1. Introduction
The digital transformation of metrology is one of the strategic challenges clearly identified by the International Committee for Weights and Measures (CIPM) towards the 2030+ strategy [1]. The “CIPM Strategy 2030” action report states that we are in the midst of a digital revolution, which is challenging the metrology community’s practices and paradigms in terms of metrology traceability and reproducibility. We must bring metrology into the digital world and “digitize” metrology if we are to ensure consistency and confidence in measurement in this new world [1]. The digital transformation of metrology can bring many benefits to our society. It can help improve measurement and data processing efficiency within the metrology field, reduce human errors, and enhance the accuracy and precision of data.
Metrology vocabulary is fundamental to metrology activities, serving as a basic tool for describing, expressing, and recording various physical and chemical quantities, etc., and is an indispensable element in the measurement process. In the context of digital transformation, these vocabularies provide the necessary framework and clarity for data exchange, analysis, and automation. Both the International Bureau of Weights and Measures (BIPM) and the SI working groups have highlighted the need to digitize metrology vocabulary, urgently requiring digital representations of physical quantities and measurement data to support the digital transformation of national and international quality infrastructure [2,3]. As a modeling tool that describes domain concepts at the semantic and knowledge levels, ontologies can capture the knowledge of a relevant domain and describe the semantics of concepts through their relationships, providing a shared understanding of the domain knowledge [4]. Due to its advantages in knowledge sharing, semantic interpretation, and scalability, it has become a solution for achieving the digitization of metrology vocabulary [5,6].
Time metrology vocabulary is a more special and important part of the entire metrology vocabulary, playing a fundamental role in various fields and applications, providing a basis for measuring, recording, and transmitting time-related information. It is an indispensable bridge between connecting real-world events and abstract data analysis. Whether in simple expressions of time in everyday life or in complex time data processing in scientific research and technical applications, time metrology vocabulary is a fundamental tool for understanding and operating with time. An ontology of metrology vocabulary can help standardize terminology and data formats in the field of metrology, making data exchange and sharing between different systems consistent and understandable, helping to avoid information inconsistency and confusion, and improving data quality. In addition to this, metrology vocabulary ontologies also define the semantics and relationships of various terms, which can facilitate information sharing and interoperability across systems and platforms. This means that data can be more easily understood and exchanged between different systems, enabling more efficient information sharing and integration, which plays an important role in the digital transformation of metrology.
Currently, the main existing ontology that includes metrology vocabulary is MetrOnto [7], a metrology ontology developed by the Italian National Research Council (CNR), which mainly consists of a variety of common units of measure and the related conversion rules, covering units of measure in several dimensions, such as length, mass, time, etc., and supporting a wide range of international common standards. Although the MetrOnto ontology contains some content related to time metrology, it still falls short in its systematic description of the time metrology vocabulary. For example, it lacks many vocabularies related to time metrology and a description of their concepts and relationships and lacks some logical relationships, etc. Moreover, there is no ontology specialized in describing time metrology vocabulary at present. To make up for the shortcomings of the MetrOnto ontology, solve the problem of word expression of time metrology vocabulary in the digital world, and promote the digital transformation of metrology, this paper takes time metrology vocabulary as the object of research; carries out the construction of an ontology; and divides classes, attributes, and instances, as well as expresses their logical relations. The main research content of this paper is as follows:
(1). To support the digital transformation of the metrology field, this study constructs the Ontology of Time Metrology Vocabulary (OTMV) based on China’s national standards, metrological technical specifications JJF1180-2007 “Glossary and Definition of Time and Frequency Metrology” [8], JJG 2007-2015 “Time and Frequency Measuring Instruments” [9], relevant materials published by the China National Institute of Metrology, etc.
(2). A series of experimental validations are conducted on the constructed ontology, such as ontology consistency validation, machine-readable validation, machine-understandable primary validation, and information retrieval validation. In addition, relevant application experiments are also performed. Finally, the work of this research is summarized, and future research work is presented.
