Thank authors for detailed answers to the previous comments. Also, references of systems engineering, software engineering, ontology, etc. help the reviewer to see a wider area of interested and related.

Reply: We appreciate your time and valuable feedback, which have helped to improve the quality of our paper. We are glad that our references to various disciplines have helped you to see a wider area of interest and relatedness to our work. Please review our response to your latest comments below. Any further comments and suggestions are very welcome.

1) A definition of the terms is needed for [trustworthiness and credibility]. If I follow the definition in https://marksinthesand.com/2014/05/27/credible-vs-trustworthy/ -‘Trustworthiness’ is an objective fact: whereas ‘Credibility’ is subjective. If this definition is correct, authors cannot use both words for their own purposes.

Reply: Thank you for the great question. We have adopted their definitions from ISO and ASME standards.

credibility: degree to which model or data has attributes that are regarded as true and believable by users in a specific context of use. Note 1 to entry: Credibility includes the concept of authenticity (the truthfulness of origins, attributions, commitments) and trust in the predictive capability of a digital twin entity. [SOURCE: ISO/IEC 25012:2008, 5.3.1.4, modified – The article “The” has been deleted; “data” has been replaced with “model or data”. ASME V&V 40-2018, 5, definition of model credibility has been added to note 1 to entry without the word “computational” and the phrase “for the context of use”, “model” has been replaced with “digital twin entity”.]

trustworthiness: ability to meet stakeholder expectations in a verifiable way. [SOURCE: ISO/IEC 22989:2022, 3.5.16] (The three notes to entry are also adopted).

These definitions are well recognized in AI and IoT domains and Modeling and Simulation domains respectively. Therefore, you can tell the interpretation and elaboration differences for the two concepts between linguistic perspective and standardization perspective. Trustworthiness focuses on transparency, accountability, and verifiability in system behavior, e.g., digital twin system; while credibility focuses on accuracy and believability of model or data in specific contexts of use, e.g., digital twin entity.

2) Authors used the word “out-of-scope” several times during your answer to my previous comment. Then, what is in-scope?

Reply: Yes, I mentioned "out of the scope of this paper" twice in my previous response. (1) “...... except for the three concepts: digital twin entity, digital twin system. and digital twin, we decide to exclude the term definitions out of the scope of this paper.” (2) “In this way, we can relate "things in IoT" to "Entity". However, I think this is out of the scope of this paper.”

Let me clarify them. First, the scope of the paper is not equal to the scope of the project, the digital twin concept system. The two instances of "out of the scope of this paper" was trying to define the scope of the paper from temporal and spatial perspectives respectively. From the project management processes perspective, requirement analysis, methodology development, architecture design of the digital twin concept system are included in the scope of the paper, the detail design of the digital twin concept system are excluded from the scope of the paper.

From the deliverables structure of the digital twin concept system perspective, four of five parts have corresponding subchapters; the fifth part, sister concepts of digital twin system, does not have its subchapter. Most contents of this part are excluded from the scope of the paper.

3) The paper gets more complex than the previous version instead of clarifying the complex problem of the issue. Maybe the authors’ view is still in the divergent stage rather than the convergent stage to the problem issue.

Reply: I am sorry you got this feel from the second version. As I mentioned in the amendments from version 1, The changes made in v2 includes three parts: reorganization of the paper, efforts of improving the readability of figure 4, and enrichment of figure 4. In version 1, some contents of the enhanced methodology are mixed inside the Result chapter. To provide a well-structured organization, all methodology related contents are moved into the Methods chapter. Three new local views (figure 6, 7, an 8) are added to simplify the complexity of figure 4. I believe the structure and visual presentation of new version is clearer and easier to understand than that of version 1. I would be happy to discuss any specific complex concerns you have with the revised version.

As for the problem issue, or the initial problem this paper addressed, v2 remains same with v1, i.e., to resolve the ambiguities due to the polysemy phenomenon of the term "digital twin”, especially the ambiguities in spatial perspective. As for the ambiguity of “digital twin” in temporal perspective, I don’t have a solution yet. Therefore, in my response to Dr. Grieves’ review report, I said, let us leave it for future industry practice and standardization work.

4) To implement SW, a consistent and complete set of definitions and a logic system is needed. Definitions for the parts corresponding to ‘out-of-scope’ should be borrowed from outside for this purpose. Since various definitions were presented without consistency with your own choice, because they are ‘out-of-scope’, it is judged that this paper did not contribute to simplify the complex problem and made the complex problem more complicated.

Reply: Thank you for your feedback. First of all, the purpose of this paper is neither to implement software nor to propose a brand-new standard on digital twin terminology. Secondly, I agree that a consistent and complete set of definitions and a logic system is needed. That is why we initiate this project and develop the digital twin concept system. That means the consistent and complete set of definitions are outcome of the logic digital twin concept system; the development of the set of definitions should follow the principles and procedures of the development of the concept system. The logic concept system comes first, followed by the consistent and complete set of definitions.

You mention that, definitions for the parts corresponding to ‘out-of-scope’ should be borrowed from outside for this purpose. However, borrowing definitions from current standards can be found all over the digital twin concept system, not limited to the fifth “out-of-scope” part. The last sentence of the first paragraph of the Method chapter says, “from the concept system development process to the determination of specific concept definitions, existing international standards are quoted as much as possible”.

You mention that, since various definitions were presented without consistency with your own choice, because they are ‘out-of-scope’. However, the argument is unsound because its evidence and conclusion are not true. As I mentioned in above reply to comment 2), the two "out of the scope of this paper" decisions have nothing to do with the so-called "inconsistency” you mentioned. As I mentioned in previous response and my replies to comment 6) and 8) below, the available and proposed definitions of “entity” and “system” are equivalent each other respectively. They are all specific views (ISO/IEC/IEEE 42010:2022 3.7) from different viewpoints (ISO/IEC/IEEE 42010:2022 3.8) of “entity” and “system” respectively. They are all correct. We just select or propose the optimum one according to the formula “intensional definition = superordinate concept + delimiting characteristic(s)" by ISO 1087 and ISO 704. That is what we mean the consistency between the position of a concept in the DTw concept system and its definition, and that is why we list "treating both a concept system and the definition of a given concept as a system" as one of system thinking viewpoints and systems engineering methods in the subchapter "the enhanced methodology of developing a concept system".

Seeing that the argument of the “since ......” is flawed, the following judgement, “this paper did not contribute to simplify the complex problem and made the complex problem more complicated”, is not reasonable and cannot hold. At first, the problem this paper addressed is simple, i.e., the ambiguity of “digital twin” in spatial perspective. Secondly, the solution is also straightforward, i.e., the differentiation between digital twin entity and digital twin system. The solution is so simple that ISO 23247-2 already did half of it by using the term DTW entity and IoT (ISO/IEC 30141 ed2) and AI (ISO/IEC 22989: 2022) already adopted similar solutions. The contributions of this paper are to make the solution rationalized and justified, expandable and sustainable.

