A data traceability verification method in data element circulation

Abstract

The present invention discloses a data traceability verification method in data element circulation, which includes: according to data equity attributes, data with equity attributes are divided into public data, enterprise data and personal data; data with equity attribute characteristics are abstractly represented as equity data elements composed of object classes, characteristics and representations; based on the equity data elements, PROV‑O and ProVOC data traceability models are integrated in combination with the data element circulation process and the data right confirmation system to obtain the PROV‑OCC data traceability model; after the W7 model is integrated with the PROV‑OCC data traceability model, semantic representation is performed to describe the circulation status and traceability status record of the equity data element in the equity data circulation instance; the traceability process in the equity data circulation instance is encoded, and the encoded ontology is reasoned and traced. The present invention realizes the automation and intelligence of equity data traceability, and improves the reliability and scalability of traceability.

Description

Technical Field

[0001] The present invention relates to the technical field of data traceability, and in particular to a data traceability verification method in the circulation of data elements. Background Art

[0002] Data elements are data and their derivative forms that can be put into production as resources after being sorted and processed according to demand in production and business activities. Their circulation, as a key link in the data element market, not only realizes data sharing, but also promotes the flow of data resources and the release of value. According to the GlobalData report, the global data and analysis market value in 2022 is US$100.84 billion, and it is expected to grow at a compound annual growth rate (CAGR) of 13.36%, reaching US$188.78 billion by 2027. As important carriers of data element circulation, big data exchanges and social media platforms involve the exchange of rights and interests of multiple data subjects, and need to ensure the compliance of data sources and ownership. Therefore, it is urgent to build data and its ownership traceability models and technologies to ensure the reliability and sustainability of the data ecosystem.

[0003] The OPM model is a universal data provenance model that realizes the interchangeability of data traceability information between different systems from a technical perspective, and supports the coexistence of multiple systems under different descriptions, including the three concepts of entity, process, and agent. Traceability is usually represented by a directed acyclic graph; it is used in the source awareness system PADS for recording publications, semantic-based data traceability methods, etc.

[0004] The Provenir model is a model that expresses workflow provenance information based on three main classes: Data, Process, and Agent. It has been applied in fields such as oceanography and biomedicine.

[0005] The CRMdig model is an extended model based on the CIDOC-CRM ontology model and can be implemented in RDFS. It captures the modeling and requirements about the source of digital objects through physical objects, digital objects, actors, times and places, and connects objects and devices that process objects.

[0006] The PROV model is a data provenance model standard published by W3C, including three W3C standards PROV-O, PROV-DM, PROV-N and PROV-CONSTRAINTS and 8 W3C records, totaling 12 files; the core concepts include entity, activity and agent, as well as multiple relationships, and detailed explanations on how users can obtain, use and verify data provenance information and exchange and represent data provenance information through multiple formats such as OWL. The PROV model is currently the most common and complete data provenance model. Scholars have analyzed the generation mechanism of the model, optimized the design of the model, and widely used it in the fields of geographic space provenance, structural changes in metadata models, data traceability in software development processes, and Web application scenarios.

[0007] ProVOC is a flexible and lightweight data provenance description model proposed based on the PROV model. The model consists of three first-level class components: data, activities, and execution entities. It adopts common expression and description methods such as directed acyclic graphs, classes, and association relationships, and is deeply applied in the fields of health and medical big data, smart libraries, etc.

[0008] OPM, PROV, etc. usually describe the historical status of data, lacking a unified traceability information publishing and access mechanism; the PROV-O model uses RDF data types to describe traceability data in an ontological manner, but its ability to express complex data processing processes is limited; the ProVOC model supports flexible expansion but has a narrow scope of application, and its universality and management effect need to be further verified and evaluated; the existing technology lacks a data traceability model that fits the data element circulation field and supports ownership change detection, and cannot meet the growing data element market's demand for a safer and more standardized circulation environment; although the existing data traceability models have been applied to multiple scenarios and fields such as medicine, biology, machinery, and computer technology, there has been no in-depth research on the characteristics and needs of data element circulation, especially in the process of data circulation involving the traceability of changes in data ownership, which is still a research gap. The OPM model is not abstract enough when describing a specific type of traceability process; the PROV-O model supports ontological description but has limited complex data processing capabilities; the ProVOC model supports flexible expansion but has poor universality. Summary of the invention

[0009] In view of this, the present invention provides a data traceability verification method in the circulation of data elements, combines the data element circulation process with the corresponding data confirmation system, improves classic data traceability models such as PROV-O and ProVOC, and establishes the first ontological equity data traceability model PROV-OCC (Ontology-Circulation Confirmation) for the circulation of major data elements and the paradigm of ownership change scenarios in data transactions through the definition of data and model concepts with common equity attribute characteristics and their relationships.

[0010] The present invention discloses a data traceability verification method in data element circulation, which comprises:

[0011] Step 1: Divide the data rights confirmation system into five dimensions: holding rights, collection rights, management rights, use rights, and income rights, according to the nature of rights intertwined in the five processes of data collection, data processing, data storage, data transaction, and data use in the circulation of data elements. Divide the subjects of data rights confirmation into three categories: public data, enterprise data, and personal data. Describe the scope of data rights and responsibilities of different data rights confirmation subjects in each dimension.

[0012] Step 2: According to the data rights and interests attributes, the data with rights and interests attributes are divided into public data, enterprise data and personal data; the data with rights and interests attributes are abstractly represented as rights and interests data elements consisting of object class, characteristics and representation;

[0013] Step 3: Based on the equity data element, the PROV-O and ProVOC data traceability models are integrated in combination with the data element flow process and the data ownership confirmation system to obtain the PROV-OCC data traceability model; the PROV-OCC data traceability model consists of three parent classes: equity data element, activity and entity;

[0014] Step 4: After integrating the W7 model with the PROV-OCC data traceability model, a semantic representation is performed to describe the circulation status and traceability status record of the equity data element in the equity data circulation instance; the traceability process in the equity data circulation instance is encoded, and the encoded ontology is reasoned and traced.

