A marking method, a detection method and a system for data rights confirmation

Abstract

This specification relates to the field of data security, and in particular to a marking method, a detection method and a system for data rights confirmation. The method includes adding a fingerprint subgraph to a knowledge graph in the process of generating a knowledge graph based on basic instance data and graph ontology definition data; the graph ontology definition data includes node definition data and edge definition data, and the knowledge graph includes node instances and edge instances; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, and the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated.

Description

Technical Field

[0001] This specification relates to the field of data security, and in particular to a marking method, a detection method and a system thereof for data rights confirmation. Background Art

[0002] Different platforms or different business fields have their own data. With the development of data management and data construction, it is hoped that data from multiple platforms and multiple business fields can be integrated and connected. Knowledge graph is a structured data expression method that can efficiently present the knowledge information contained in the data.

[0003] However, if the data ownership of the data holder cannot be guaranteed, its enthusiasm for knowledge sharing will be greatly reduced. Therefore, the issue of data ownership in knowledge graph sharing has become an urgent problem that needs to be solved. Summary of the invention

[0004] One of the embodiments of the present specification provides a marking method for data rights confirmation, which includes adding a fingerprint subgraph to a knowledge graph during the process of generating a knowledge graph based on basic instance data and graph ontology definition data; the graph ontology definition data includes node definition data and edge definition data, and the knowledge graph includes node instances and edge instances; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, and the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated.

[0005] One of the embodiments of the present specification provides a marking system for data rights confirmation, which includes a fingerprint subgraph adding module: used to add a fingerprint subgraph to a knowledge graph in the process of generating a knowledge graph based on basic instance data and graph ontology definition data; the graph ontology definition data includes node definition data and edge definition data, and the knowledge graph includes node instances and edge instances; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, and the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated.

[0006] One of the embodiments of the present specification provides a marking device for data rights confirmation, including a processor, wherein the processor is used to execute the above-mentioned marking method for data rights confirmation.

[0007] One of the embodiments of the present specification provides a detection method for data rights confirmation, including: obtaining query conditions for a fingerprint subgraph; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instances are generated based on the basic instance data and the graph ontology definition data, the fabricated node instances are defined by the graph ontology definition data but their data values ​​are fabricated, the query conditions include shape description information of the fingerprint subgraph; based on the query conditions of the fingerprint subgraph, one or more subgraphs with the same shape are matched from the knowledge graph; based on the similarity between the one or more subgraphs and the fingerprint subgraph, the source of the knowledge graph is determined.

[0008] One of the embodiments of the present specification provides an exploration system for data rights confirmation, including: a query condition acquisition module, used to obtain the query condition of a fingerprint subgraph; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated, and the query condition includes shape description information of the fingerprint subgraph; a subgraph matching module, used to match one or more subgraphs with the same shape from the knowledge graph based on the query condition of the fingerprint subgraph; a source exploration module, used to confirm the source of the knowledge graph based on the similarity between the one or more subgraphs and the fingerprint subgraph.

[0009] One of the embodiments of the present specification provides a detection device for data rights confirmation, including a processor, wherein the processor is used to execute the above-mentioned detection method for data rights confirmation.

[0010] One of the embodiments of the present specification provides a storage device storing a knowledge graph, wherein the knowledge graph is generated based on basic instance data and graph ontology definition data; the graph ontology definition data includes node definition data and edge definition data, and the knowledge graph includes node instances and edge instances; the knowledge graph also has a fingerprint subgraph embedded therein, wherein the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, and the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0012] Figure 1 is a schematic diagram of a knowledge sharing scenario according to some embodiments of this specification;

[0013] Figure 2 is an exemplary flow chart of a marking method for data rights confirmation according to some embodiments of this specification;

[0014] Figure 3 is an exemplary structural diagram of a knowledge graph with a fingerprint subgraph added according to some embodiments of this specification;

[0015] Figure 4 is an exemplary flow chart of a method for detecting data rights according to some embodiments of this specification;

[0016] Figure 5 is an exemplary module diagram of a marking system for data rights confirmation according to some embodiments of this specification;

[0017] Figure 6 It is an exemplary module diagram of an exploration system for data rights confirmation according to some embodiments of this specification. DETAILED DESCRIPTION

[0018] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0019] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0020] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0021] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0022] Figure 1 It is a schematic diagram of a knowledge sharing scenario according to some embodiments of this specification.

[0023] like Figure 1 As shown, the scenario 100 may include a data provider 110 , a graph platform 120 , a network 130 , and a data application 140 .