2. Materials and Methods
2.1. Ontology Construction Methods and Tools
Currently, the methods for ontology construction mainly include manual construction, semi-automatic construction, and automatic construction [10]. The manual construction method mainly determines the knowledge content and relationship through the guidance of domain experts and thus has higher quality and higher accuracy; the semi-automatic construction method refers to using machines to provide assistance in manual construction, which still focuses on the role of human beings, and in the face of massive knowledge, the semi-automatic construction has a great advantage in the aspects of knowledge extraction, relationship extraction, etc., but it still needs to be designed and selected by the final rules and programs with human intervention and validation [11]; the automatic construction method refers to machine learning, artificial intelligence, and other technologies, so that it is free from human intervention to realize independent learning and evolution, but at present, the application of automatic construction of an ontology is rare, it cannot completely get rid of human intervention [12], and the knowledge in the field of metrology requires a high degree of knowledge completeness and accuracy, so compared to the above three methods, the manual construction method is the most suitable for carrying out the ontology construction in the metrology domain.
At present, the main methods for manual ontology construction primarily include the IDEF5 method [13], Skeleton method [14], TOVE method [15], METHONTOLOGY method [16], seven-step method [17], etc. Among these, the IDEF5, Skeleton, and TOVE methods are used for constructing enterprise ontologies. The METHONTOLOGY method is specifically used for constructing chemical ontologies, while the seven-step method can be applied across multiple domains, especially for constructing domain ontologies. It provides a structured and systematic approach that helps ensure the logical consistency of ontologies by defining classes, properties, and relationships [18]. Furthermore, the seven-step method also emphasizes the scalability of ontologies and their suitability for building specific domain knowledge ontologies [19]. Therefore, in this paper, the seven-step method is chosen as the method for OTMV construction. The main processes of ontology construction using the seven-step approach are (1) identifying the domain and scope of specialization of the ontology; (2) examining the possibility of reusing the existing ontology; (3) listing the important terms in the ontology; (4) defining the class and class hierarchy; (5) defining the attributes of the class; (6) defining the facets of the attributes; and (7) creating instances. However, manual construction is not a simple task, and many challenges are faced during the construction process, such as obtaining accurate and comprehensive knowledge from domain experts, modeling complex conceptual relationships, maintaining consistency and completeness, maintaining and updating the knowledge, as well as performance and scalability issues when scaling up. Therefore, the selection of appropriate tools and methods for ontology construction is crucial.
RDF (Resource Description Framework) [20], RDFS (Resource Description Framework Schema) [21], and OWL (Web Ontology Language) [22] are ontology description languages recommended by the W3C. They are written based on XML (Extensible Markup Language), which are metadata (data that describe data) capable of being understood and processed by machines to address semantic issues [23]. RDF can describe entities, their attributes, and the relationships between them, but it cannot describe the relationships between classes and their attributes, etc.; RDFS offers improved semantic expression capabilities over RDF, though it still has limitations in semantic expression; compared to RDF and RDFS, OWL includes additional predefined vocabulary, supports rich semantic expression and logical reasoning, and has superior semantic capabilities. Therefore, this paper chooses OWL 2 [24] as the description language for ontology construction.
This paper opts for the Protégé [25] software to select ontology construction tools. Protégé is an ontology editing and knowledge acquisition software developed in Java by Stanford University and serves as a core tool for constructing ontologies within the Semantic Web. Protégé supports descriptions in Chinese and is highly compatible with the seven-step method. It also integrates the open-source OWL2 reasoner Pellet developed by the MindSwap Laboratory at the University of Maryland, College Park. Compared to other ontology construction tools, it offers numerous advantages [26].
In addition to the aforementioned tools, this paper also incorporates the Simple Knowledge Organization System (SKOS) [27] to aid in ontology construction. SKOS is a currently evolving description language for simple knowledge organization that allows the structure and concepts of vocabularies to be expressed in a machine-understandable manner, facilitating data exchange, reuse, and sharing [28]. Meanwhile, to support the usability of ontologies, this paper adopts bilingual descriptions in both English and Chinese for ontology construction, rdfs:label in English, and rdfs:alternative label in Chinese. Additionally, ontologies can be used to semantically annotate words by defining and declaring related concepts and distinguishing the meanings of different words based on the contextual information provided in the definitions or declarations. For example, the meanings of “apple” in “apple” and “Apple Inc.” can be distinguished using definitions in the ontology and because the meanings of “apple” and "apple company" are defined in the ontology, the ontology can infer that “apple” refers to a fruit based on the context when processing the relevant information, while in “Apple Inc.”, “Apple” refers to a company name. This can help users to be able to understand the terms and concepts in the ontology in depth, thereby using the knowledge in the ontology more accurately.