[0015] Furthermore, before step 1, the method further includes:

[0016] Based on the data lifecycle model, we extract the concepts of multiple circulation processes and cluster them into multiple categories. The top N categories in terms of word frequency are general processes that conform to real logic and represent the various links in the circulation of data elements. The top N categories in terms of word frequency include data collection, data processing, data storage, data transaction and data usage.

[0017] Furthermore, the step 2 comprises:

[0018] Data with equity attributes are packaged into data packets in big data exchanges and organized into data blocks on social media platforms; data packets are data products formed through cleaning, preprocessing, analysis and processing, and are traded and supervised by big data exchanges; data blocks are data that generate value or benefits when published, forwarded or received on social media platforms; data blocks have equity attributes, enabling data producers and publishers, platforms and regulators, recipients and forwarders, and users to share data and gain value; equity attributes include buying, selling, transferring and redeeming.

[0019] Furthermore, the step 3 comprises:

[0020] The object class of equity data elements is a data packet and data block of abstract concepts. Each object has unique characteristics, including attributes, relationships and tradability. Attributes are used to describe the characteristics of equity data elements, which include variable attributes and immutable attributes. Variable attributes include time, space and data rights; immutable attributes include name and number. Relationships are used to reflect the connection between equity data elements and entities and activities, which includes ownership, generation and use. Tradability is used to measure the circulation and exchange capabilities of equity data elements in the data element market. Representation is the value domain of the characteristic, describing its expression method, and each characteristic has only one representation.

[0021] Furthermore, there is a one-to-one correspondence between object classes and characteristics. A rights and interests data element concept is composed of rights and interests data objects, namely data packets and data blocks, and their unique characteristics. Multiple rights and interests data element concepts and multiple different representations constitute rights and interests data elements.

[0022] Furthermore, the step 3 also includes:

[0023] Equity data elements are tradable data elements with equity attributes, including data packets in big data exchange scenarios and data blocks in online social media; activities refer to a series of processes that accompany the circulation of equity data elements in the circulation of data elements, including data collection, data processing, data storage, data trading and data use and their sub-processes; entities include service platforms and service objects, and service platforms and service objects are divided into individuals, enterprises and public institutions; in the circulation of data elements, attributes are used to describe the basic characteristics and status of entities, activities and equity data elements, among which attributes are divided into variable attributes and immutable attributes; in the circulation of data elements on a single platform, activities are used as immutable attributes to record changes in equity data elements; in the circulation of data elements on multiple platforms, activities are used as variable attributes to record changes in the same activity of equity data elements on each platform.

[0024] Furthermore, in the PROV-OCC data traceability model, the three parent classes of entity, activity and equity data element and their corresponding subclasses are interconnected through object attributes to form the core structure of the model. There are authorized object attributes between the entity's subclass service platform and service objects; there are derived object attributes between the two equity data elements; the object attributes between the three categories of entity, equity data element and activity are expanded from the following aspects: entities and activities are interrelated; entities own, use and generate equity data elements; activities affect equity data elements; and at the same time, entities, activities and equity data elements all have attributes.

[0025] Furthermore, the W7 model is integrated with the PROV-OCC data traceability model to perform semantic representation, including:

[0026] The W7 model is composed of seven interrelated elements: "What", "When", "Where", "How", "Who", "Which" and "Why", each of which can be used to track events that affect data. In the PROV-OCC data traceability model, "What" refers to activities, that is, a series of processes that accompany the flow of equity data elements in the circulation of data elements, including data collection, data storage, data trading, data processing, data use and their sub-processes; "How" refers to the object attributes that connect the three parent classes of equity data elements, activities and entities; "Who" refers to the agent that causes the event, that is, the service objects and service platforms in the two scenarios of big data exchanges and online social media; "When" refers to the time point and time period of each activity; "Where" refers to the space where the activity occurs, that is, the place where the equity data element changes; "Which" is used to express the data ownership that may exist in the entity that changes with the event, including the right to use, the right to collect, the right to benefit, the right to manage and the right to hold; "Why" refers to the reason for conducting traceability activities, that is, to determine the compliance of the equity data element and the authenticity and accuracy of the content.

[0027] Furthermore, the encoding of the traceability process in the equity data circulation instance includes:

[0028] The OWL2 language is used to encode the seven interrelated elements of "What", "When", "Where", "How", "Who", "Which" and "Why" after the integration of the W7 model and the PROV-OCC data traceability model, as well as the relationships between the elements, to facilitate data traceability query and reasoning.

[0029] Furthermore, the reasoning and source tracing verification of the encoded ontology includes:

[0030] After the PROV-OCC data traceability model is ontologized based on SWRL reasoning rules, the conflict relationships between entities, activities and equity data elements and between them and instances are imported into the Pellet reasoning engine. The Pellet reasoning engine is used to deeply mine the implicit logical relationship between classes and instances, and verify the ability and reliability of the PROV-OCC data traceability model to trace equity data.

[0031] Due to the adoption of the above technical solution, the present invention has the following advantages:

[0032] (1) The concept of “equity data element” is proposed, and the data elements in the circulation of data elements are classified according to their equity attributes. The definition, characteristics and classification of equity data elements are clarified, providing a new perspective and dimension for data traceability.

[0033] (2) This method integrates the mainstream data traceability models PROV-O and ProVOC with the data element circulation process and data ownership confirmation, and proposes a model PROV-OCC that can realize data traceability and detect ownership changes in data element circulation. It is integrated with the W7 traceability technology to provide a technical framework and semantic representation model for equity data traceability that includes activities, relationships, entities, time, space, data ownership and traceability reasons, thereby achieving effective traceability of the source, change, impact and responsibility of equity data elements.