[0024] The data held by the data provider 110 may be collected from its own business. In some embodiments, the data provided by the data provider may be data used in various industries, including but not limited to the financial industry, insurance industry, Internet industry, automobile industry, catering industry, telecommunications industry, energy industry, entertainment industry, sports industry, logistics industry, medical industry, security industry, etc.

[0025] The graph platform 120 may be a platform for converting the data provided by the data provider 110 into a knowledge graph and sharing and using it. In some embodiments, the data application party 140 may obtain the knowledge graph in the platform by accessing the graph platform 120. The graph platform 120 may include a processing device of one or more processing engines (e.g., a single-core processing engine or a multi-core processing engine). As an example only, the processing device may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, etc. One or more combinations thereof. In some embodiments, the processing device may include one or more storage devices for storing data that the processing device needs to process or the result data of the processing, etc. For example, the storage device may store knowledge graphs corresponding to the data of multiple data providers, etc.

[0026] The network 130 connects the components of the system so that the components can communicate with each other. The network between the components in the system may include a wired network and / or a wireless network. For example, the network 130 may include a cable network, a wired network, an optical fiber network, a telecommunications network, an internal network, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network (ZigBee), a near field communication (NFC), a bus within a device, a line within a device, a cable connection, etc., or any combination thereof. The network connection between each two components may be in one of the above-mentioned ways, or in multiple ways.

[0027] The data application party 140 is the actual application party of the data provided by the data provider 110. In some embodiments, the data application party 140 can access the graph platform 120 through the network 130 to obtain the knowledge graph and perform applications such as graph reasoning, model training, knowledge graph reference, reuse, and secondary processing.

[0028] Different business platforms or data holders store their own data, and each business platform or data holder can record their own business data in the form of knowledge graphs or data tables. In order to improve the utilization rate of data information and break the information island, data holders can provide their own business data to the graph platform as data providers. The graph platform usually integrates and processes the business data provided by the data provider to obtain graph data (or knowledge graph) and share it, so that other platforms or institutions that are data users can use it to improve business efficiency.

[0029] However, the process of knowledge graph sharing often involves citing or reusing other existing knowledge graphs, or even reprocessing the original knowledge graphs, which makes data ownership confirmation in the process of knowledge graph sharing a problem.

[0030] Data rights confirmation is used to indicate that after data is open and shared, such as in knowledge reference, reuse, and secondary processing, the source or ownership of the data is determined. Among them, knowledge reference is to extract part or all of other existing knowledge graphs for the current task. Knowledge reuse refers to the use of knowledge by extracting part or all of multiple existing knowledge graphs and fusing them to obtain a new knowledge graph. Secondary processing involves more in-depth knowledge application, which can specifically add or delete attributes or standardize the format of nodes or edges in the knowledge graph, fuse nodes, or establish new edges between nodes. It is not difficult to understand that these methods of knowledge application will change the appearance of the original knowledge graph to varying degrees, making it difficult to confirm the source of the data. Taking secondary processing as an example, if the graph platform performs secondary processing on a part of the existing knowledge graph for the needs of knowledge application (which may be at the request of the data application party), some attribute information of the nodes in the processed knowledge graph will change, which greatly increases the difficulty of data rights confirmation.

[0031] In view of this, the embodiments of this specification provide a marking method for data rights confirmation, which adds fingerprint subgraphs in the process of generating a knowledge graph from data to facilitate subsequent identification, thereby achieving data rights confirmation.

[0032] Before introducing the labeling method for data rights confirmation, we first introduce the definitions and technologies related to knowledge graphs.

[0033] Knowledge graph refers to a knowledge base composed of a series of node instances (such as data instances corresponding to entities and attributes) and the relationships between node instances (edge ​​instances). Among them, entities can refer to tangible objects in the physical world, such as people, cars, merchants, etc., or intangible objects, such as words, songs, movies, funds, program codes, etc.; attributes can be gender, age, region, etc. Data instances can be actual examples corresponding to the abstract concept of entities, such as people can be Zhang San, Li Si, Li Ming, etc., songs can be "Blue and White Porcelain", "Nightingale", "Swan Lake", merchants can be merchant A, merchant B, merchant C, etc.; age can be 18 years old, 30 years old, etc. There can be relationships between node instances, for example, merchant A and merchant B have business dealings, merchant C is a sub-merchant of merchant A, Zhang San is the manager of merchant A, etc. In the data instances corresponding to entities, the relationship between node instances can also be regarded as the relationship between corresponding entities, such as people and merchants can have a management relationship or employment relationship.

