantic integration in distributed systems, and adaptive reasoning.

3. Add concrete, real-world examples for key elements of the model. For instance, when introducing concept types or rules, provide an example from domains such as education, healthcare, or IoT.

4. Improve the explanation of the model’s formal structure. Rather than simply listing components, explain their meaning and purpose in plain language, and show how they work together in a sample scenario.

5. Discuss how the proposed language could be implemented in practice. Would it map to existing semantic web standards or logic programming systems?

6. Include a use case or hypothetical application to demonstrate the value of the model. This could be a small-scale simulation or a conceptual walkthrough of an open environment scenario.

7. Revise the conclusion to include a discussion of limitations and future directions, such as performance considerations, implementation challenges, or plans to validate the model experimentally.

8. Do not use future tense in the paper (In this paper, we will introduce a new intensional conceptualization model that can capture the dynamic nature of an open environment in terms of volatile domains and relations. )

 

Author Response

Dear Reviewer,

I hope this email finds you well. Thank you for your time and the valuable feedback you provided on my manuscript.

Below is a summary of the major revisions made:

1. Updated References: Revised to include more recent and relevant sources, focusing on publications from the past five years.

2. Added Conceptual Model Example (Section 3.2.1): Introduced a university-based scenario to illustrate the structure and elements of the conceptualization model.

3. Enhanced Theoretical Validation (Section 3.4): Included a formal proof and complexity analysis to strengthen the model’s theoretical foundation.

4. New Section on Limitations and Future Work (Section 4): Clearly identifies current limitations and outlines directions for future research and development.

5. Language and Tense Review: Revised the manuscript for clarity, grammar, and consistency, removing unnecessary future tense.

6. Rewritten Introduction: Clarified the paper’s key contributions and relevance to semantic integration in open environments.

Please let me know if any additional revisions are needed.

Best regards,

Khaled Badawy

kkamalmo@uwo.ca

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript is dense with formal definitions, mathematical models, and comparisons but lacks clear evidence of novelty through implementation. Here's a concise critical evaluation:

a. I suggest author add some calculation value in abstract 

b. i suggest author add formal proofs showing properties like consistency, completeness, closure under operations, and stability.

c. Please add computational complexity analysis for your model 

d. Kindly simulate how the model performs under a dynamic setting

e. i suggest author present a graph showing how the size of the domain D, relation set R, and world set W grows over time 

f. Introduce a probabilistic or fuzzy component for handling uncertainty in concept existence or relation validity.

g. Use bar or line charts to visually compare Guarino, Bealer, and ICMOE models along criteria such as extensibility, dynamism, semantics richness, and computational overhead.

h. Analyze how sensitive the model is to small changes in input assumptions or rules U. Present sensitivity plots.

i. Define quantitative metrics (e.g., concept coverage, semantic depth, dynamic adaptability index) and evaluate ICMOE using them.

j. Add a quantitative comparison table showing how ICMOE performs (in terms of metrics from point 10) vs Guarino and Bealer’s model.

k. Include a flowchart or pseudocode

l. Discuss possible logical conflicts or interpretation errors, and show a table of how ICMOE resolves or prevents them.

Author Response

Comments and Suggestions for Authors

The manuscript is dense with formal definitions, mathematical models, and comparisons but lacks clear evidence of novelty through implementation. Here's a concise critical evaluation:

1. I suggest author add some calculation value in abstract   

Thank you for the valuable comment. We have updated the abstract to reflect your suggestion and included a description of the model’s complexity to better highlight its theoretical depth and relevance.

b. i suggest author add formal proofs showing properties like consistency, completeness, closure under operations, and stability. 

Thank you for your feedback. Accordingly, we have updated the manuscript to include an enhanced theoretical validation (Section 3.4). The section on ‘Modelling the Dynamism of Open Environments’ has been revised to incorporate a formal proof and a comprehensive complexity analysis, thereby emphasizing the theoretical soundness of the proposed model.

c. Please add computational complexity analysis for your model  

Yes, thank you for the comment. We have added Enhanced Theoretical Validation (Section 3.4): The section titled “Modelling the Dynamism of Open Environments” has been revised to include a formal proof along with a comprehensive complexity analysis, reinforcing the theoretical soundness of the proposed model.