[0034] (3) This method ontologizes the equity data traceability model, cites typical examples in social network media scenarios, and graphically displays the application process of the equity data traceability model. It also uses the OWL2 language to encode the elements and relationships of the ontological model, and uses SWRL reasoning rules and the Pellet reasoning engine to reason and verify the traceability ontology, thus realizing the automation and intelligence of equity data traceability and improving the reliability and scalability of traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 This is a schematic diagram of the universal circulation process of equity data elements according to an embodiment of the present invention;

[0037] Figure 2 A conceptual diagram of the rights and interests data element of an embodiment of the present invention;

[0038] Figure 3A schematic diagram of the core structure of the PROV-OCC model according to an embodiment of the present invention;

[0039] Figure 4 A schematic diagram of the PROV-OCC model ontology according to an embodiment of the present invention;

[0040] Figure 5 It is a schematic diagram of an instantiation of equity data traceability of the fusion of W7 and PROV-OCC models in an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram of SWRL rules after being imported into Protégé according to an embodiment of the present invention;

[0042] Figure 7 This is a schematic diagram of the inference result that the service object A in the embodiment of the present invention is the original author of the equity data element V;

[0043] Figure 8 This is a schematic diagram of the inference result that the service object B in the embodiment of the present invention is the secondary creator of the equity data element V;

[0044] Fig. 9 This is a schematic diagram of the inference result that the service object C in the embodiment of the present invention is the secondary creator of the equity data element V;

[0045] Fig.10 This is a schematic diagram of reasoning results in which the service object G of the embodiment of the present invention is the person whose data rights are infringed. DETAILED DESCRIPTION

[0046] The present invention is further described in conjunction with the accompanying drawings and embodiments, and the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field should fall within the scope of protection of the embodiments of the present invention.

[0047] See also Figure 1 The present invention provides an embodiment of a data traceability verification method in data element circulation, which includes:

[0048] 1. Data element circulation process and its ownership confirmation system

[0049] 1.1 Definition of data element flow process

[0050] (1) Process concept based on data lifecycle model reuse

[0051] The circulation of data elements is generally the process of data providers and demanders transferring data according to certain rules. It can be regarded as a subset of the data life cycle that focuses on data transfer and exchange. The main data life cycle models include ANDS (the Australian National Data Service), BLM (Bureau Of Land Management), CSA (the Cloud Security Alliance), DataONE (the Data Observation Network for Earth), DCC (the Digital Curation Centre), DDI (The Data Documentation Initiative), DigitalNZ (Digital New Zealand), Ecoinformatics, Generic Science, Geospatial, UK Data Archive, and the models proposed by scholars Siddiqa, Qin Shun, and Xing Wenming. Based on these 13 reusable data lifecycle models, 92 concepts of circulation processes were extracted and clustered into 10 categories. The top five categories in terms of word frequency are common processes that conform to real logic and also represent various links in the circulation of data elements, including data collection, data processing, data storage, data transaction and data use, as shown in Table 1.

[0052] Table 1. Frequency of terms and typical sub-items of data life cycle concept clustering

[0053]

[0054] (2) Concept of data element flow process based on application scenario analysis

[0055] The definition of the data element circulation process also needs to be combined with real application scenarios to ensure its effectiveness. The "Data Elements White Paper (2023)" released by the China Academy of Information and Communications Technology (CAICT) mentioned that big data exchanges and social media platforms are two representative data element circulation carriers and key scenarios for changes in data rights and interests.

[0056] In the big data exchange scenario, when both parties of data supply and demand enter the big data trading platform, they first apply to register as members to better provide and obtain data. The data supplier must first conduct compliance testing and quality control on its own data, and after passing the review, sign a contract with BDTE and upload the data to the platform. The big data exchange backs up, pre-processes, packages and publishes the uploaded data for data demanders to browse and query. After the information of the transaction parties is reviewed and a contract is signed, the big data exchange will also provide a test data environment and clearing and settlement services to ensure the security of data circulation.

[0057] In the social media scenario, data suppliers publish content on the platform. After the platform reviews the data quality and credibility, it saves and uploads the original data to the cloud server. After the platform standardizes the data, it matches the content with the characteristics of the data demander, publishes and pushes it to the target audience, creates data value, and gives corresponding shares and subsidies to data suppliers based on indicators such as page views and likes. At the same time, data demanders can contact stakeholders through the platform, apply for data use rights and sign contracts to conduct secondary processing and creation of data.

[0058] Integrating the process of data element circulation in the above scenarios, the present invention summarizes the universal process of rights and interests data element circulation, which is as follows: When both parties of data supply and demand enter the platform, they need to sign an agreement to protect the privacy and rights of the data subject and regulate the use of data. The platform reviews the data provided by the data supplier. After the review is passed, the original data is collected and backed up for the first time. The platform then standardizes the original data internally and regularly backs up the processed data to prevent data loss. The platform integrates and publishes the standardized data, and at the same time interacts and matches the information of both parties of data supply and demand, plays its intermediary role, and meets the needs of data demanders. In order to obtain the intended data or part of its usage rights, the data demander will transfer the rights and interests of the data released by the platform, including purchasing data from the big data exchange and effectively browsing, forwarding, liking, commenting on the data in social media, and obtaining partial data usage rights. After the platform signs a contract with the data demander, it delivers the data to the demander and conducts fund settlement with the data supplier in accordance with the contract to complete the data transaction. In addition, the data supplier can also perform secondary processing on the data according to the provisions of the contract. See the circulation process. Figure 1 .