Rationalized. The simple solution of the differentiation between digital twin entity and digital twin system from the term “digital twin” implements the mononymy and monosemy principle between concept and term (ISO 704:2022, 7.7.1) and thus resolve the ambiguity of “digital twin” polysemy in spatial perspective.

Reply: Thank you very much for you suggestion. Previously, we excluded most definitions of 50 concepts outside the paper because they belong to the detail design of our project and the purpose of this paper is not to propose a brand-new standard on digital twin terminology. We already have all the definitions in Chinese and most of them have been translated into English or copied from their English sources. I'm uncertain whether it's appropriate to include an annex with 50 terms, along with their definitions, sources, and notes to entry in a journal paper. I will confirm this issue with Digital Twin editorial team. If it is acceptable, I will include these definitions in the next revision of this paper.

6) [the term Entity and System, i.e. it is not necessary to apply the method and tools in Fig 2 to the coined term "Target Entity".] Discussing the [Target Entity] without definition of the [Entity] is 砂上樓閣(house of cards).

Reply: Thank you for the comment. As you can see in my reply to comment 8 below, I can answer the question - What kinds of ‘Entity’ can be and could be digitally twinned, without referring to the definition of “Entity”. As I mentioned in previous response and my reply to comment 8) below, three available definitions of entity and the proposed definition are equivalent each other. The discussion and decision making on the definition of entity have no influence on the scope of the target entity. I believe that there are two key points here.

The architecture design of the digital twin concept system has great influence on the definitions of the concepts inside the concept system. For example, when we design or develop a concept system in forward way, according to ISO 1087 and ISO 704 mentioned in the second paragraph of the Methods chapter, the position of a concept in the concept system really matters to determine its definitions, not vice versa.

Reply: Do you mean figure 2 and 4 in version 2? Yes, you are right. It is the only difference between the DTw Framework in ISO 23247 and the DTw System in this paper. As I mentioned in the previous response, a DTw system is kind of bi-system. I agree that a DTw system must include the ‘physical entity’, or the digital twinning target. On the far left of figure 4 in version 2, there is long solid thin line to show “Target Entity” is one of the components of “Digital Twin System”.

8) [Target Entity] What kinds of ‘Entity’ can be and could be digitally twinned? I see [Physical entity = Target Entity]. This is because all non-physical entities are viewed as DTw. Also, if looking at, [entity refer to thing (physical or non-physical) having a distinct existence], ‘entity’ and ‘thing’ seem to be in apposition.

Reply: Thank you for the great question. We share a similar perspective with ISO/IEC 30173 on the scope of digital twinning target, i.e., target entity is beyond physical entity. As I mentioned in the previous response and version 2, a target entity can be a physical entity, a virtual entity, or a physical-virtual hybrid entity. For example, a CPS or a IoT system which is a physical-virtual hybrid entity, can be a target entity of a DTw system. I guess, in the future, human dream can be another possible target entity of a DTw system. That means, people can partially simulate or even manipulate another one’s dream via brain computer interface (BTW, we include BCI as one of sister concepts of DTw system in version 2). People may think that dream is still a kind of physical entity based on biochemical reactions of the nervous system. However, because of its subjective content, my personal point of view, dream is kind of virtual entity, neither physical entity nor digital entity. Therefore, from standardization perspective, I prefer not to limit the scope of target entity too much.

Let me clarify your statement, “all non-physical entities are viewed as DTw”. Either DTw entity or DTw system, whatever you refer to with the word “DTw”, it does have its own boundary. There are huge amounts of non-physical entities outside the boundary. Seeing that the application of DTw technologies is still in its infant stage at present, vast majority non-physical entities are not DTw entities or doesn’t belong to any DTw systems.

As for the relationship between entity and thing, from ISO/IEC 20924:2021 perspective, they are in apposition. The case of “entity” is similar to the multiple definitions of “system”. It is my view that the three definitions of entity from ISO/IEC 20924, ISO 9000, and WordNet are equivalent each other. They are candidates or input of the definition of the root node concept of the top-level ontology of the digital twin concept system. Besides the term “entity” and “thing”, object, item, anything, and everything are all candidate term inside the synset of the root node concept. It is also my view that “entity” is the most appropriate designation (or preferred term according to ISO 1087) of the synset.  

9) The reviewer agrees to [antonym of "physical" is "virtual" digital entity" is a subset of "virtual entity"]

Reply: Thank you for your recognition. I am glad that we share the same opinion on this point.

10) Fig.1 - [Vertical axis: causality or means-ends relationship] is strange to me. ‘Relationship’ usually represented by a network rather than a coordinate axis of increasing value.

Reply: Thank you for the great question. The purpose of figure 1 is not to represent any relationships, but to organize the classification perspectives of a taxonomy with a framework. We include the concept “framework” into the DTw concept system in version 2, not shown in figure 4 but shown in figure 5. We adopted the definition of framework (logical structure for classifying and organizing complex information) from ISO/IEC 19763-1:2015 4.1.4. In the subchapter “the enhanced methodology of developing a concept system”, it is mentioned that, temporal relationship, spatial relationship, means-ends/cause-effect relationship are the three basic relationships in the world. Therefore, we use the three dimensional framework to organize the classification perspectives of a taxonomy. BTW, the framework is not a strictly defined coordinate system, and the three dimensions are not strictly defined coordinate axes (of increasing value) either, because there is no arrow for each dimension, each dimension has only a finite number of values, and the set of values of each dimension has no strictly logical sequence.

11) Fig.2 - The [user entity] is a mixture of human user, human interface, applications, other DTws. It can be an outside world of the DTw system where the core of the DTw system can be excluding the [user entity]. Also, the ‘cross entity system’ at the right-side is not well explained.

Reply: Thank you very much for the comment. Yes, you are right. From functional point of view, user entities are not components of a DTw system in most cases. We will modify the blue box with dash line or remove the blue box in figure 2 in the next revision of this paper.

As for the cross-system entity, the purpose of figure 2 is just to show an effort trying to solve the spatial ambiguity of “digital twin”. “The newly published ISO 23247 took the first step by adopting the concept ‘digital twin entity’, as shown in figure 2”. The diagram is from ISO 23247-2. The explanation and interpretation of ISO 23247-2 is out of the scope of this paper.

12) Fig.3 - There are multiple boxes which have same titles; two boxes of ‘representation’, three boxes starting with ‘model’, two boxes starting with ‘simulation’, two boxes starting with ‘twin’. They are confusing to readers of the paper.