d. Kindly simulate how the model performs under a dynamic setting  

Thank you for the excellent feedback. This model is intended as a theoretical framework, supported by mathematical proofs for validation. The next step is to implement the model’s language and reasoning system to enable practical simulations. For now, we have included a small IoT example in Section 3.5 (Algorithms) to illustrate how new, unknown sensors can be integrated under the assumption of an open environment.

e. i suggest author present a graph showing how the size of the domain D, relation set R, and world set W grows over time 

Yes, we believe this addition helps demonstrate the model’s scalability. While a comprehensive scalability analysis is beyond the current scope of this work, it can be addressed as part of future research. To support this aspect, we have included a discussion on the model’s complexity.

Additionally, we have added Figure 1 — ‘ICMOE University Example: An illustration of the intensional conceptualization model in a university domain’ — to present the structure and key elements of the model visually.

f. Introduce a probabilistic or fuzzy component for handling uncertainty in concept existence or relation validity. 

Yes, this is a valuable measure. However, the primary goal of this research is to propose an intensional conceptualization model—along with its language and principal components—that addresses the limitations of existing models in supporting open environments. Therefore, we focus on this aspect through mathematical proofs, language commitments, and formal logical definitions. While the inclusion of additional evaluation metrics is indeed beneficial, it is more appropriate for the next research milestone, where the practical implementation of the model and language will be the focus.

g. Use bar or line charts to visually compare Guarino, Bealer, and ICMOE models along criteria such as extensibility, dynamism, semantics richness, and computational overhead. 

Yes, that is an important point. However, the model aims to address the gap posed by the open-environment assumption; therefore, it should be compared to foundational models, such as those by Guarino and Bealer, specifically in the context of this assumption.

h. Analyze how sensitive the model is to small changes in input assumptions or rules U. Present sensitivity plots.

Yes, sensitivity analysis is indeed essential. However, the primary goal of this model is to address the gap in existing models that do not support the open environment assumption. At this stage, sensitivity measures are not the primary focus compared to the open environment aspect. That said, we acknowledge their value, but they are beyond the current scope of this work.

i. Define quantitative metrics (e.g., concept coverage, semantic depth, dynamic adaptability index) and evaluate ICMOE using them.

The goal of this research is to address the gap in existing models regarding the assumption of an open environment. For this reason, the comparative analysis is explicitly focused on how other models handle this aspect. Other dimensions, such as richness and completeness, are essential but are beyond the current scope and may be explored in future work.

j. Add a quantitative comparison table showing how ICMOE performs (in terms of metrics from point 10) vs Guarino and Bealer’s model.

Yes, this can be useful. However, for this research, it is essential to conduct a comparative analysis against other models, specifically about the open-environment assumption and its associated aspects. This will help highlight the distinct contributions and advantages of the proposed model within dynamic and evolving domains.

k. Include a flowchart or pseudocode 

Thank you for the valuable feedback.

Done: We have added Section 3.5, 'Algorithm and Pseudocode,' to illustrate the procedural logic of the model and support its theoretical framework.

l. Discuss possible logical conflicts or interpretation errors, and show a table of how ICMOE resolves or prevents them. 

Great comment — We might need to consider this during the implementation phase. However, at this stage, the focus is on developing a mathematical model defined using set theory and validating it through formal proofs and complexity analysis.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The paper entitled “Intensional Conceptualization Model and its Language for Open Distributed Environments” introduces the Intensional Conceptualization Model for Open Environments (ICMOE). The authors formalize a five-tuple structure 〈D̂, T̂, R̂, Û, Ŵ〉 to capture domains, concept types, relations, rules and possible worlds in dynamic, heterogeneous settings. A companion logical language is defined, together with interpretation functions and compatibility conditions, to represent and reason over evolving entities and relations. The manuscript positions ICMOE as an extension of earlier intensional frameworks (e.g., Guarino’s intensional relational structure and Bealer’s PRP theory) and provides a university scenario to illustrate usage. A comparative table highlights differences between ICMOE and prior models. The stated goal is to offer a more flexible foundation for semantic integration under an open-world assumption.

 

While the topic is timely and the theoretical effort is appreciated, the manuscript requires substantial revision before it can meet rigorous scholarly standards. Significant improvements are needed in the areas of theoretical clarification, completeness of formal definitions, engagement with recent literature, empirical or formal validation, and writing precision.