[0059] (3) The connotation of the data element flow process that integrates the data lifecycle model and application scenarios

[0060] Data collection: refers to obtaining raw data elements from various types of data sources. The source of the data needs to be clearly defined, and legal and compliant technologies need to be used to obtain the data. The acquired data needs to be subject to compliance testing and quality control to ensure the availability, accuracy, completeness and consistency of the data. The collection process records the source of the data, the date of collection, the collection method and other information for subsequent use, traceability and auditing.

[0061] Data storage: refers to the storage and management of collected data and related contracts in the data transaction process in a structured or semi-structured manner. For example, create data indexes to facilitate retrieval and data reprocessing; regularly back up data and agreements to ensure effective recovery and prevent disputes when data is lost or damaged.

[0062] Data processing: refers to the standardized processing of raw data, such as data encryption, processing, maintenance, format conversion, etc., to meet specific needs or applications. Common data processing methods include data cleaning, data conversion, data aggregation, etc. In the process of data processing, relevant data protection and data privacy policies must be observed, including protecting sensitive information, obtaining data use permission, etc., to ensure the compliance of data element circulation.

[0063] Data transaction: refers to the act of transferring data elements from one entity to another through contracts, agreements, etc. for the purpose of value exchange between different entities, including transaction preparation, transaction matchmaking, transaction contract signing, delivery and settlement, etc. Before data transaction, both parties need to clarify the content, structure, ownership and value of data elements, and formulate contracts or agreements on this basis to determine matters such as delivery method, use conditions, ownership change, price and time limit.

[0064] Data use: refers to the use of acquired data as a production factor in the fields of business, scientific research, military, and people's livelihood, thereby realizing the value of data. Data use includes reading data, writing data, analyzing data, visualizing data, mining data, and secondary processing of data.

[0065] The above data element circulation process involves changes in data ownership. Data ownership confirmation can improve the compliance and privacy protection of data element circulation, and can further improve the traceability and controllability of data.

[0066] 1.2 The ownership confirmation system for data element circulation

[0067] This invention uses the "five-power separation" method based on ternary data subjects to confirm the rights of circulating data. The core idea is to divide the data rights confirmation system into five dimensions: holding rights, collection rights, management rights, use rights, and income rights according to the rights nature intertwined in the five processes of data collection, processing, storage, transaction, and use in the circulation of data elements, and divide the data rights confirmation subjects into three categories: public data, corporate data, and personal data, describing the scope of data rights and responsibilities and obligations of different data subjects in each dimension. The relevant evidence retrieved includes 30 Policies & Regulations from the World Constitution Website, the American Law Search Network, Curia, Peking University Law Treasure, etc.; 23 Scholars' views in academic journals from core databases such as Web of Science and CNKI; and a total of 1,215 judicial cases obtained using keywords such as "data rights" and "data disputes" in Curia, CourtListener, China Judgment Documents Network, etc. The specific content of the data rights confirmation system is shown in Table 2.

[0068] Table 2 Data ownership confirmation system based on the ternary data subject's 'five rights separation'

[0069]

[0070] 2. Data traceability model based on data element circulation and confirmation of ownership

[0071] 2.1 Definition of Equity Data Element

[0072] In the process of data element circulation, big data exchanges and social media platforms are important data circulation carriers. In order to protect the rights and interests of data subjects, it is necessary to introduce data types with rights and interests attributes. According to the rights and interests attributes of data, it is divided into public data, enterprise data and personal data. Public data is collected, opened and shared by government departments or public institutions; enterprise data is data generated in the production and operation process of enterprises or obtained through collection and processing; personal data is divided into data provided and created by individuals and privacy-related information data. This kind of data with rights and interests attributes is packaged into data packets in big data exchanges and organized into data blocks on social media platforms. Data packets are data products formed by cleaning, preprocessing, analysis and processing, and are traded and supervised by big data exchanges; while data blocks are data that generates value or benefits when published, forwarded or received on social media platforms. For example, Douyin, Toutiao, YouTube, Facebook and Twitter encourage the creation of high-quality content through revenue sharing. Data blocks have rights and interests attributes such as buying, selling, transferring and exchanging, so that data producers and publishers, platforms and regulators, receivers and forwarders and users can all realize data sharing and value benefits.

[0073] The present invention abstractly represents the above-mentioned data with equity attribute characteristics as "equity data element". It consists of object class, characteristics and representation. The object class of equity data element is a data packet and data block of abstract concepts. Each object has its own characteristics, such as attributes, relationships and tradability. Attributes are used to describe the characteristics of equity data elements, including variable attributes such as time, space, data rights and invariant attributes such as name and number. Relationships are used to reflect the connection between equity data elements and entities and activities, including ownership, generation, use, etc. Tradability is used to measure the circulation and exchange ability of equity data elements in the data element market, including quality, value, degree, etc. Representation is the value domain of the characteristic, describing its expression method, and each characteristic has only one representation.

[0074] See the concept of equity data element Figure 2 ,Each data packet or data block has corresponding number, time and other characteristics. The number can be represented by "letters and numbers" and the time can be represented by "DATE". There is a one-to-one correspondence between object classes and characteristics. An equity data element concept is composed of equity data objects, namely data packets and data blocks, and their unique characteristics. Multiple equity data element concepts and multiple different representations constitute equity data elements.

[0075] 2.2 Traceability model design

[0076] Based on the definition of equity data elements, the present invention integrates the PROV-O and ProVOC data traceability models in combination with the data element circulation process and the data confirmation system, and designs the PROV-OCC (Ontology-Circulation Confirmation) model. The model consists of three parent classes: equity data elements, activities, and entities. As shown in Table 3, these three classes form the basis of the data traceability model and are used to create traceability descriptions of other subclasses. Equity data elements are tradable data elements with equity attributes, including data packets in big data exchange scenarios and data blocks in online social media; activities refer to a series of processes (events) that accompany the circulation of equity data elements in the circulation of data elements, including major processes such as data collection and data storage and their sub-processes; entities include service platforms and service objects, and service objects and service platforms are divided into individuals, enterprises, and public institutions. During the circulation of data elements, attributes are used to describe the basic characteristics and status of entities, activities and equity data elements. Attributes are divided into variable attributes (traceability attributes) and immutable attributes. During the circulation of data elements on a single platform, activities serve as immutable attributes to record changes in equity data elements; during the circulation of data elements on multiple platforms, activities serve as variable attributes to record changes in the same activity of equity data elements on each platform.