Reply: Thank you for the comment. As mentioned in the response to another reviewer’s report, figure 3 is just a visualization of the semantic relationships among twin-related words from linguistic perspective; all the definitions and relationships in figure 3 are copied from WordNet of Princeton University. There are ten synsets of “representation”, nine synsets of “model”, and four synsets of “twin” in WordNet. We selected those synsets whose definitions are related to the topic of this paper by excluding the definitions from humanity and social science. The steps of identifying the most appropriate synsets are mentioned in two subchapters, “the tools and procedures for evaluating and selecting proper superordinate concept” and “the analysis of the superordinate concepts of digital twin definitions”.

13) Fig.4 - If Fig.2 is a simplified version of Fig.4, please show correspondence by using such as dotted outline or coloring.

Reply: Thank you for the comment. Figure 2 is not a simplified version of figure 4. Figure 7 is the simplified view of figure 4 from the DTw system perspective. Figure 2 is kind of a functional view of DTw system; however, neither figure 2 nor ISO 23247 adopts the concept “digital twin system”.

14) ["digital twinning target" (a seven-character phrase in Chinese), and to adopt the new term "Target Entity" (a four-character phrase in Chinese)]. I know that each character of Chinese language has a meaning. Listing the four and seven constitutive meanings will help readers understand the difference, and writing Chinese characters together will also help.

Reply: Thank you very much for you suggestion. Besides digital twinning target and target entity, a admitted term and the preferred term of the concept in the DTw concept system, we have another concept, digital twinning, and its definition in the DTw concept system. We will add a new paragraph to describe the two concepts and three terms in the next revision of this paper. We agree to adopt “target entity” as the preferred term of the concept just because its higher communication efficiency (four characters vs, seven characters in Chinese and three words vs. two words in English).

15) Please provide a line number of the text for the next review or comment.

Reply: Thank you very much for you suggestion. I need to confirm this issue with Digital Twin editorial team.

I have been over this paper a dozen times. What I can say is that it is one of many Digital Twin frameworks. I find it to be a bit arcane and overly complex (See Figure 3 for example). I don’t know what the “eight four concepts and fifty-eight definitions” do for either understanding or operationalizing digital twins.  

Reply: This paper is not about a framework or a reference architecture of digital twin systems. A concept system is about a set of concepts and their relationships and definitions. Figure 3 is the outcome visualization of the development of the digital twin concept system which includes 84 concepts and 48 definitions. As shown in Figure 8 (a) and (b), a concept system is the first step of standardization of a new technology domain. The standardization methodology and process from concept system to reference architecture has been described in ISO/IEC 30141:2018 [64]. The methodology and process of developing a concept system has been applied to several domains, such as systems engineering (ISO/IEC JTC1/SC7), health informatics (ISO/TC215), traditional Chinese medicine (ISO/TC249), nuclear energy (ISO/TC85), top-level ontologies and metadata registries (ISO/IEC JTC1/SC32/WG2), IT for learning, education and training (ISO/IEC JTC1/SC36), geographic information (ISO/TC211 and [66]), and Intelligent transport systems (ISO/TC204), as mentioned in the response to another reviewer.  

BTW, we adopt the definition of framework and architecture from following standards: ISO/IEC 19763-1:2015 Information technology — Metamodel framework for interoperability (MFI) — Part 1: Framework, 4.1.4 framework: logical structure for classifying and organizing complex information. ISO/IEC/IEEE 42020:2019 Software, systems and enterprise — Architecture processes, 3.3 architecture: fundamental concepts or properties of an entity in its environment and governing principles for the realization and evolution of this entity and its related life cycle processes.  

While I am all for standards, it may be that we are too early for the attempt at a comprehensive standard of Digital Twins. I would contend that DT standards at this stage need an incremental approach. We don’t actually know yet what works and what doesn’t work. As an aside, I dislike that the standards need to be purchased. Standards should be in the public domain.  

Reply: The interaction between standardization and industrial practice is an interesting topic. First of all, standardization is not an obstacle to innovation or industrial practice. Technology evolution has its own natural characteristics, such as the hype cycle by Gartner or S-curve. After a dozen years of initial application exploration, the digital twin technology system is sliding into the trough of the hype cycle (please see the diagram here).  

Although the standardization of a new technology also has its own pace typically with the sequence from basic (such as concepts and terminology, reference architecture, etc.) to advanced (maturity model, process specification and assessment, application guidance, etc.), from a specific area then to general standards. You are right. Standardization needs an incremental approach. Effective and efficient interactions between standardization and industrial practice are crucial to the success of technology innovations. Standardization is not only a summary of industrial practice outcomes and consensus, but also an enabler for new technology R&D and applications.

For example, the first international standard in digital twin area, ISO 23247 (digital twin framework for manufacturing), which currently has four parts, was published in October 2021 after nearly four years development. Stakeholders from national bodies are working on the standard draft of digital twin concepts and terminology and reference architecture of digital twin systems. We think industry and academia currently have enough basic consensus on what is digital twin or what should be regarded as a digital twin system and what should not. It was right time to initiate these projects years ago and industry are looking forward to their deliverables to be published asap.  

BTW, some online official standard stores (such as ISO, IEC and some national bodies) offer preview pdf version of a standard which at least covers its contents and scope. ISO website also offers a free preview access to more than 99% ISO standards extending to the terms and definitions (typically it is clause/chapter 3 or 4 of a standard). This is an extremely useful resource for standardization engineers and for us writing this paper.  

Others might find the article relevant for their work, so readers can determine for themselves if they find the article or parts of the article useful. As a result, I am categorizing the article as “Approved with reservations”. That said, I’ll give some specific comments on issues I have.

1. The paper claims there are “more than a dozen definitions of digital twins.” Doing a Google search of definition has this estimate off by three or maybe even four or five magnitudes. This is problematic for the rest of the paper that relies on a limited number of definitions as its basis.  

Reply: Thank you for the comment and correction. In the reply to another reviewer, I used the phrase “dozens of” instead of “a dozen of”, “In addition to the dozens of digital twin definitions in review articles mentioned in the introduction chapter and table 2 are examined ……”. We will reword the statement with “more than dozens of definitions of digital twin” in the next revision of this paper.  

As for the number of the definitions of digital twin, the Google and Google Scholar searching results (255 million and 37 thousand respectively, please see here and here) with the expression (“digital twin” AND definition) don’t mean that there are 255 million or 37 thousand different definitions of digital twin.   

All the review or survey papers about digital twin just listed and analyzed dozens of definitions when the definition of digital twin is one of their topics. For example, 29 definitions were analyzed in reference [1]. Even if it is true that the number of digital twin definitions is more than 1,000 or even one million, only top 20 or 30 definitions from standardization organizations, high citation index authors, famous research institutes and companies will be examined and analyzed thoroughly, the vast majority of the rest will be ignored. This is an effect of power-law distributions of the influence and popularity of the definitions, i.e., a superhit effect of word of mouth.  