 

1. Originality and Theoretical Soundness
• The paper asserts that ICMOE uniquely supports open environments, yet the distinctive contribution over recent work is not fully demonstrated. For instance, Ali & McIsaac (2020) and Adhnouss et al. (2023) also propose intensional or hybrid frameworks for dynamic settings. Please articulate concretely what conceptual or technical gap ICMOE fills beyond these efforts (e.g., specific mechanisms for adding/removing relations or concept types, guarantees of consistency, etc.).
• Several assumptions are introduced without proof. If the model is claimed to stay “stable” when new entities appear, provide formal lemmas or theorems (and proofs) showing that consistency, satisfiability, or decidability is preserved.
2. Completeness and Reproducibility of the Formalism
• Relationships between elements—for example, how domain variations D ∈ D̂ interact with possible worlds W ∈ Ŵ—should be formally specified. Clarify whether each world chooses a single domain or a subset of domains.
• The hierarchy among concept types (t) and their constraints with concepts (c) are only informally described. Consider adding axioms for is-a, inheritance, and disjointness to avoid ambiguity.
• The language L is defined, but no fully worked logical formula is given. Please present at least one complete, executable example (e.g., encode “Every professor must hold at least one PhD” in L, show how a reasoner would evaluate it).
• To support reproducibility, outline an algorithmic workflow for constructing an ICMOE instance from real data, and for updating it when new entities arrive.
3. Coverage of Related Work
• Recent advances in knowledge graphs, ontology alignment, and open-world reasoning (2023-2025) deserve discussion. E.g., dynamic ontologies for IoT or federated knowledge graphs address similar integration challenges. Compare ICMOE’s capabilities (e.g., handling unknown classes, incremental reasoning) with these systems.
• The literature review omits some current ontology-based semantic integration toolkits (e.g., OntoAligner, 2025) and open-world machine-learning approaches. Including them will place this work more clearly in the state of the art.
4. Validation and Evaluation
• The paper presently contains only a toy university example. A case study or prototype demonstration on a real heterogeneous dataset (e.g., an IoT deployment) would considerably strengthen the contribution. Show how ICMOE facilitates semantic interoperability when new sensors or services are added, and compare with a baseline closed-world model.
• If empirical validation is not feasible, provide a deeper formal analysis: specify the description-logic fragment (if any) that underlies ICMOE, prove decidability, and analyze worst-case reasoning complexity.
5. Structure, Language, and Terminology
• Terminology consistency: ensure that key terms are defined once and used uniformly (e.g., “concept type,” “relation type”). Explain unfamiliar notions with intuitive examples or analogies.
• Spelling and citations: correct errors such as “Gurino” → “Guarino”, and replace “Unknown Author (2016)” with a proper citation for Bealer’s original work.
• Visual aids: a diagram of the ICMOE architecture and a graphic illustration of the university example would greatly improve clarity.
• Formatting: the PDF header still states “Submitted to Materials”; adapt the template and reference style to the target journal’s guidelines.

Author Response

Comments and Suggestions for Authors

 

The paper entitled “Intensional Conceptualization Model and its Language for Open Distributed Environments” introduces the Intensional Conceptualization Model for Open Environments (ICMOE). The authors formalize a five-tuple structure 〈D̂, T̂, R̂, Û, Ŵ〉 to capture domains, concept types, relations, rules and possible worlds in dynamic, heterogeneous settings. A companion logical language is defined, together with interpretation functions and compatibility conditions, to represent and reason over evolving entities and relations. The manuscript positions ICMOE as an extension of earlier intensional frameworks (e.g., Guarino’s intensional relational structure and Bealer’s PRP theory) and provides a university scenario to illustrate usage. A comparative table highlights differences between ICMOE and prior models. The stated goal is to offer a more flexible foundation for semantic integration under an open-world assumption.

 

While the topic is timely and the theoretical effort is appreciated, the manuscript requires substantial revision before it can meet rigorous scholarly standards. Significant improvements are needed in the areas of theoretical clarification, completeness of formal definitions, engagement with recent literature, empirical or formal validation, and writing precision.