[0077] Table 3 Concepts of PROV-OCC data traceability model

[0078]

[0079] In the PROV-OCC model, there are 35 object attributes between classes, and their names, explanations, and attribute facets are shown in Table 4.

[0080] Table 4 Statistics of object attributes of PROV-OCC data traceability model

[0081]

[0082] In the PROV-OCC model, the three parent classes of entity, activity and equity data elements and their corresponding subclasses are connected to each other through object attributes to form the core structure of the model. Figure 3As shown in the figure, there are authorization object attributes between the subclasses of entities, such as the data supplier authorizing the data trading platform; there are derived object attributes between two equity data elements, such as the equity data element can derive a new equity data element. The object attributes between the three categories of entities, equity data elements, and activities are developed from the following aspects: entities and activities are related to each other; entities own, use, and generate equity data elements; activities affect equity data elements, such as generation, deletion, and addition; at the same time, entities, equity data elements, and activities all have attributes.

[0083] This paper uses protégé5.5 to visualize the PROV-OCC model, showing the hierarchical structure and relationship between the parent class and its subclasses. Due to space limitations, only three layers of ontology are shown, such as Figure 4 shown.

[0084] 3. Data traceability model verification

[0085] 3.1 Semantic Representation of the Fusion of W7 and PROV-OCC Models

[0086] The W7 model is used to perform semantic analysis of the PROV-OCC data traceability model. The W7 model is a traceability technology based on the annotation of the entire life cycle of data traceability. It is composed of seven interrelated elements: "What", "When", "Where", "How", "Who", "Which" and "Why". Each element can be used to track events that affect data. In the constructed PROV-OCC model, "What" as the basic component of the W7 model, that is, activities, refers to a series of processes (events) that accompany the circulation of equity data elements in the circulation of data elements, including data collection, data storage, data transactions, data processing, data use and other processes and their sub-processes; "How" refers to the object attributes that connect the three parent classes of equity data elements, activities and entities, such as having, revising, processing, etc.; "Who" refers to the agent that causes the event, which plays a certain role in the event and makes a certain contribution; in the scenario, it refers to the service objects and service platforms in the two scenarios of big data exchanges and online social media, specifically individuals, enterprises and public institutions; "When" refers to the time when each activity occurs. "Where" refers to the space where the activity occurs, that is, the place where the equity data element changes, such as the IP address of the service object, the network space of the service platform, etc. "Which" is used to express the program or tool used in the event. In the PROV-OCC model, "Which" is used to express the data ownership of the entity that may change with the event, including the right to use, the right to collect, the right to benefit, the right to manage and the right to hold. "Why" refers to the reason for the traceability activity, that is, to determine the compliance of the equity data element, the authenticity and accuracy of the content, and whether the equity data element has infringed the rights and interests. The semantic representation is shown in Table 5.

[0087] Table 5 Semantic representation of the fusion of W7 and PROV-OCC models

[0088]

[0089] 3.2 Traceability example verification of the fusion of W7 and PROV-OCC models

[0090] 3.2.1 Tracing Examples

[0091] In order to verify the effectiveness of the integration of W7 and PROV-OCC models, a positive public welfare data circulation example "Data dissemination of donations for mountain children's education" on China's mainstream social media platforms in 2023 was selected for traceability model verification. Case content: In March 2022, a public welfare organization (with 30,000 followers) launched a public welfare activity called "Donate loving books to children in mountainous areas" and released a related promotional video "Loving books convey a warm future" on a social platform. The video called on everyone to donate books to schools and students in remote mountainous areas. In order to further promote the activity, blogger Li Moumou (with 5 million followers) released a promotional version of the video through secondary creation in April 2022, and attached some self-made incentive elements. The short video was quickly widely circulated and forwarded in large numbers, with more than 100,000 likes, which enabled more users to participate in the donation activity. In July 2022, another public welfare blogger Zhang Moumou (with 1 million followers) also recreated and released the video. Through this video, he called on the public to pay attention to the education issues in mountainous areas and combined it with offline public welfare activities, which achieved good social response. In February 2023, as the activity progressed, the mountain school reported to the relevant charity platforms and organizations that the donated books had been successfully distributed and the donated funds were used to purchase teaching equipment. To ensure the transparency and effectiveness of each donation data, charity organizations and social platforms use W7 and PROV-OCC models to trace the data and data ownership of all donation activities throughout the process to ensure compliance and transparency in the data circulation process.

[0092] 3.2.2 Traceability description and coding

[0093] The graph instantiation of the W7 and PROV-OCC fusion model shows the circulation status and traceability status record of the equity data element, see Figure 5 .

[0094] (1) Case description

[0095] The service object "A" (a public welfare organization) created an original video titled "Books of Love Deliver a Warm Future" on the service platform "a certain platform", which is the equity data element "V". "A" has the data rights of "V" (rights of possession, use, collection, income, and management). The video shows public welfare information about mountain schools and students, and calls on the public to donate books of love. The relevant data rights of mountain schools belong to the mountain education institution "G". In the data collection activity, "A" collected relevant data of mountain schools and displayed it in the video. In the data storage activity, "A" published "V" on "a certain platform" and shared the proceeds according to the platform's public welfare promotion plan. At this time, "G" has partial income rights of "V", and "a certain platform" has management rights and partial income rights of "V".