We don't deny the value of the long tail effect of a few niche definitions. We believe that most of those niche definitions will fall into a well-recognized definition's customization into some specific areas. Some other niche definitions will try to challenge the superhit definitions by addressing their potential problems or dilemmas. The later one is exactly the position of this paper.  

2. A criterion of a research article is “are sufficient details of the method and analysis provided to allow replication by others? There is a “procedure of developing the concept map as shown as follows:” It undoubtedly will produce some sort of concept map. However, it’s highly unlikely it will produce THE concept map of this paper. There is just too much ambiguity in this process. If someone is able to reproduce the concept map, they can make that known. Reproducibility is a key requirement for research.  

Reply: Figure 2 is just a visualization of the semantic relationships among twin-related words from linguistic perspective. All the definitions and relationships are copied from WordNet of Princeton University. The notation of UML class diagram used in figure 2 is clear to everybody. Figure 2 does not invent anything new, just a combination of the content (WordNet) and the form (UML). We think it is highly unlikely someone will produce a concept map totally different from figure 2 according to the procedures. We do appreciate if you can provide the detail of any ambiguity.

As mentioned in the response to another reviewer, the purpose of figure 2 is to analyze the semantic relationship among all candidate superordinate concepts of an important or confusing or brand new concept or term. The outcome of figure 2 just addresses a single node in the digital twin concept system. The benefit of figure 2 (semantic relationship analysis of superordinate concepts for an individual term) is to facilitate the term definition work by building connections between linguistic perspective (the term) and business perspective (the concept).  

3. The same criticism applies to the procedure of developing a taxonomy.  

Reply: Thank you for the comment. We will correct one typo in step 4 of the procedure in the next revision of this paper. One more note and criteria will be added to step 5 as well: “The three-dimensional taxonomy framework can provide initial orthogonality of the classification dimensions. Keep in mind to check the orthogonality of the newly added dimension to existing ones.”  

Comparing with the copy-paste work of developing the concept map of figure 2, developing a taxonomy with consensus involves more creative system thinking and teamwork. The purpose of the taxonomy framework and the taxonomy development procedure is to improve the quality of various classification results. The two quality criteria mentioned in this paper and above are the key point of the framework and procedure: (1) For single-dimensional classification, the classification dimension shall keep consistent; for multi-dimensional taxonomy system, each classification dimension should be as orthogonal as possible; (2) For the classification results of a certain dimension, the intersection between the subsets shall be empty, and the union of all subsets shall be identical to the universal set of the entity-of-interest.  

In our opinion, one of the best ways to evaluate the effectiveness of the framework and procedure is their acceptance and adoption level by standards. As mentioned in this paper, the taxonomy framework has been adopted as part of the MBSSE reference framework in ISO/IEC/IEEE FDIS 24641 Systems and Software engineering - Methods and tools for model-based systems and software engineering [63], and figure 7 (a taxonomy of model) provided useful inputs to the classification of model in the same standard as well.  

4. The procedure above also relies on Table 2 to develop “superordinate concepts of digital twin (entity/system) used in typical definitions.” The table is an arbitrary selection of definitions that the paper simply claims to be “typical” with no criteria selection. The “Authors’ comments” column is equally arbitrary and without criteria.  

Reply: Thank you for the comment. Table 2, and figure 2 as well, only care about the superordinate concepts appeared in those definitions. So two criteria are applied for definitions selection in table 2: (1) to identify the superordinate concepts as more as possible, and (2) to select those authoritative definitions by standardization organizations, high citation index authors, famous research institutes and companies. As for “authors’ comments”, they are all about the identified superordinate concept. Each comment is based on one criterion: is the superordinate concept appropriate according to the intensional definition writing principles, requirements, and guidelines mentioned in ISO 704:2022 [19] 6.4.3? We will add these criteria in next revision of this paper.  

5. Step two of the procedure is “Select the first synset (twin (either of two offspring born at the same time from the same pregnancy)”. (This is also in Figure 2). While this is not an uncommon assumption, Digital twins were never meant to be compared to human twins. The “twin” metaphor is ontologically based. It is a basic way of human comparison in how humans know the world. All “twin” requires is duality and strong similarity. It does not require timeline simultaneity and certainly not human gestation (see Grieves 20221).  

Reply: Thank you for the comment. As mentioned above, the definitions and relations in figure 2 are copied from WordNet. Here we don’t mean comparing digital twin to human twins. We examine semantic relations among these superordinate concepts just from linguistics perspective. Here “twin” is regarded as a superordinate concept of “digital twin” and thus become the starring point of the concept map because of WordNet. The dashed line between the two synsets of “twin” just shows its etymological association. We can ignore or even remove the dangling node of the first synset (either of two offspring).  

We agree with you that all “twin” requires is duality and strong similarity. As for timeline simultaneity, you mentioned the “digital twin exists ONLY after there is a physical product” fallacy in your paper. However, the two major digital twin standards, ISO 23247-1:2021 [31] and ISO/IEC CDV 30173 Digital twin — Concepts and terminology, from different technical committees ISO TC184 and ISO/IEC JTC1/SC41, both regard synchronization as the delimiting characteristic of digital twin. We see a potential disagreement here. Let us leave it for future industry practice and standardization work.  

6. The DIKW (Data-Information-Knowledge-Wisdom) hierarchy model is, in my estimation, fatally flawed. While it looks profound, it suffers from the problem that there is no agreement as to what information, knowledge, and wisdom are and if there really is a hierarchy. (Data is much better defined.) There is no real consensus on the definition and differentiation of information, knowledge, and wisdom. The citation for DIKW is a Wikipedia page, which is generally unsuitable for academic references. In this case, the Wiki entry has some very valid criticisms of the DIKW model.  

Reply: Thank you for the comment. We will replace the Wikipedia citation for DIKW with related ISO standards in next revision of this paper. There are a few ISO standards offering the definitions of data, information, and knowledge following the DIK(W) hierarchy.

(1) ISO 8000-8:2015 Data quality — Part 8: Information and data quality: Concepts and measuring

3.1 data: reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing.

(2) ISO 5127:2017 Information and documentation — Foundation and vocabulary

3.1.1.15 data (pl): reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing. Note 1 to entry: Data are often understood as taking the form of a set of values of qualitative or quantitative variables.

(3) ISO 9000:2015 Quality management systems — Fundamentals and vocabulary

3.8.1 data: facts about an object.