 

1. Originality and Theoretical Soundness
• The paper asserts that ICMOE uniquely supports open environments, yet the distinctive contribution over recent work is not fully demonstrated. For instance, Ali & McIsaac (2020) and Adhnouss et al. (2023) also propose intensional or hybrid frameworks for dynamic settings. Please articulate concretely what conceptual or technical gap ICMOE fills beyond these efforts (e.g., specific mechanisms for adding/removing relations or concept types, guarantees of consistency, etc.).
•  Yes, this is important, so we have updated this part with the gap they have. Please see below:
• “A hybrid model~\cite{adhnouss2023hybrid} integrates both intensional and extensional conceptualization methods. It employs epistemic logic to manage knowledge and belief systems within multi-agent environments, where different entities may express similar semantics in diverse ways. This approach facilitates more flexible communication and collaboration among agents by capturing both the intensional meaning (conceptual understanding) and the extensional representation (practical application). However, the model operates solely under the \textit{possible worlds assumption} and lacks support for the \textit{open environment assumption}”.
• Several assumptions are introduced without proof. If the model is claimed to stay “stable” when new entities appear, provide formal lemmas or theorems (and proofs) showing that consistency, satisfiability, or decidability is preserved. 

• Yes, we agree, so we have added the following:
• Theoretical Validation (Section 3.4): The section on “Modeling the Dynamism of Open Environments” has been revised to include a formal proof and a comprehensive complexity analysis, emphasizing the theoretical soundness of the model.

 

2. Completeness and Reproducibility of the Formalism

• Relationships between elements—for example, how domain variations D ∈ D̂ interact with possible worlds W ∈ Ŵ—should be formally specified. Clarify whether each world chooses a single domain or a subset of domains. 

 

• Yes, thank you for your valuable feedback. We have added the following:
• “Let us define an example to illustrate the structure of a concept, the set of possible domains \(\hat{D}\), and the set of possible worlds \(\hat{W}\) under the \textit{open environment assumption}.
• Consider a new concept \(c_{\text{new}}\) that introduces a new domain \(D_{\text{new}} \in \hat{D}\). Correspondingly, a new possible world \(W_{\text{new}} \in \hat{W}\) would exist to represent the semantics and behaviour associated with the new domain \(D_{\text{new}}\).
• This illustrates how, under the open environment assumption, new concepts can dynamically expand the space of domains and possible worlds, enabling more adaptable and extensible semantic models.”

 

• The hierarchy among concept types (t) and their constraints with concepts (c) are only informally described. Consider adding axioms for is-a, inheritance, and disjointness to avoid ambiguity.

 

• Yes, thank you — We agree. To avoid ambiguity regarding is-a, inheritance, and disjointness, we have added the following clarifications. These updates are also reflected in the difference file for your reference.

“item \textbf{$A$ (Atomic Concept Type)}: This is the set of concept types that can only be interpreted as concrete or leaf concepts in the type hierarchy. We propose this element to identify terminal types in the hierarchy of concept types. Formally, \(A \subseteq T\), and any concept \(c\) of type \(A\) satisfies \(c \sqsubseteq A\). If we have a concept instance \(c_x \in A_x\), this implies \(c_x \sqsubseteq A_x\).

\item \textbf{$N$ (Non-Atomic Concept Type)}: This is the set of concept types that can be interpreted as other concept types within the domain. We propose this type to represent universal or intermediate types in the concept type hierarchy. Formally, \(N \subseteq T\), and \(A \sqsubseteq N\). If we have a type \(A_x \in N_x\), this implies \(A_x \sqsubseteq N_x\).”

• The language L is defined, but no fully worked logical formula is given. Please present at least one complete, executable example (e.g., encode “Every professor must hold at least one PhD” in L, show how a reasoner would evaluate it).

 

• Thank you for this feedback. Yes, to clarify this point, we have added an example that illustrates the language elements in Section 3.3.4: Language Interpretation Function.

 

• To support reproducibility, outline an algorithmic workflow for constructing an ICMOE instance from real data and for updating it when new entities arrive.

 

• Yes, you are right. To address this, we have added Section 3.5: Algorithm and Pseudocode to provide a more explicit procedural representation of the model.

3. Coverage of Related Work

• Recent advances in knowledge graphs, ontology alignment, and open-world reasoning (2023-2025) deserve discussion. E.g., dynamic ontologies for IoT or federated knowledge graphs address similar integration challenges. Compare ICMOE’s capabilities (e.g., handling unknown classes, incremental reasoning) with these systems.