[0096] In the data trading activity, "a certain platform" recommended "V" to service objects "B" (Li XX) and "C" (Zhang XX). In the data use activity, "B" and "C" browsed "V" and obtained "V's" authorization. At this time, "B" and "C" have the right to collect and use "V". In the data processing activity, "B" and "C" performed secondary processing on "V" and created new videos "V-1" and "V-2" respectively. At this time, "B" and "C" have the data rights of "V-1" and "V-2" respectively.

[0097] In the data trading activities, "B" and "C" published "V-1" and "V-2" on "a certain platform" and obtained certain public welfare promotion benefits from it. "G" and "A" have partial income rights of "V-1" and "V-2", and "a certain platform" has partial income rights and management rights of "V-1" and "V-2". "A certain platform" continuously recommends "V", "V-1" and "V-2" to a large number of service objects for viewing and reposting, so that these videos have been widely disseminated and received public welfare attention. At this time, the service objects participating in the reposting have the right to use and partial income rights of "V", "V-1" and "V-2", and the service objects who browse have partial income rights of "V", "V-1" and "V-2", and "a certain platform" has the management rights and partial income rights of "V", "V-1" and "V-2".

[0098] After the charity event ended, in order to ensure the transparent flow of donated funds and materials, "a certain platform" and the charity organization used the W7 and PROV-OCC models to trace the rights and interests data of "V", "V-1" and "V-2", identify the data propagation path and ownership, and ensure the compliance, authenticity and transparency of all donation information.

[0099] (2) Encoding the traceability process using the W7 model and ontology language OWL2

[0100] Encode the elements and relationships of the PROV-OCC rights data traceability model using W7 technology with the OWL2 language to facilitate data traceability queries and reasoning. The following is the main content of the encoding:

[0101] "Who": Describes the entities in the PROV-OCC model, namely the service platform "a certain platform" and the service object "individual", encoded as follows:

[0102] <!-- http: / / www.PROV-OCC.com# a certain platform -->

[0103] <owl:class rdf:about="http: / / www.PROV-OCC.com#某平台">

[0104] <rdfs:subclassof rdf:resource="http: / / www.PROV-OCC.com#服务平台：企业" / >

[0105] < / owl:class>

[0106] "How": Describes the relationships between entities, rights data elements, and activities in the PROV-OCC model, such as: generated by hasGeneratedBy, published by hasPublishedBy, profit shared by hasProfitsSharedBy, browsed by hasBrowsedBy, recommended through hasRecommendedThrough, added by hasAddedBy, reprinted by hasReprintedBy, encoded as follows:

[0107] <!-- http: / / www.PROV-OCC.com#hasIncomeRight -->

[0108] <owl:objectproperty rdf:about="http: / / www.PROV-OCC.com#hasIncomeRight">

[0109] <rdfs:subPropertyOfrdf:resource="http: / / www.w3.org / 2002 / 07 /

[0110] owl#topObjectProperty" / >

[0111] <rdf:type rdf:resource="http: / / www.w3.org / 2002 / 07 / owl#SymmetricProperty" / >

[0112] <rdfs:domain rdf:resource="http: / / www.PROV-OCC.com#实体" / >

[0113] <rdfs:range rdf:resource="http: / / www.PROV-OCC.com#权益数据元" / >

[0114] < / owl:objectproperty>

[0115] "What": Describes the activities in the PROV-OCC model, namely the process of data element flow. In the activity description "data transaction", service objects "A", "B", "C" and the service platform "a certain platform" respectively publish and recommend public welfare videos "V-1" and "V-2" to more service objects, encoded as follows:

[0116] <!-- http: / / www.PROV-OCC.com# data transaction: published / recommended V-1, V-2 by a certain platform -->

[0117] <owl:namedindividual rdf:about="http: / / www.PROV-OCC.com#数据交易：由某平台发布 / 推荐V-1、V-2">

[0118] <rdf:type rdf:resource="http: / / www.PROV-OCC.com#数据交易" / >

[0119] <www:hasassociatedwith rdf:resource="http: / / www.PROV-OCC.com#A" / >

[0120] <www:hasassociatedwith rdf:resource="http: / / www.PROV-OCC.com#B" / >

[0121] <www:hasassociatedwith rdf:resource="http: / / www.PROV-OCC.com#C" / >

[0122] <www:hasassociatedwith rdf:resource="http: / / www.PROV-OCC.com#某平台" / >

[0123] < / owl:namedindividual>

[0124] "When": Describes the time period when activities occur in the PROV-OCC model and the time when rights data elements change (such as generation, publication, secondary processing, etc.). For example, the start and end time periods for the rights data elements "V", "V-1", "V-2" to spread on "a certain platform" are from January 2023 to August 2023. Encoded as follows:

[0125] <!-- http: / / www.PROV-OCC.com#V -->

[0126] <owl:namedindividual rdf:about="http: / / www.PROV-OCC.com#V">

[0127] <www:hasEndsAtrdf:datatype="http: / / www.w3.org / 2001 /

[0128] XMLSchema#decimal">2023.8

[0129] <www:hasspace> A platform< / www:hasspace>

[0130] <www:hasStartAtrdf:datatype="http: / / www.w3.org / 2001 /

[0131] XMLSchema#decimal">2023.1

[0132] < / owl:namedindividual>

[0133] "Where": Describes the location where activities occur in the PROV - OCC model and the storage location of entitlement data elements. In this scenario, the location where activities occur is on the service platform "a certain platform", and the storage location of entitlement data elements is on the personal terminals of service recipients, the cloud of the service platform, etc. For example, in the "data storage" activity, "V" is stored in the cloud server of "a certain platform" and is released by a certain platform. The encoding is as follows:

[0134] <!-- http: / / www.PROV-OCC.com#Data storage: Store V on a certain platform and release V by a certain platform -->

[0135] <owl:namedindividual rdf:about="http: / / www.PROV-OCC.com#数据存储：在某平台存储V、由某平台发布V">

[0136] <rdf:type rdf:resource="http: / / www.PROV-OCC.com#数据存储" / >

[0137] <www:hasassociatedwith rdf:resource="http: / / www.PROV-OCC.com#A" / >

[0138] <www:hasassociatedwith rdf:resource="http: / / www.PROV-OCC.com#G" / >

[0139] <www:hasassociatedwith rdf:resource="http: / / www.PROV-OCC.com#某平台" / >

[0140] <www:hasspace> "A certain platform" cloud server< / www:hasspace>

[0141] < / owl:namedindividual>

[0142] "Which": Describes the data ownership of entities in each activity of the PROV - OCC model. In this scenario, the entities related to data ownership mainly include service recipients "A", "B", "C", "G" and the service platform "a certain platform"; data ownership includes the right of use, the right of income, the right of collection, the right of management and the right of possession. For example, when service recipient "A" generates entitlement data element "V" in the "data aggregation" activity, "A" has all the data rights of "V". The provenance description and encoding are as follows:

[0143] <!-- http: / / www.PROV-OCC.com#A -->

[0144] <owl:namedindividual rdf:about="http: / / www.PROV-OCC.com#A">

[0145] <rdf:type rdf:resource="http: / / www.PROV-OCC.com#数据存储" / >

[0146] <www:hasCollectionRightrdf:resource="http: / / www.PROV-OCC.com#V" / >

[0147] <www:hasincomeright rdf:resource="http: / / www.PROV-OCC.com#V" / >

[0148] <www:hasManagementRightrdf:resource="http: / / www.PROV-OCC.com#V" / >

[0149] <www:hasownershipright rdf:resource="http: / / www.PROV-OCC.com#V" / >

[0150] <www:hasuseright rdf:resource="http: / / www.PROV-OCC.com#V" / >

[0151] < / owl:namedindividual>

[0152] "Why": Describes the reason for using the PROV - OCC model for provenance activities. In this scenario, "a certain platform" traces and identifies possible data ownership problems and provides them as evidence to the management parties of relevant public welfare projects and judicial organs.

[0153] 3.2.3 Provenance Verification Based on Ontology Reasoning

[0154] This method uses SWRL reasoning rules and Pellet reasoning engine to perform reasoning and traceability verification on the ontology after the W7 and PROV-OCC model fusion encoding. SWRL reasoning rules are an ontology-based rule language that can be used to define rules similar to Horn logic to enhance the reasoning and expression capabilities of the ontology. Based on SWRL reasoning rules, the conflict relationships between classes and between classes and instances after the PROV-OCC model ontology are handled. The ontology and SWRL rule descriptions are imported into the Pellet reasoning engine in the protégé software. The Pellet reasoning engine is used to deeply mine the implicit logical relationship between classes and instances, and the ability and reliability of the PROV-OCC model to trace equity data are verified.

[0155] This method constructs SWRL inference rules for equity data traceability in social media scenarios and imports them into the Protégé rule library, such as Figure 6 As shown in Table 6, x and v are instances of entity and equity data elements in the ontology, respectively, and hasIncomeRight, hasCollectionRight, hasManagementRight, hasUseRight, hasOwnershipRight, hasPartialIncomeRight, and hasAssociatedWith are object attributes.

[0156] Table 6 Some SWRL rules for equity data traceability

[0157]

[0158] This method imports the entities, activities and equity data elements of the traceability cases involved in the PROV-OCC model into the instance layer of the ontology, maps them with the ontology, and then adds object attributes and data attributes according to the actual situation. On this basis, according to the designed SWRL reasoning rules, the Pellet reasoning engine is used for knowledge reasoning and implicit knowledge mining. The reasoning results obtained include that the service object "A" is the original creator of the equity data element "V" (the public welfare organization that publishes the love video), "B" and "C" are the secondary creators of the equity data element (the platform service objects of the secondary creation of the love video), and "G" is the data right owner (mountain education institution). Figures 7 to 10 As shown in the figure, the reasoning results successfully traced the source object of the infringing data and the victim object of the infringed data rights, verified the consistency and integrity of the ontology, and further proved the ability and reliability of the PROV-OCC model to trace the rights and interests data.

[0159] This invention proposes the concept of "equity data element" for the first time, classifies the data elements in the circulation of data elements according to their equity attributes, clarifies the definition, characteristics and classification of equity data elements, and provides a new perspective and dimension for data traceability.

[0160] This paper integrates the mainstream data traceability models PROV-O and ProVOC with the data element circulation process and data ownership confirmation, and proposes a model PROV-OCC that can realize data traceability and detect ownership changes in data element circulation. It is integrated with the W7 traceability technology to provide a technical framework and semantic representation model for equity data traceability, including activities, relationships, entities, time, space, data ownership and traceability reasons, thereby achieving effective traceability of the source, change, impact and responsibility of equity data elements.