(1) ISO/IEC/IEEE FDIS 24641 Systems and Software engineering — Methods and tools for model-based systems and software engineering [63]

5.1.1 Build models processes and Data-Information-Knowledge-Wisdom (DIKW)

The primary objective is building useful models in an effective and collaborative way. The core modelling activities can be put in perspective through the lenses of the Data-Information-Knowledge-Wisdom (DIKW) (See Ref [DIKW]) pyramid: ...... The relationships among the Build models processes and the Data-Information-Knowledge-Wisdom pyramid are presented by the Figure 3 ...... Figure A.2 provides an example of a Cognition Dimension based on the DIKW pyramid. A.1 Abstractness and generalizability of a MBSSE reference framework On the one hand, MBSSE dramatically enhances the effectiveness and efficiency of the transformation and collaboration among the DIKW pyramid; on the other hand, the cognition dimension provides a framework of information and knowledge management of MBSSE.

(3) ISO 56006:2021 Innovation management — Tools and methods for strategic intelligence management — Guidance

4.3 Core of the strategic intelligence process

The primary strategic intelligence cycle is referred to as the DIKI model: Data -> Information -> Knowledge -> Intelligence

The implementation of the strategic intelligence process requires use of various tools and methods, e.g. data mining, analytics, artificial intelligence, machine learning, prediction techniques, environmental scanning, technology watching, ethnographic research, to support the DIKI model.  

Although there are different ways to define data, information, and knowledge, such as bottom-up or top-down, the fact that data, information, and knowledge are tightly coupled is clear. In light of they are always used as superordinate concept each other in ISO standards, we would like to use the term “DIK(W) hierarchy” to represent the relationships among them. We agree that the pyramid shape is an over-simplified structure to represent the hierarchical relationship. The pyramid gives people an illusion that data is the only source of information and information is the only source of knowledge. We will not cite DIKW pyramid in the next revision of this paper.  

7. Figure 6 – I really don’t understand among other things how microscopic, mesoscopic, and macroscopic are “Temporal relationships” on an increasing time scale.  

Reply: Thank you for the comment. We used the scale terms, microscopic, mesoscopic, and macroscopic, for both spatial and temporal relationships. We understand that these terms mainly refer to spatial scales. To avoid confusion, we will rename these three terms with "microscopic time scale", "mesoscopic time scale", and "macroscopic time scale".  

We do appreciate your valuable and challenging comments. They are really helpful for us to re-examine and improve the quality of this paper a lot. We will update accordingly in the next revision of this paper.

It's an interesting topics to discuss the digital twin concepts, since there exits different categories of concepts originated from different background and domains. For better understanding of the readers, here are the suggestions for the authors' considerations:

Would you please clearly illustrate the most important motivations of research on digital twin concepts and proposal of the concept system? For example, you may consider to provide specific conflicts or other issues in standardization, academic or industry.

According to ISO 704:2022 (Terminology work — Principles and methods), the first principle of relationships between designations and concepts is “mononymy and monosemy” (ISO 704:2022 7.7.1). It says, “ ideally, when precise and accurate communication is required in a given special language, the objective is to achieve both mononymy and monosemy (depending on the perspective taken) at least within one and the same domain or subject. This condition reduces ambiguity, while synonymy (see 7.7.2), polysemy and homonymy (see 7.7.5) can lead to ambiguity”.

Table 1 of this paper shows the polysemy phenomenon of the term “digital twin”. It demonstrates that people have different points of view and understandings of the intensional meaning and scope of this term. People in communication perhaps can figure out the exact meaning of the term "digital twin" appearing in the context by reasoning and guessing. However, a systematic concept system and clear definitions are really helpful and needed for standardization work and software implementation. First of all, the term needs certain modifiers to resolve its polysemy and achieve the goal of monosemy.

(1) Would you please illustrate the methodology you use in this paper? What is the origin of this methodology? (2) Do you have any innovation or update on the methodology? (4) Why is this methodology appropriate to research on digital twin concepts? (3) Does this methodology be applied to other related concepts analysis?

Clause 5.6 in ISO 704:2022 is about concept systems. Clause 5.6.1 lists six functions and benefits of concept systems.

“ Concept systems are cognitive tools that serve to:

model concepts and relations between them within a concept field in a domain or subject;

“ Developing concept systems involves a series of iterative operations leading for example, to the compilation of a terminology resource in a specific domain or subject. These operations generally include:

selecting the concept field, the preliminary designations and concepts to be treated by taking into account the domain or subject as well as the target audience and its needs;

Therefore, the second and third paragraphs of the Methods chapter of this paper summarize the importance of a concept system and the relationship between developing a concept system and writing a term definition by referring to these two standards.

(2) However, the six steps in ISO 704:2022 5.6.2 are too general and lack of detailed guidance. The authors enrich and update the methodology of developing a concept system with system thinking viewpoints and systems engineering methods. The current revision of this paper mentions three innovations to the methodology.

Nine viewpoints and methods from Systems Engineering and TRIZ are proposed to introduce into the methodology, and four criteria about the concept system development process and tools are generated in the last part of general introduction of the chapter Methods. We will add new references from ISO/IEC JTC1/SC7 (system and software engineering) to the nine viewpoints and methods in the next revision of this paper.

(3) The concept system development methodology proposed by ISO 704 and enriched by this paper is general and applicable to any domain including digital twin.

A couple of domains have adopted this methodology in their standards and verified the effectiveness and benefits of developing their concept systems. The two most relevant domains are IoT and systems engineering. Especially, ISO/IEC 30141 IoT Reference Architecture is a perfect example of demonstrating the fundamental value of a concept system not only for terminology work but also for architectural standardization work.

The last sentence of this paper mentions that according to ISO’s definitions, the term “conceptual model” in application areas [64-66] is a synonym with “concept system” in the terminology work area [18, 19]. Figure 2 of ISO/IEC 30141:2018 (referred to as figure 8 (a) of this paper) shows that a conceptual model can derive a reference model described by different architecture views. In clause 8.1 main purpose of IoT conceptual model (CM) of ISO/IEC 30141:2018, it says, " CM provides a common structure and definitions for describing the concepts of, and relationships among, the entities within IoT systems". Annex C of ISO/IEC 30141:2018 describes the relationships between conceptual model, reference model and reference architectures in detail. Furthermore, in the introduction chapter of the draft of ISO/IEC 30141 next edition, it says, “ Conceptual View describes the IoT RA on a principal level. It defines the foundational concepts part of IoT. The types of capabilities provided by IoT devices, connectivity principles, characteristics and requirements are addressed”. The conceptual model or concept system becomes one of views of RA.

The next example of adopting the concept system development methodology is ISO/IEC/IEEE FDIS 42010 Software, systems and enterprise — Architecture description. It builds conceptual foundations based on conceptual models of architecture description related terms and concepts. ISO/IEC/IEEE 42010 is offering a meta-architecture of mandatory principles and rules and a precise and structured approach to the development of the latest edition of ISO/IEC 30141. It will do the same for the standard of digital twin system reference architecture.