[0034] The knowledge graph may correspond to ontology definition data, or the schema of the knowledge graph. The ontology definition data of the knowledge graph includes node definition data and edge definition data in the knowledge graph. In some embodiments, the ontology definition data of the knowledge graph also includes graph operators.

[0035] The ontology definition data of the knowledge graph can guide the collection of data instances, and construct graphs based on data instances to obtain a knowledge graph (also called an instance graph). The node definition data in the ontology definition data of the knowledge graph can define the node types contained in the knowledge graph and the attribute fields under each type of node. For example, the node definition data can define the nodes of the user type contained in the knowledge graph, and the node of this type includes attribute fields such as age and gender. Without causing confusion, the node type can be referred to as a node, such as a user node, a merchant node, etc. The aforementioned Zhang San and Li Si can be regarded as instance data under the user node, and can also be called a node instance. The edge definition data can define the type of edge between nodes and the attribute fields of the edge of this type. For example, there can be an edge of employment relationship between the employee node and the company node, which can further include an attribute field of employment nature, and its specific attribute values ​​can include temporary employment, formal employment, etc. Through the edge definition data, when constructing the knowledge graph, it can be determined whether there is an edge between two node instances. The graph operator is used to find the node instances corresponding to each node in the knowledge graph from a large number of data instances based on the node definition data and / or edge definition data and determine the relationship between the node instances. Graph operators can also be understood as graph computing algorithms or methods, which are data processing operations or calculations used for graph construction, and can include attribute standardization operators, link establishment operators, entity fusion operators, etc. Graph operators can be implemented in various ways such as data processing / calculation units, program codes, machine learning models, etc. In some embodiments, data can be input to the operator, and the operator can perform corresponding data processing / calculation, complete the data transformation, and output the transformed data. In some embodiments, the graph operator can be regarded as an algorithm or method based on the ontology definition data (including entity definition and relationship description) of the knowledge graph, and can also be regarded as a part of the ontology definition data. It should be noted that the knowledge graph containing instance data can also be called a data graph or graph data. Without causing confusion, the node instance can also be referred to as a node, and the edge instance can also be referred to as an edge.

[0036] Figure 2 It is an exemplary flow chart of a marking method for data authentication according to some embodiments of this specification.

[0037] Figure 3 This is an exemplary structural diagram of a knowledge graph with a fingerprint subgraph added according to some embodiments of this specification.

[0038] refer to Figure 2 In some embodiments, the labeling method 200 for data authentication includes: adding a fingerprint subgraph 230 to the knowledge graph 240 in the process of generating a knowledge graph based on basic instance data 210 and graph ontology definition data 220.

[0039] As can be seen from the above introduction to the knowledge graph, in some embodiments, generating a knowledge graph can be considered as processing the basic instance data 210 based on the ontology definition data 220 of the graph, thereby generating a graph 240. Figure 3 , the gray nodes, black nodes and the edges between them in the knowledge graph 300 are all generated based on the basic instance data. In some embodiments, the basic instance data 210 can be Figure 1 The business data provided by the data provider 110 in the illustrated scenario 100 .

[0040] In some embodiments, the fingerprint subgraph 230 includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances. Figure 3 , the black node 310 in the knowledge graph 300 is a seed node, and the hollow nodes 320 around the black node 310 and connected to it are fabricated nodes.

[0041] In other words, the seed node instance is generated based on the basic instance data 210, which has actual knowledge value. The node instance with actual business or knowledge value is included in the fingerprint subgraph to ensure that the fabricated nodes in the fingerprint subgraph can be "attached" to the knowledge graph to avoid being filtered out in the knowledge application link. In some embodiments, the number of seed nodes in the fingerprint subgraph 230 can be greater than one, such as including two, three or four seed node instances, and multiple seed node instances can be connected to each other or not.

[0042] In some embodiments, the fabricated node instance is defined by the graph ontology definition data 220, but its data value is fabricated. The fabricated node instance is defined by the graph ontology definition data 220, so that the fabricated node instance has the appearance of a normal node instance in the knowledge graph, further avoiding being filtered out in the knowledge application link. Exemplarily, for a fabricated node, its type can be a type existing in the graph node definition data, such as "gender", but its data value is fabricated. For example, the data value corresponding to the type "gender" can be "20 years old". Obviously, "20 years old" is not a regular data value of "gender". Therefore, in some scenarios, it is possible to determine whether the node instance is a fabricated node instance by obtaining the values ​​of some types or attribute types in the fabricated node instance. It can be understood that the less explainable the difference between the data value of the fabricated node instance and the basic data in the basic instance data 210, the more non-repudiation it has when determining whether the node instance is a fabricated node instance. It should be noted that the data values ​​of the above fabricated node instances are only examples. In some embodiments, the data values ​​may include any characters, such as standard characters and special characters. For example, the data value may be "g!joT#". Figure 1In scenario 100 , the fabricated data values ​​may be set by the data provider 110 or generated by the graph platform 120 .