 

• This is a very insightful observation. However, the model is intended to serve as a theoretical framework, supported by mathematical proofs for validation. Therefore, it should be compared to similar conceptual theories rather than applied models.

 

• The literature review omits some current ontology-based semantic integration toolkits (e.g., OntoAligner, 2025) and open-world machine-learning approaches. Including them will place this work more clearly in the state of the art.

 

• This is a very insightful observation. However, the model is intended to serve as a theoretical framework, supported by mathematical proofs for validation. Therefore, it should be compared to similar conceptual theories rather than applied models.

4. Validation and Evaluation

• The paper presently contains only a toy university example. A case study or prototype demonstration on a real heterogeneous dataset (e.g., an IoT deployment) would considerably strengthen the contribution. Show how ICMOE facilitates semantic interoperability when new sensors or services are added, and compare with a baseline closed-world model.,

 

• Thank you for the excellent feedback. This model is intended to serve as a theoretical framework, supported by mathematical proofs for validation. The next milestone for this research is to implement the model’s language and reasoning system, enabling evaluation with real datasets. However, to provide some practical insight at this stage, we have included a small example in Section 3.5 (Algorithms) to illustrate how new, unknown sensors can be integrated under the assumption of an open environment.

 

• If empirical validation is not feasible, provide a deeper formal analysis: specify the description-logic fragment (if any) that underlies ICMOE, prove decidability, and analyze worst-case reasoning complexity. 

 

• This exists in the 3.41 proof and 3.4.2 complexity analysis

5. Structure, Language, and Terminology

• Terminology consistency: ensure that key terms are defined once and used uniformly (e.g., “concept type,” “relation type”). Explain unfamiliar notions with intuitive examples or analogies.

 

• Yes please find this in the following sections:
• The ICMOE Main Elements Definitions,  Conceptual Model Example, Figure 1. ICMOE University Example

 

• Spelling and citations: correct errors such as “Gurino” → “Guarino”, Fixed 
• and replace “Unknown Author (2016)” with a proper citation for Bealer’s original work.  Fixed  
• Visual aids: a diagram of the ICMOE architecture and a graphic illustration of the university example would greatly improve clarity.

 

• Figure 1 added “ICMOE University Example: An illustration of the intensional conceptualization model in a university domain”

• Formatting: the PDF header still states “Submitted to Materials”; adapt the template and reference style to the target journal’s guidelines.
• Fixed to Imdbi Template 

• We have included a PDF file with the difference for your convenience. 

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The authorss seem to make some changes according my comments, but I can not track all of them, please upload the new version of the paper where you colorize changes in red.

Author Response

Comments and Suggestions for Authors

 

The authorss seem to make some changes according my comments, but I can not track all of them, please upload the new version of the paper where you colorize changes in red.

We have included a PDF file with the difference for your convenience. Additionally, please find below my responses to the first round of feedback, with references to the specific sections in the manuscript where changes have been made.

1. Rewrite the introduction to clearly state the research problem, articulate the need for a new model, and explain what the paper contributes.

          Thank you, we have added the following :

Rewritten Introduction: The introduction has been rewritten for greater clarity, with an improved explanation of the paper’s key contributions and relevance to open environment semantic integration.

 

2. Update the literature review by including more recent research (especially from the last five years) on dynamic ontologies, semantic integration in distributed systems, and adaptive reasoning.

Thank you, we have added the following: Updated References: All references have been reviewed and updated with the most recent publications, focusing on sources from the last five years where applicable,  to ensure relevance and currency.

3. Add concrete, real-world examples for key elements of the model. For instance, when introducing concept types or rules, provide an example from domains such as education, healthcare, or IoT.

Thank you so much, we have added the following:

• • Added Conceptual Model Example (Section 3.2.1): A real-world scenario—centred around a university environment—has been introduced to concretely illustrate the components and structure of the proposed conceptualization model.
  • Figure 1 added “ICMOE University Example: An illustration of the intensional conceptualization model in a university domain”
  • We have added a small example in Section 3.5 (Algorithms) to illustrate how new, unknown sensors can be integrated under the assumption of an open environment.