In addition to the above two examples from IT area, health informatics (ISO/TC215), traditional Chinese medicine (ISO/TC249), nuclear energy (ISO/TC85), top-level ontologies and metadata registries (ISO/IEC JTC1/SC32/WG2), IT for learning, education and training (ISO/IEC JTC1/SC36), geographic information (ISO/TC211), and Intelligent transport systems (ISO/TC204) has adopted the methodology in their terminology or information modeling standards. Therefore, the methodology is applicable to and necessary for all terminology and ontology-related standards development.

Actually, the most important technical committee adopting this methodology is ISO/TC 184/SC 4, industrial data. Its two flagship serial standards, ISO 10303 (STEP) and ISO 15926 (integration of life-cycle data for process plants including oil and gas production facilities) are classic examples of using the methodology. Essentially, their goal is to build domain concept systems and ontologies for all kinds of industrial data and activities. To my understanding, the terminology work maturity level of some parts of the two serial standards is almost at level 5, i.e. achieving model-based ontology engineering. ISO/TC184/SC4 launched an advisory group (AG3) entitled “core terminology for industrial data” last year in order to build connections between domain concept systems and top level ontologies and align with relevant activities outside ISO/TC184/SC4.

All above mentioned standards and technical committees are offering enough best practices and lessons learned about adopting this methodology.

(4) Besides the universality of the methodology mentioned above, there is no obvious reason or characteristic of digital twin to show that it is an exception to the methodology. On the contrary, we can see a necessity that digital twin standardization and terminology work should adopt the concept system development methodology.

The interdisciplinarity of digital twin makes it become a general purpose technology and an integrated technology system. It needs to draw multidisciplinary support from the perspectives of not only academic research and industry implementation but also standardization. As a consequence, its high quality standardization will facilitate and benefit all industries.

Therefore, I recommend that the entry maturity level for digital twin terminology work should start from level three. We can achieve level four as a short-term objective and level five in the long run if needed for those areas need to be collaborated and integrated with other technical committees and top level ontologies.

Would you please illustrate the main logic of the proposed concept system? You may also consider to use some examples from industry or other domains to verify the robustness of the proposed system. Besides, the figures of the proposed system is very complicated. You may consider to divide them into a series figures, in which one perspectives are given.

In addition to the dozens of digital twin definitions in review articles mentioned in the introduction chapter and table 2 are examined for analyzing the polysemy phenomenon of the term “digital twin” and identifying those superordinate concepts, the authors did check those reference models and architectures proposals by various organizations and current digital twin standard projects of ISO, IEC, IEEE and ITU. We didn’t find conflicts between them and the proposed concept system especially the differentiation of digital twin entity and digital twin system. We will continue to check industrial use cases and the progress of standard projects in order to verify the robustness of the proposed concept system and improve it continuously.

We decided to choose EXPRESS-G (ISO 10303-11) as the modeling tool of Fig 3 instead of UML or other languages just because of its concise representation rules and high information density. It saves a lot of page space but sacrifices the readability. In addition to Fig 4, 5 and 6 are simplified view, local view, and enrichment view of Fig 3 respectively, we will add a new figure as a local view of DTw Entity, modify Fig 5 as a local view of DTw System, and add another new figure as a simplified global view of Fig 3 only with generic relations and without attribute relations in next revision of this paper.

Would you please illustrate the application areas and potential benefits? Will the concept system be a potential contribution to ISO/IEC CDR 30173 digital twin - concepts and terminology? Could it be applied to practical design of a digital twin system for an equipment or a factory?

As for ISO/IEC 30173, I recommend adopting the methodology to develop a systematic and robust digital twin concept system. The proposed concept system could be a benchmark or a prior art for checking potential conflicts of current definitions in ISO/IEC 30173 with other top level ontologies and other domains.

Yes, the proposed concept system can be applied to the practical design of a domain-specific digital twin system, such as equipment, a factory, an energy network, or a city.

First of all, all kinds of target entities can find their position in the proposed concept system (Fig 3) and especially in the top level ontology of Entity (Fig 4). Thereby the superordinate concept of the target entity can offer some constraints or business considerations to the digital twin system.

We will update the conclusions accordingly in the next revision of this paper.

1) Sentences are too long to understand. Please change to shorter sentences.

Thank you very much for your comment. We will improve the writing in the next revision.

2) What are the components of DTw (Digital twin) system?

According to Figure 3, DTw system has six components: 

(a) communication framework of a list of DTw Entities,

3) In Fig. 1, the difference between the ‘DTw framework’ and the ‘DTw system’ is seen only as physical entities (observable manufacturing elements), please explain the difference.

Yes, this is the only difference between DTw Framework in ISO 23247 and DTw System in this paper. There are various points of view about the scope of DTw System, such as with or without physical entities, and with or without user entities. I think all of them have their own rationales. The blue box scope in Figure 1 is adopted in this paper according to the trend of system completeness from TRIZ [20,21] mentioned in Methods chapter. A DTw system is kind of bi-system from TRIZ perspective. I think the broader scope of DTw system reflects the interaction and co-evolution of the target entities and DTw entities in a DTw system more precisely and conveniently. To my personal point of view, the coming DTw system reference architecture should be based on the broader scope.

4) Terms needing definition: Important terms, but lack of explanation. Entity, Target entity, System

Due to space limitations, the definitions of most concepts of the DTw concept system are not listed in this paper. However, the references of the meanings of the above three terms are provided in this paper.

(a) As for Entity, in the subchapter of "Top-level ontology with entity related concepts", the first sentence of the second paragraph, says "By referring to WordNet 3.1 (RRID:SCR_022182) and ISO/IEC 21838-2:2021 [61]…". It means that not only the definitions of Entity in WordNet and BFO are important inputs to this paper, but also we completely agree with the highest position of the term Entity in WordNet and BFO. Therefore, Figure 4 (Top-level ontology and taxonomy of entity) is developed under the guideline mentioned in the subchapter "The taxonomy framework related concepts" and double check with WordNet and BFO.

As for the definition of Entity of the DTw concept system, we analyzed the definitions in ISO/IEC 20924:2021, ISO 9000:2015, and WordNet.

ISO/IEC 20924:2021 3.1.18, entity

thing (physical or non-physical) having a distinct existence

[SOURCE: ISO/IEC 15459-3:2014, 3.1]

ISO 9000:2015 3.6.1, object/entity/item

anything perceivable or conceivable

EXAMPLE: Product (3.7.6), service (3.7.7), process (3.4.1), person, organization (3.2.1), system (3.5.1), resource.

Note 1 to entry: Objects can be material (e.g. an engine, a sheet of paper, a diamond), non-material (e.g. conversion ratio, a project plan) or imagined (e.g. the future state of the organization).