[0043] In some embodiments, the number of fabricated node instances is a preset value, and / or a preset number of edge connections are set between fabricated node instances, so that the fingerprint subgraph 230 has a preset shape. In some embodiments, the preset shape includes a polygon, a regular polygon, a rhombus, a snowflake, and a horseshoe.

[0044] like Figure 3 As shown in the figure, the number of fabricated node instances is 4, and the fabricated node instances, in addition to the 4 edges connected to the seed node instances respectively, also include 4 edges for connecting to two adjacent fabricated nodes. The fabricated node instances and edges in the figure make the fingerprint subgraph 230 have a diamond shape. It is easy to know that in some embodiments, the fingerprint subgraph can also be made into a pentagon or a five-pointed star shape by fabricating 5 node instances and 5 edges. In some embodiments, edges can be set only between the fabricated node instances and the seed node instances, such as by connecting 18 fabricated node instances to the seed node instances to construct a snowflake fingerprint subgraph. The fingerprint subgraph has a special preset shape, which can further increase non-repudiation.

[0045] In some embodiments, similar to the fabricated node instance, the edge instance between the seed node instance and the fabricated node instance or the edge instance between the fabricated node instances is defined by the graph ontology definition data 220, but its data value is fabricated. Exemplarily, the graph ontology definition data 220 for the edge instance defines an edge with an employment relationship type between the employee node and the company node; if the seed node instance belongs to the company node and the fabricated node instances all belong to the employee node, then the edge instance between the seed node instance and the fabricated node instance can be an employment relationship type, and its corresponding data value can be "1111111". Through this setting method, when the data user refers to or reprocesses the knowledge graph 240, the probability of the node instance being eliminated in the fingerprint subgraph 230 is reduced. According to the description of the above embodiment, it can be understood that the fabricated node instance or its related edge instance has no knowledge value, and its role is only to increase the non-repudiation of the source of the proof data. In some embodiments, considering the application of knowledge graphs, the data application party can partially reference or filter the knowledge graph according to business needs. In order to avoid the fingerprint subgraph being reasonably filtered out, multiple node instances with real knowledge value can be selected in the knowledge graph as seed node instances, and then multiple fingerprint subgraphs can be evenly embedded in the knowledge graph. Exemplarily, several node instances can be selected from each type of node instance as seed nodes to construct a fingerprint subgraph.

[0046] In some embodiments, the preset shape of the fingerprint subgraph and the fabricated data value can be provided by a basic data instance provider (such as Figure 1 The data provider 110 in the example is set to facilitate data exploration. For example, in some embodiments, the basic data entity provider can provide a preset shape and a fabricated data value when providing a basic data instance. For more information about data exploration, please refer to the following text. Figure 4 Related description.

[0047] In some embodiments, the preset shapes and fabricated data values ​​may also be provided by the platform (e.g. Figure 1 The atlas platform 120 in is automatically generated.

[0048] In some embodiments, the knowledge graph obtained based on the above-mentioned marking method can be stored in a storage device (such as a processing device or storage medium of a graph platform). Since the fabricated nodes in the fingerprint subgraph do not have real knowledge value, but have the appearance of nodes or edges in the knowledge graph (such as type), the fingerprint subgraph is not easily filtered out in the knowledge application (such as reference, reuse or secondary processing, etc.) link, and will not affect the normal use of the knowledge graph.

[0049] Figure 4 It is an exemplary flow chart of an exploration method for data authentication according to some embodiments of this specification.

[0050] One or more steps in the exploration method for data authentication can be performed by Figure 1 The data provider 110 or the graph platform 120 performs the method, and the process 400 of the method includes:

[0051] Step 410, obtaining the query condition of the fingerprint subgraph. In some embodiments, step 410 may be performed by a query condition obtaining module.

[0052] The fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instances are generated based on the basic instance data and the graph ontology definition data, and the fabricated node instances are defined by the graph ontology definition data but their data values ​​are fabricated.