 

4. Improve the explanation of the model’s formal structure. Rather than simply listing components, explain their meaning and purpose in plain language, and show how they work together in a sample scenario.

Thank you so much, we have added the following:

• • Added Conceptual Model Example (Section 3.2.1): A real-world scenario—centred around a university environment—has been introduced to concretely illustrate the components and structure of the proposed conceptualization model.
  • Figure 1 added “ICMOE University Example: An illustration of the intensional conceptualization model in a university domain”
  • We have added a small example in Section 3.5 (Algorithms) to illustrate how new, unknown sensors can be integrated under the assumption of an open environment.

 

5. Discuss how the proposed language could be implemented in practice. Would it map to existing semantic web standards or logic programming systems?

Thank you for this feedback. Yes, to clarify this point, we have added an example that illustrates the language elements in Section 3.3.4: Language Interpretation Function.

 

6. Include a use case or hypothetical application to demonstrate the value of the model. This could be a small-scale simulation or a conceptual walkthrough of an open environment scenario.

Thank you so much, we have added the following:

• • We have added Conceptual Model Example (Section 3.2.1): A real-world scenario—centred around a university environment—has been introduced to concretely illustrate the components and structure of the proposed conceptualization model.
  • We have added Figure 1, “ICMOE University Example: An illustration of the intensional conceptualization model in a university domain.”
  • We have added a small example in Section 3.5 (Algorithms) to illustrate how new, unknown sensors can be integrated under the assumption of an open environment.

 

7. Revise the conclusion to include a discussion of limitations and future directions, such as performance considerations, implementation challenges, or plans to validate the model experimentally.

We have added Limitations and Future Work (Section 4): A dedicated section has been added to address the model’s current limitations transparently and to outline clear directions for future development and implementation.

 

8. Do not use future tense in the paper (In this paper, we will introduce a new intensional conceptualization model that can capture the dynamic nature of an open environment in terms of volatile domains and relations. )

We have done a language and Tense Review: The paper’s English has been thoroughly reviewed. All future tense usage has been revised to maintain consistency in academic tone.

Author Response File: Author Response.pdf

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

Some issue still not solve

a. I suggest author add some calculation value in abstract   

b. i suggest author present a graph showing how the size of the domain D, relation set R, and world set W grows over time 

c. Use bar or line charts to visually compare Guarino, Bealer, and ICMOE models along criteria such as extensibility, dynamism, semantics richness, and computational overhead

d.  Define quantitative metrics (e.g., concept coverage, semantic depth, dynamic adaptability index) and evaluate ICMOE using them.

e. Add a quantitative comparison table showing how ICMOE performs (in terms of metrics from point 10) vs Guarino and Bealer’s model.

Author Response

a. I suggest author add some calculation value in abstract

Thank you for the feedback. In the previous review round, we aimed to address this point by incorporating the model’s complexity into the abstract to highlight its theoretical depth and relevance better. In this revision, we have further updated the abstract to include the exact quantitative evaluation measures used to assess ICMOE’s performance.

b. i suggest author present a graph showing how the size of the domain D, relation set R, and world set W grows over time 

Thank you for the suggestion. I have incorporated this analysis into Section 4.3, Growth Dynamics of Domain, Relations, and Worlds, as part of the expanded evaluation.

c. Use bar or line charts to visually compare Guarino, Bealer, and ICMOE models along criteria such as extensibility, dynamism, semantics richness, and computational overhead

Thank you for the suggestion. I have included this analysis in Section 4.2, Comparative Evaluation of Semantic Models, to enhance the comparative assessment.

d.  Define quantitative metrics (e.g., concept coverage, semantic depth, dynamic adaptability index) and evaluate ICMOE using them.

Thank you for the valuable suggestion. I have incorporated the requested analysis into Section 4.4, Quantitative Evaluation Metrics for ICMOE, including the following subsections: 4.4.1 Defined Evaluation Metrics, 4.4.2 Empirical Assessment of ICMOE, and 4.4.3 Discussion.

e. Add a quantitative comparison table showing how ICMOE performs (in terms of metrics from point 10) vs Guarino and Bealer’s model.

Thank you for the insightful suggestion. I have added the requested analysis to Section 4.4.4, Quantitative Comparison with Existing Models, to enhance the comparative evaluation of ICMOE.

Author Response File: Author Response.pdf