Entity in WordNet: that which is perceived or known or inferred to have its own distinct existence.

We proposed a definition of Entity for the DTw concept system based on WordNet and combing ISO 9000:2015 3.6.1: that which is perceived or known or inferred or conceived to have its own distinct existence. ISO/IEC 20924:2021 3.1.18 and the note of ISO 9000:2015 3.6.1 were used as input to our definition's note, i.e. the rest part of the first sentence of the second paragraph, it says "this paper sorts out the taxonomy perspectives of entity and performs a first-level decomposition of Entity, including physical entity(comprising of man-made physical entity, natural entity, and social group), virtual entity, physical-virtual hybrid entity; abstract entity, concrete entity; matter, energy, information; system, process; asset and non-asset". 

(b) As for System, it is at the second level of the top-level ontology and taxonomy of entity (Figure 4). We agree with its definitions from related fundamental ISO standards, such as ISO 9000:2015 3.5.1 (set of interrelated or interacting elements) and ISO/IEC/IEEE DIS 15288:202x 3.47 (arrangement of parts or elements that together exhibit a stated behaviour or meaning that the individual constituents do not). Besides the definitions from standards, personally, I like the dual definition of System proposed by Avenir I. Uyomov, a former USSR and Ukrainian philosopher. In his book "Системный подход и общая теория систем" (Systems Approach and General Systems Theory, 1978, page 120-121), it says: " any entity in which there is a relationship of a predetermined property is a system; any entity in which there is a property of a predetermined relationship is a system". I think that the epistemological definition of System can be regarded as a major theory fundamental to information modeling and conceptual modeling languages and methods, such as EXPRESS (ISO 10303-11) and OPM (ISO 19450).

After reviewing these three definitions of System, we understood that they are equivalent and offer a specific view of System respectively. According to ISO 1087 and ISO 704 mentioned in the second paragraph of the Methods chapter, the position of a concept in a concept system really matters to determine its definitions. When the top level of Entity and second level of System are determined in the DTw concept system, their scopes and meanings are clear for readers. For instance, we decline the definition of System in ISO/IEC/IEEE 15288:2015 4.1.46 (combination of interacting elements organized to achieve one or more stated purposes) due to the inconsistency with its position in the DTw concept system.

As mentioned in the chapter Result, more than half concepts in the DTw concept system have a formal definition. The analysis and decision-making of their definitions were all gone through such a process like the concept Entity and System discussed above and followed the guideline of terminology work standards and system thinking techniques mentioned in the chapter Methods. Although determining these definitions is an important work for the quality of the DTw concept system, it is still the detail design and development part of the whole project, which has less significance than the architecture design of the DTw concept system, like determining the position of a concept. Therefore, except for the three concepts: digital twin entity, digital twin system. and digital twin, we decide to exclude the term definitions out of the scope of this paper.

(c) We provide an extensional definition of Target Entity in the subchapter, "The analysis of the superordinate concepts of digital twin definitions" when the term first appears. It includes two options: physical entity or physical-virtual hybrid entity. However, there are three options for Target Entity Type in Figure 3: Physical Entity, Digital Entity, and Physical-Virtual Hybrid Entity. I will update its extensional definition in the next revision according to Figure 3.

BTW, we used the term "digital twinning target" translated from the Chinese version of the DTw concept system two years ago [60]. With the development progress of ISO/IEC 30173, we adopted the term "target entity" used by 30173 as both of us share the same understanding of the concept, i.e. the scope of the counterpart of DTw Entity.

5) Fig. 2 seems to be a good trial. Semantic-relation is good. It would be better if the above terms (Entity, Target entity, System) are included in Fig. 2 and explained together.

The purpose of Fig 2 is to analyze the semantic relationship among all candidate superordinate concepts of an important or confusing or brand new concept or term. The outcome of Fig 2 only resolves a problem of a node in the  DTw concept system. The benefit of Fig 2 (semantic relationship analysis of superordinate concepts for an individual term) is to facilitate the term definition work by building connections between linguistic perspective (the term) and business perspective (the concept). They are two nodes or two aspects of the semiotic triangle. BTW, the latest text of ISO/IEC CD 30173 describes the relationships between digital twin and the semiotic triangle.

Regarding the high-level and key concepts like Entity and System in the DTw concept system, it is not necessary to apply the method and tools in Fig 2. With the help of BFO [61], WordNet, and other general purpose standards like ISO 9000 and ISO/IEC/IEEE 15288:202x, the technical community already has enough consensus views on their positions and intensional meanings in both a top ontology like BFO and the DTw concept system.

As for Target Entity, its situation is quite different from that of Entity and System. First of all, the semantic relationship distance between Target Entity and Entity / System is so long that Target Entity becomes a domain-specific term rather than a general term. Secondly, even in the domain of digital twin, Target Entity is still an artificial and imaginary local concept. The origin of the concept comes from a root question: In the world and the universe, what kinds of Entity can be and could be digital twinned? We can derive a couple of sub-questions from the root question, such as (a) Is there any kinds of Entity that cannot be digital twinned forever? (b) Is it possible the process or phenomenon of digital twinning can be nested like the plot of the film "Inception"? (c) If the concept and term Physical Entity is not enough to cover the scope of digital twinning target, how do we coin a new concept and term to describe it?

The answers to the root question and its subquestions led us to develop the top-level ontology and taxonomy of Entity (Figure 4), to define the new concept "digital twinning target" (a seven-character phrase in Chinese), and to adopt the new term "Target Entity" (a four-charactre phrase in Chinese) from ISO/IEC 30173. As mentioned in the chapter Methods, according to ISO 704:2022 [19] 3.11, 5.6.3, and 6.3, intensional definitions are critical to the development of a generic concept system. Due to the locality of the concept Target Entity and its extensional definition, its importance in the DTw concept system is much lower than other concepts. This is why we only assign Target Entity a constructed data type SELECT rather than a named data type ENTITY of ISO 10303-11 [24] in Figure 3. To my understanding, this is also why the current ISO/IEC CD 30173 only offers a note to target entity rather than a formal definition. The case of Target Entity can be regarded as another application example of Ockham's Razor principle mentioned in the subchapter "The analysis of the superordinate concepts of digital twin definitions".

As a result, we draw the same conclusion as the term Entity and System, i.e. it is not necessary to apply the method and tools in Fig 2 to the coined term "Target Entity".

6) How is ‘Entity’ different from ‘Thing’ in IoT?

According to ISO/IEC 20924:2021 3.1.18 or any other definitions mentioned above in the reply (a) to comment 4), entity refer to thing (physical or non-physical) having a distinct existence; according to ISO/IEC 20924:2021 3.2.7, Internet of Things refer to infrastructure of interconnected entities, people, systems and information resources together with services which processes and reacts to information from the physical world and virtual world.  "Entity" is the highest concept of the DTw concept system. Therefore, ‘Thing’ in IoT is only a small subset of Entity.