The OTMV construction flowchart is illustrated in Figure 1. The construction process is divided into three main parts i.e., the determination of concepts related to time metrology vocabulary, ontology construction, and ontology evaluation and validation. Among them, the determination of concepts related to time metrology vocabulary primarily involves China’s national standards, metrological technical specifications, relevant materials published by the China National Institute of Metrology, etc. The ontology construction’s aim is to extract information from the above-mentioned relevant documents and materials to establish the model, which mainly contains the following parts:
(1). Determining the scope of knowledge. Based on the selected time metrology vocabulary documents, relevant information is extracted to identify the knowledge content that needs to be constructed. Determining the scope of knowledge is crucial for the construction of the ontology to ensure that the OTMV can be effectively utilized and maximally applied in the future.
(2). Defining hierarchy relationships between classes. To adapt the OTMV to the requirements of digital transformation, this paper proposes a classification principle that meets the intrinsic requirements of time transfer in the digital world for the division of parent classes, and a combination of top-down and bottom-up methods for the division of subclasses. Based on the knowledge content determined in the first step, the important terms are listed and which terms will be used as classes is determined, and then, the hierarchical relationship of these terms is further divided to establish a hierarchical structure.
(3). Defining object properties of classes. After defining the class hierarchy, it is necessary to add object properties to the ontology, which define the relationships between classes. In addition, domains and ranges need to be assigned to these object properties, and constraints may be added as necessary. The domains and ranges in the object attributes point to the classes.
(4). Defining data properties of classes. Data properties describe the relationship between individuals and data values, and data properties can only be added to instances. Data properties also require the addition of corresponding domains, value domains, and constraints, where the definition domain of the data properties points to the class and the value domain refers to the data type of the data value.
(5). Creating instances. After defining the classes, object properties, and data properties of the ontology, the ontology can be further refined and perfected by adding corresponding instances to the class with instances. The addition of instances can verify whether the structure of the ontology is complete and accurate, assist in explaining and understanding the concepts within the ontology, and be used for knowledge reasoning and discovery, evaluating the applicability of the ontology.
The evaluation and validation of the ontology are mainly performed to assess its reliability and applicability. The construction of an ontology is not an easy task; it requires significant time and energy to sort out the knowledge, find out the relationship in these concepts, etc. It is also limited by the researcher’s own ability, so there will inevitably be some errors in the process of constructing it, which will cause some inaccuracies and redundancies, so it is essential to carry out the relevant checking and correcting of the ontology [29]. A consistency check of an ontology can check the correctness of the grammar and logical consistency in the ontology; a machine-readable verification can check whether the ontology can be read and processed correctly by a computer; knowledge reasoning can help us to better understand and utilize the knowledge in the ontology, and to improve the completeness and accuracy of the knowledge; and information retrieval can enhance the applicability of the ontology. The construction of ontologies also needs to keep up with the times and needs to be constantly updated and improved. By optimizing the structure of ontologies and using efficient inference engines to improve performance and scalability, the efficiency and quality of manually constructed ontologies can be improved to ensure their effectiveness in real-world applications to meet the needs of the metrology field.
2.2. Ontology Construction
2.2.1. Determination of Time Metrology Knowledge
Relying on the background of the metrology field, the scope of knowledge is the content related to time in the field of metrology. By consulting relevant national standards, metrological technical specifications, and scholarly articles, this study primarily bases its findings on JJF1180-2007 “Glossary and Definition of Time and Frequency Metrology”, JJG 2007-2015 “Time and Frequency Measuring Instruments”, relevant materials published by the China National Institute of Metrology, and related literature research. Time-related terms, concepts, and relationships are extracted from them and used for ontology construction.