[0161] The present invention ontologizes the equity data traceability model, cites typical examples in social network media scenarios, graphically displays the application process of the equity data traceability model, uses the OWL2 language to encode the elements and relationships of the ontological model, and adopts SWRL reasoning rules and Pellet reasoning engine to reason and verify the traceability ontology, thus realizing the automation and intelligence of equity data traceability and improving the reliability and scalability of traceability.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A data traceability verification method in data element circulation, characterized in that: include: Step 1: Divide the data rights confirmation system into five dimensions: holding rights, collection rights, management rights, use rights, and income rights, according to the nature of rights intertwined in the five processes of data collection, data processing, data storage, data transaction, and data use in the circulation of data elements. Divide the subjects of data rights confirmation into three categories: public data, enterprise data, and personal data. Describe the scope of data rights and responsibilities of different data rights confirmation subjects in each dimension. Step 2: According to the data rights and interests attributes, the data with rights and interests attributes are divided into public data, enterprise data and personal data; the data with rights and interests attributes are abstractly represented as rights and interests data elements consisting of object class, characteristics and representation; Step 3: Based on the equity data element, the PROV-O and ProVOC data traceability models are integrated in combination with the data element flow process and the data ownership confirmation system to obtain the PROV-OCC data traceability model; the PROV-OCC data traceability model consists of three parent classes: equity data element, activity and entity; Step 4: After integrating the W7 model with the PROV-OCC data traceability model, a semantic representation is performed to describe the circulation status and traceability status record of the equity data element in the equity data circulation instance; Encode the traceability process in the equity data circulation instance, and perform reasoning and traceability verification on the encoded ontology; The step 3 comprises: The object class of equity data element is a data packet and data block of abstract concepts. Each object has unique characteristics, including attributes, relationships and tradability. Attributes are used to describe the characteristics of equity data elements, including variable attributes and invariant attributes. Variable attributes include time, space and data rights. Invariant attributes include name and number. Relationships are used to reflect the connection between equity data elements and entities and activities, including ownership, generation and use. Tradability is used to measure the circulation and exchange capabilities of equity data elements in the data element market. Representation is the value domain of the characteristic, describing its expression method. Each characteristic has only one representation. After the W7 model is integrated with the PROV-OCC data traceability model, semantic representation is performed, including: The W7 model is composed of seven interrelated elements: "What", "When", "Where", "How", "Who", "Which" and "Why", each of which can be used to track events that affect data. In the PROV-OCC data traceability model, "What" refers to activities, that is, a series of processes that accompany the flow of equity data elements in the circulation of data elements, including data collection, data storage, data trading, data processing, data use and their sub-processes; "How" refers to the object attributes that connect the three parent classes of equity data elements, activities and entities; "Who" refers to the agent that causes the event, that is, the service objects and service platforms in the two scenarios of big data exchanges and online social media; "When" refers to the time point and time period of each activity; "Where" refers to the space where the activity occurs, that is, the place where the equity data element changes; "Which" is used to express the data ownership that may exist in the entity that changes with the event, including the right to use, the right to collect, the right to benefit, the right to manage and the right to hold; "Why" refers to the reason for conducting traceability activities, that is, to determine the compliance of the equity data element and the authenticity and accuracy of the content.

2. The method according to claim 1, characterized in that Before step 1, the method further includes: Based on the data lifecycle model, we extract the concepts of multiple circulation processes and cluster them into multiple categories. The top N categories in terms of word frequency are general processes that conform to real logic and represent the various links in the circulation of data elements. The top N categories in terms of word frequency include data collection, data processing, data storage, data transaction and data usage.

3. The method according to claim 1, characterized in that The step 2 comprises: Data with equity attributes are packaged into data packets in big data exchanges and organized into data blocks on social media platforms; data packets are data products formed through cleaning, preprocessing, analysis and processing, and are traded and supervised by big data exchanges; data blocks are data that generate value or benefits when published, forwarded or received on social media platforms; data blocks have equity attributes, enabling data producers and publishers, platforms and regulators, recipients and forwarders, and users to share data and gain value; equity attributes include buying, selling, transferring and redeeming.

4. The method according to claim 1, characterized in that: There is a one-to-one correspondence between object classes and characteristics. A rights and interests data element concept is composed of rights and interests data objects, namely data packets and data blocks, and their unique characteristics. Multiple rights and interests data element concepts and multiple different representations constitute rights and interests data elements.

5. The method according to claim 1, characterized in that The step 3 also includes: Equity data elements are tradable data elements with equity attributes, including data packets in big data exchange scenarios and data blocks in online social media; activities refer to a series of processes that accompany the circulation of equity data elements in the circulation of data elements, including data collection, data processing, data storage, data trading and data use and their sub-processes; entities include service platforms and service objects, and service platforms and service objects are divided into individuals, enterprises and public institutions; in the circulation of data elements, attributes are used to describe the basic characteristics and status of entities, activities and equity data elements, among which attributes are divided into variable attributes and immutable attributes; in the circulation of data elements on a single platform, activities are used as immutable attributes to record changes in equity data elements; in the circulation of data elements on multiple platforms, activities are used as variable attributes to record changes in the same activity of equity data elements on each platform.

6. The method according to claim 1, characterized in that In the PROV-OCC data traceability model, the core structure of the model is composed of the three parent classes of entity, activity and equity data element and their corresponding subclasses connected to each other through object attributes. There are authorized object attributes between the entity's subclass service platform and service objects; there are derived object attributes between the two equity data elements; the object attributes between the three categories of entity, equity data element and activity are expanded from the following aspects: entities and activities are interrelated; entities own, use and generate equity data elements; activities affect equity data elements; at the same time, entities, activities and equity data elements all have attributes.

7. The method according to claim 6, characterized in that The encoding of the traceability process in the equity data circulation instance includes: The OWL2 language is used to encode the seven interrelated elements of "What", "When", "Where", "How", "Who", "Which" and "Why" after the integration of the W7 model and the PROV-OCC data traceability model, as well as the relationships between the elements, to facilitate data traceability queries and reasoning.

8. The method according to claim 1, characterized in that The reasoning and source tracing verification of the encoded ontology includes: After the PROV-OCC data traceability model is ontologized based on SWRL reasoning rules, the conflict relationships between entities, activities and equity data elements and between them and instances are imported into the Pellet reasoning engine. The Pellet reasoning engine is used to deeply mine the implicit logical relationship between classes and instances, and verify the ability and reliability of the PROV-OCC data traceability model to trace equity data.

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