7) A simplified version of Fig. 3 is needed. The current version is too complex to comprehend. The ‘target entity type’ is visible, but where is the ‘target entity located? I can't distinguish colors well. Please change the colors. Where is the ‘digital twin’ located?

Actually, Figure 4 is a simplified view of Figure 3 focusing on Entity-related concepts; Figure 5 is a local view of Digital Thread connecting DTw System and DTw Entity; Figure 6 is an enrichment view of Model in Figure 3. Only generalization relationships are represented in the UML class diagrams in Figure 4 and 6. They are much easier to read than Figure 3. The reading and understanding of Figure 3 need familiarity with the syntax and notion of EXPRESS-G [24]. In a summary, the meaning of legend to Figure 3 is as follows:

The thick solid line and the small circle represent the inheritance and generalization relationship, and the thick solid line marked with "1" represents the exclusive inheritance and generalization relationship constraint (all lower level specific concepts under a certain higher level generic concept are mutually exclusive relationships); thin solid lines with a small circle represent a necessary attribute relationship, the dotted line with a small circle represent an optional attribute relationship, (DER) means derived attribute, and “L” means List aggregation type; the solid line box represents ENTITY data type, and the dashed box with the left vertical line represents SELECT data type.

We will change the purple color in the next revision. The dashed box "Target Entity type" is where "Target Entity" is located. I used the name "Target Entity type" to emphasize that it is a SELECT data type. We will rename the dashed box with "Target Entity" in the next revision.

According to Table 1 (The polysemy phenomenon of the term digital twin(s)), we propose a definition of digital twin in the subchapter "Digital twin system related concepts", "the enabling technology system, discipline field, or industrial ecology derived from a digital twin entity or digital twin system". Therefore, it is hard to position the concept "digital twin" in Figure 3. Besides the meaning of DTw System or DTw Entity its referring to, "Digital Twin" will be also appeared as a subordinate concept of several superordinate concepts, such as Industry, technology (subordinate concept of Knowledge), market (subordinate concept of commercial activity, and subset of process), discipline field (subordinate concept of Knowledge), etc, Therefore, it doesn't make sense to include the term "Digital Twin" into Figure 3.

8) Fig. 4:  (a) How does ‘Entity’ relate to ‘Thing’ of IoT?

In Figure 3, we can see that IoT is a kind of Physical-Virtual Hybrid Entity. So we can add a subordinate concept "IoT" to "Physical-Virtual Hybrid Entity" in Figure 4, and enrich the node "IoT" by introducing the generic relationships in Figure 10 (high level view of IoT conceptual model) of ISO/IEC 30141:2018 [64]. In this way, we can relate "things in IoT" to "Entity". However, I think this is out of the scope of this paper.

(b) Where is the ‘target entity’ located?

As mentioned above in the reply to comment 5), "Target Entity" is a domain-specific local concept. It is not appropriate for it to be appeared in Figure 4, the top-level ontology of Entity. Anyway, we can add a class box of "Target Entity" in Figure 4 with three <> arrows pointing to the class boxes of "Physical Entity", "Digital Entity", and "Physical-Virtual Hybrid Entity" respectively, Seeing that the semantic meaning and relationships of "Target Entity" have been already represented in Figure 3, it is not necessary to do it again in Figure 4. It would make the UML class diagram a little bit weird.

(c)  ‘Digital entity’ and ‘physical entity’ seem to correspond to each other, so that how about placing their positions in the figure in the corresponding position?

Figure 4 is not from the perspective of DTw system or DTw entity, but from the omniscient point of view. The purpose of Figure 4 is to develop a comprehensive taxonomy for Entity. Some ISO standards, like ISO/IEC 20924:2021 (IoT Vocabulary), think that "digital entity" and "virtual entity" are synonyms or even identical. However, we have a different opinion in this paper. There are two definitions of physical entity: (1) entity that has material existence in the physical world (ISO/IEC 20924:2018, 3.1.26); (2) thing that is discrete, identifiable and observable, and having material existence in real world (ISO/IEC 23093-1:2020, 3.2.14). So the antonym of "physical" is "virtual" (existing in essence or effect though not in actual fact), not "digital" (relating to, using, or storing data or information in the form of digital signals). "Digital" is a subset of "virtual", and "digital entity" is a subset of "virtual entity". Virtual Entity can be classified as Digital Entity and non-digital virtual entity. The strict differentiation between virtual and digital will be helpful to answer the three subquestions mentioned in the reply to comment 5).

9) The logic of adopting the definition of ISO/TS 18101-1:2019 in this paper lacks explanation. There is an explanation of the benefits, but additional explanations and logic such as the similarities of each component are needed.

We don't adopt the definition of ISO/TS 18101-1:2019, only its superordinate concept "digital asset" is adopted. The simple logic is, that we cannot use a subfunction (digital representation) as the superordinate concept of DTw Entity, we need to think out of the linguistic box of semantic relationships and break the psychological inertia by introducing business factors.

10) There are ‘DTw system’, ‘DTw entity’, and ‘digital twin’ in front of Fig. 5, but a relationship diagram between them is needed.

Besides the attribute relationships between DTw System and DTw Entities shown in Figure 5, both Figure 3 and Figure 4 show the generic relationships of DTw System and DTw Entity to their superordinate concepts. DTw System and DTw Entity are only two nodes of Figure 3, however, the name or title of Figure 3 can only be called "The Digital Twin concept system". That is the relationship among DTw, DTw System, and DTw Entity. The term "digital twin" used in the title of Figure 3 is consistent with the definition of "Digital Twin" in this paper.

11) The ‘Taxonomy’ section including (Fig. 6, Fig. 7) looks useful, but there are no additional new contents, and it seems to be a simple introduction and example, so it is desirable to move the section to the appendix.

Besides the taxonomy framework itself, this subchapter shows the procedures of the development of Figure 4 and 7. Just like the top-level ontology of Entity offers the backbone for the DTw concept system, the taxonomy framework provides the implementation support of the DTw concept system. Therefore, the meta-model of the taxonomy framework is also indispensable to the DTw concept system.

12) Overall, the paper is good because there are new works, but after reading the paper, I expected difficult problems to be sorted out and simplified, but it became more complicated after reading the paper. Perhaps it was because the content was difficult to understand.

We do appreciate your comments to help us improve the quality and readability of this paper. The main purpose of this paper is to resolve the polysemy issue of the term "digital twin". People in communication perhaps can understand each other and figure out the exact meaning and scope of the term "digital twin" appearing in the conversation by reasoning and guessing. However, for the standardization work and software implementation, a systematic concept system and clear definitions are really helpful and needed.