2.2.2. Classification Principles
Traditional classification methods categorize vocabulary based on the concept of the words; although this method can divide the vocabulary in a certain field into classes, from the logical level, the expression is not clear enough, and there is no specific classification principle for the division of classes. Therefore, to address this issue, this paper combines the work of time services in metrology–time conservation, time measurement, and time service, and the process of time transfer and proposes a classification principle that meets the inherent requirements of time transfer in the digital world—that is, following the process of transferring an accurate time to the digital world and expressing it, dividing the parent classes.
Based on the above principles, the time metrology vocabulary is categorized into the four core classes of the national measurement standard for the duration of a second, time conservation, time measurement, and time service, and the relationship between this transfer process and the core classes is illustrated in Figure 2.
2.2.3. Defining Hierarchy Relationships between Classes
Based on the content extracted in the first step and the four defined core classes, a combination of top-down and bottom-up approaches is used to divide the subclasses. Initially, the extracted terms are classified as properties, analyzing which terms should be categorized as classes and which as properties. Then, according to the classification method, these classes are added to the four core classes. To more visually display the hierarchical structure of the classes, this paper uses the time service class as an example to showcase the class hierarchy, with the results presented in Table 1.
2.2.4. Defining Object Properties of Classes
The definition of object properties is an essential part of the ontology construction process, which is related to the completeness and accuracy of the ontology. Object properties describe the relationships between two individuals. Their definition and usage are crucial for ontology construction and reasoning, which can help us clarify and distinguish between different types of objects and provide rich information for the reasoning engine [30]. By defining object properties, we can more accurately represent the relationships between entities, thus facilitating the application of ontologies in knowledge representation and semantic reasoning.
The definition of object attributes in OTMV is primarily based on the descriptions of time metrology vocabulary concepts and relationships found in national standards, metrological technical specifications, and various related materials, from which the relationships between entities and entities are extracted. And, these relationships are defined as object properties, to which the corresponding domains and ranges are added, and some constraints are needed if necessary, which will make the relationships described by the object properties more accurate to avoid ambiguity.
Figure 3 illustrates the design of the primary object properties centered on the time scale class. The box represents the class, the arrows represent object properties, the starting point of the arrow is the domain, and the endpoint is the range. For instance, an instant is a point on the time scale, and a time interval is a difference between two points on the time scale, so the time scale includes instant and time intervals. Beijing time is a form of expressing an instant, and leap seconds are introduced to correct Coordinated Universal Time (UTC), among others. By establishing object properties, the relationships between classes can be clearly represented, making it more intuitive than traditional text representation.
2.2.5. Defining Data Properties of Classes
The definition of data properties describes the relationship between individuals and data values. Data properties are attributes of the class itself, and thus, they can only be assigned data values to instances. Data properties also require the specification of domains and ranges. The domain refers to the class, while the range refers to the data type of the data values.
Table 2 lists some of the data properties in OTMV and their corresponding data types. For example, the time encoding format uses the International Standard Encoding Format, which can be found in the file contents in rows 6 and 7 of Table A1 in Appendix A, so the data type is xsd:dateTime; the value of the leap second is an integer, so the data type of the data property “has a leap seconds of” is xsd:integer. The addition of data properties and the accurate description of data types are very important for the creation of instances. Furthermore, they can help verify the consistency of the ontology. If the data type of a value in an instance does not match the data type specified in the data property, it can lead to inconsistencies during the reasoning of the ontology.
2.2.6. Graphical Presentation of OTMV Construction Results
After the OTMV is constructed by the above construction process, the relationship between the entities can be visualized more intuitively. In this paper, we take the time service class in OTMV as an example to visualize the results, as shown in Figure 4. The figure shows the hierarchical relationship of each class in the time service class, the relationship between classes and classes, and the relationship between classes and instances.
3. Results
3.1. Ontology Consistency Check
The consistency check of the ontology uses the OntOlogy Pitfall Scanner (OOPS!) [31] online tool. OOPS! performs more automated checking than other existing tools. The system is independent of a specific editor and has a simple interface, thus making it an easy-to-use and understandable tool for non-semantic technologist users. It helps us detect and fix some common errors while building ontologies in time. OOPS! encompasses a total of 41 pitfalls, which are categorized by their impact on the ontology into three levels of severity: critical, important, and minor. We can modify the ontology based on the results of the checks and the seriousness of the pitfalls detected.
The results of the OOPS! inspection of the OTMV can be seen in Figure A1 in Appendix A. According to the validation results, the OTMV has no syntactic or logical errors and conforms to the consistency of the ontology.
3.2. Machine-Readable Verification
Machine-readable ontology can realize data transmission and processing, parsing semantic information in the ontology such as concepts, relationships, and constraints, providing fundamental support for knowledge reasoning and semantic search within the ontology. Machine-readable formats include RDF/XML, N-Triples, Turtle, etc., and an ontology built using Protégé can be saved in these formats. The OTMV is stored in RDF/XML format, which provides a prerequisite for the ontology’s machine readability.
This paper uses the RDFlib library in Python to perform machine-readability verification of the ontology and two experiments for verification. Experiment 1 uses the SPARQL Query [32] language to query triples within the ontology and displays them in the format of Subject:{subject}, Predicate:{predicate}, Object:{obj}, and the results of the experiments can be seen in Figure A2 in Appendix A. To present the results of the experiments more clearly, this paper selects eight ternary groups and draws them into a table for display, as shown in Table 3; Experiment 2 queries all properties in the OTMV and counts the top 10 most frequently occurring properties. To display the query results more intuitively, this paper displays the query results in the form of a table, as shown in Table 4. The results from both experiments indicate that the OTMV is machine-readable.
3.3. Machine-Understandable Primary Verification
The machine-understandable primary verification verifies the knowledge reasoning capabilities of the ontology. Knowledge reasoning can infer some implicit logical relationships in the ontology based on predefined rules, which demonstrates that the ontology can be understood by the machine and can be machine-understandable.
This study uses the Semantic Web Rule Language (SWRL) [33] for rule writing and utilizes the Pellet reasoning engine for knowledge reasoning. Example rules are shown in Table 5.
This rule expresses that if a global navigation satellite system instance ‘?x’ uses a certain function ‘?f’, then another global navigation satellite system instance ‘?y’ will also use the same function ‘?f’.
The results of the reasoning based on the above rules are shown in Table 6, and the specific reasoning results can also be seen in Figure A3 in Appendix A. From the table, we can see that the results after reasoning are that the Galileo satellite navigation system is similar to the global positioning system, BeiDou satellite navigation system, and GLONASS satellite navigation system; uses GPS common-view, two-way time and frequency transfer, and carrier phase measurements; and has the same functionalities as other instances within the global navigation satellite system, thereby verifying that the OTMV has the capability for knowledge reasoning, can be understood by machines, and possesses machine understandability.
3.4. Information Retrieval Validation
The information retrieval validation of OTMV is carried out using the SPARQL Query module in Protégé. Take querying the instances in the global navigation satellite system as an example for retrieval validation, and the validation results can be seen in Figure A4 in Appendix A. Four instances in the global navigation satellite system were successfully retrieved from the validation results, which validates that OTMV has an information retrieval function.
4. Ontology Applications
In modern information retrieval systems, the application of ontology technology is becoming increasingly widespread, especially in semantic search and knowledge quizzing. An ontology is a formalized representation method for expressing domain knowledge. It defines a set of concepts and their interrelationships and can effectively support the semantic processing and understanding of information. Through the ontology model, semantic search and knowledge quizzing can understand the context of queries and recognize the connectivity between concepts. This enables the system to consider the hierarchical structure and logical relationships between concepts when handling complex queries, thus returning more relevant and precise information.
4.1. Examples of Semantic Search Applications
This study utilized the constructed OTMV to design a semantic search application experiment based on the metrology domain, which can search other related concepts according to the keywords and give the corresponding labels, alternative labels, and definitions. In this paper, we take the “time scale” as an example to search related concepts, and the experimental results are shown in Figure 5. From the experimental results, the semantic search can search out the concepts and definitions related to “time scale”, demonstrating its capability to perform semantic searches.
4.2. Knowledge Quiz Application Example
This study designed a knowledge quizzing experiment based on the metrology domain using OTMV, which mainly verifies the knowledge quizzing capability of OTMV to see whether it can give corresponding answers according to the questions asked. Additionally, the experiment assesses whether OTMV can deliver corresponding answers when the language of the questions is changed, i.e., whether it can respond accurately in Chinese or English.
The experimental results are shown in Figure 6. From the experimental results, the knowledge quiz can give the corresponding answers according to the questions asked and can give the corresponding answers in Chinese or English according to the questions asked in Chinese or English.
4.3. Metrology Applications
The OTMV constructed in this study contains the relevant fields from the time calibration certificates and adds semantic annotations to these fields. Digital calibration certificates are the focus of metrology digitization research and need to meet the requirements of machine readability and comprehensibility. At present, although digital calibration certificates can be machine-readable, there is still a lack of sufficient semantic information to support machine comprehensibility. This study adds relevant fields from the time calibration certificates to the time metrology vocabulary ontology and provides semantic annotations for these fields. This enables machines to use the semantic information in the ontology to perform more complex data processing and data exchange when handling information related to time calibration certificates. It also lays a certain foundation for time calibration certificates to exchange data between different systems and to realize metrological traceability. For the time calibration certificates included in the OTMV, relevant content can be found in the ontology published in Appendix B.
5. Conclusions and Future Work
This paper, based on China’s national standards, metrological technical specifications JJF1180-2007 “Glossary and Definition of Time and Frequency Metrology”, JJG 2007-2015 “Time and Frequency Measuring Instruments”, relevant materials published by the China National Institute of Metrology, etc., proposed a classification principle that meets the intrinsic requirements of time transfer in the digital world; adopted the seven-step method of ontology development along with the Protégé tool to classify classes, properties, and instances; expressed the logical relationships between time metrology vocabulary domain knowledge; and successfully constructed OTMV. A series of validation experiments were also conducted on the ontology, which verified the syntactic correctness and logical consistency of the ontology by using OOPS! and verified the machine-readable nature of the ontology by using the RDFLib library; the machine-understandable capability of the ontology was verified using the Pellete inference engine and the SWRL rule language; and the information retrieval capability of the ontology was verified using the SPARQL Query statement. Finally, an application experiment was conducted on semantic search and knowledge Q&A for this ontology, and the application of OTMV in the field of metrology was introduced. Through a series of experiments, it can be concluded that the classification principle proposed in this paper for time metrology vocabulary is feasible and can construct OTMV correctly. The construction of this ontology can solve the problem of time metrology vocabulary word expression in the digital world and lay the foundation for realizing the digitization of China’s metrology vocabulary, and its readability, comprehensibility, and sharing.
Ontology construction is an iterative process that needs to keep pace with the times, continuously updating and refining based on domain knowledge and application needs so that it can continue to be applied in further research. Although the ontology construction of the time metrology vocabulary is carried out in this paper, the method of knowledge extraction is still manual, and the manual construction of the ontology requires a lot of manpower and effort. Although the OTMV provides a systematic description of the time metrology vocabulary, which contains the concepts of the vocabulary, the relationships, etc., it needs to be further extended to cover metrology-related contents more comprehensively, for example, descriptions of experimental processes in time calibration and measurement, which is crucial for future handling of experimental processes in a machine-readable and machine-understandable manner. For the expansion of the ontology, we can use Protégé or RDFlib library to add, modify, or delete the content of the OTMV, and in the case of large metrology datasets, we can use the batch import method to realize the expansion of the OTMV, the Pellet inference engine to carry out the knowledge inference test, and the SPARQL query statement to carry out the information query testing. The performance of the ontology can also be tested by utilizing the JVM monitoring tool to detect the memory consumption of the ontology when processing large-scale data.
In addition to this, we are going to further explore how we can use semi-automated or automated approaches to improve the efficiency of ontology construction while using algorithms to enhance its accuracy, which will be a major challenge in future research. In addition, the multilingual support of OTMV needs to be further improved; we have used official documents for the description of ontologies and have not linked the everyday idiomatic usage (unofficial usage) with the official description. Moreover, the integration of OTMV with other related ontologies needs to be investigated as a way to extend the application scope and interoperability of OTMV to meet the requirements of metrology development and to further explore its application in digital transformation to support other metrology endeavors, which is also the future research work of this paper.
Conceptualization, M.D., B.G. and X.X.; methodology, M.D., B.G., X.X. and Z.L.; data curation, M.D.; writing—original draft preparation, M.D.; writing—review and editing, X.X., Z.L., S.W. and B.G.; supervision, X.X., S.W., Z.L. and Y.L.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.
The data supporting the reported results are available from the corresponding author upon reasonable request.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 3. Design of the primary object properties centered on the time scale class.
Enlarge this image.Figure 5. Semantic search experiment results. (The Chinese characters in the figure refer to alternative labels for terms in OTMV, where the primary label for terms in OTMV is in English and the alternative label is in Chinese).
Enlarge this image.Figure 6. Knowledge quiz experiment results. (The black Chinese in the figure refers to the alternative labeling of the terms in the OTMV and the Chinese definitions of the terms, and the green Chinese refers to the question statements for the knowledge quiz on the OTMV).
Hierarchical division of time service classes.
| Class | First-Level Subclass | Second-Level Subclass | Third-Level Subclass |
|---|---|---|---|
| time service | time synchronization technology | time transfer | — |
| time comparison | — | ||
| time synchronization | — | ||
| time synchronization method | satellite time synchronization | global navigation satellite system | |
| radio time service | shortwave time signal station | ||
| long-wave time signal station | |||
| mechanical time synchronization | mechanical clock | ||
| telephone time service | — | ||
| Internet time service | network time protocol |
Examples of some of the data properties and data types in OTMV.
| Data Properties | Data Types |
|---|---|
| the time code format is | xsd:string |
| has a time expressed as | xsd:dateTime |
| has a duration of | xsd:string |
| the number of days with Julius is | xsd:float |
| has an accuracy of | xsd:double/xsd:float |
| has a leap seconds of | xsd:integer |
| has timing accuracy of | xsd:double/xsd:float |
| has years | xsd:int |
Triple query results.
| Subject | Predicate | Object |
|---|---|---|
| time delay | time transfer | |
| GPS common-view | time comparison | |
| millisecond meter | time interval measuring instrument | |
| clock | time measurement | |
| IERS Bulletin | coordinated universal time | |
| time wander | time measurement | |
| modified Julian day | calendar and time representation | |
| atomic second | time scale |
Count of the 10 most frequently occurring properties.
| Property | Count |
|---|---|
| 270 | |
| 182 | |
| 167 | |
| 165 | |
| 152 | |
| 95 | |
| 78 | |
| 75 | |
| 39 | |
| 26 |
Examples of SWRL rules.
| Rule Name | Rule |
|---|---|
| S2 | global navigation satellite system (?x) ^ use(?y, ?f) -> global navigation satellite system (?y) ^ use(?x, ?f) |
OTMV inference results.
| Title | Before Inference | After Inference |
|---|---|---|
| Individuals | Galileo satellite navigation system | Galileo satellite navigation system |
| Same Individual As | Global positioning system | Global positioning system |
| Object property assertions | — | use ‘GPS common-view’ |
Appendix A
In
The main referenced standards.
| File Name |
|---|
| JJF 1180-2007 [ |
| JJG 2007-2015 [ |
| GB/T 7408.1-2023 [ |
| GB/T 42579-2023 [ |
| GB/T 39411-2020 [ |
| ISO 8601-1:2019 [ |
| ISO 8601-2:2019 [ |
| ISO 34000:2023 [ |
In
In
Enlarge this image.Figure A2. OTMV triple query results. (The non-English part of the diagram refers to the URL of the predicate in the triad).
In
In
Enlarge this image.Figure A4. OTMV information retrieval validation results. (The Chinese characters in the picture refer to the Chinese label for “Global Navigation Satellite Systems”).
Appendix B
To facilitate communication and sharing, we have posted the OTMV on Zenodo at the following link:
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; Liu, Zilong 2
; Xiong, Xingchuang 2 ; Luo, Yuqi 3 1 College of Information Engineering, China Jiliang University, Hangzhou 310018, China;
2 Center for Metrology Scientific Data, National Institute of Metrology, Beijing 100029, China;
3 The School of Optics and Electronic Technology, China Jiliang University, Hangzhou 310018, China;