[0053] In the fingerprint subgraph, the edge instance between the seed node instance and the fabricated node instance or the edge instance between the fabricated node instances is defined by the graph ontology definition data but its data value is fabricated. In some embodiments, more information about the fingerprint graph can be found in Figure 3 The relevant description in will not be repeated here.

[0054] In some embodiments, the query condition includes the shape description information of the fingerprint subgraph. In some embodiments, the query condition may also include the identifier or specific data value of the node instance and / or edge instance. In some embodiments, the shape description information of the fingerprint subgraph may include the seed node instance in the fingerprint subgraph, the number and type of the fabricated node instance, and the connection relationship (edge) of the aforementioned node instance.

[0055] Step 420, based on the query condition of the fingerprint subgraph, one or more subgraphs with the same shape are matched from the knowledge graph. In some embodiments, step 420 may be performed by a subgraph matching module.

[0056] In some embodiments, matching subgraphs with the same shape in a knowledge graph can be implemented based on existing subgraph matching methods, and this specification does not impose any limitations.

[0057] Step 430, based on the similarity between the one or more subgraphs and the fingerprint subgraph, confirm the source of the knowledge graph. In some embodiments, step 430 can be performed by a source detection module.

[0058] In some embodiments, when the similarity between at least one of the subgraphs and the fingerprint subgraph satisfies a preset condition, it is determined that the source of the knowledge graph is related to the setting party of the fingerprint subgraph.

[0059] In some embodiments, the similarity can be a value between 0 and 1. The preset condition can be a judgment threshold (such as 0.7, 0.85, etc.). When the similarity is greater than the judgment threshold, it can be considered that the explored knowledge graph contains data in the knowledge graph with the embedded fingerprint subgraph, that is, it is related to the setting party of the fingerprint subgraph, thereby realizing data confirmation.

[0060] According to the relevant description of the fingerprint subgraph, in addition to having a preset shape, the values ​​of its node instances and / or edge instances are also specific. Therefore, in some embodiments, the similarity between the subgraph obtained by the query and the fingerprint subgraph is jointly determined based on the shape similarity and the data value similarity of the node instances and / or edge instances.

[0061] In some embodiments, shape similarity is used to characterize the similarity in shape between the fingerprint subgraph and the matched subgraph. For example, if the fingerprint subgraph is a pentagonal snowflake, and the subgraph matched from the knowledge graph based on the query condition is also a pentagonal snowflake, then the two can be considered to have similar shapes. In some embodiments, shape similarity can be a value between 0 and 1, where 0 is different and 1 is the same.

[0062] In some embodiments, if the shape and instance data of the matched subgraph are exactly the same as those of the fingerprint subgraph, that is, the similarity is 100%, then the source of the knowledge graph can be determined without doubt. However, in some embodiments, in order to improve the exploration efficiency, it is not necessary to compare all the instance data of the subgraph with the instance data of the fingerprint subgraph. Exemplarily, the data exploration party provides the data values ​​of 10 node instances and edge instances in the fingerprint subgraph. When it is determined that the data values ​​of 8 node instances and edge instances in the matched subgraph are the same as the provided data values, it can be determined that the similarity of the data values ​​of the two is 80%. Generally speaking, the shape of the subgraph obtained by subgraph matching is the same as the fingerprint subgraph, that is, the shape matching degree is 100%. Finally, the similarity can be jointly determined to be 80% (such as taking the lowest value of the two). If the preset condition is that the similarity is not less than 0.7, it can be determined that the source of the explored knowledge graph is related to the setting party of the fingerprint subgraph.

[0063] In some embodiments, the preset condition may also be that the data value of a node instance or edge instance is a non-conventional field. For example, in the above example, when the data value corresponding to the "gender" of a node instance in the subgraph is "g!joT#", it can be foreseen that the data value is unlikely to be obtained by chance or provided by other data providers. Therefore, it can be considered that the source of the knowledge graph is related to the setting party of the fingerprint subgraph.

[0064] It should be noted that the above description of the marking method 200 and process 400 for data rights confirmation is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the marking method 200 and process 400 for data rights confirmation under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0065] Figure 5 It is an exemplary module diagram of a marking system for data authentication according to some embodiments of this specification.

[0066] like Figure 5 As described above, in some embodiments, the marking system 500 for data authentication may include a fingerprint subgraph adding module 510 .

[0067] The fingerprint subgraph adding module 510 can be used to add a fingerprint subgraph to a knowledge graph in the process of generating a knowledge graph based on basic instance data and graph ontology definition data; the graph ontology definition data includes node definition data and edge definition data, and the knowledge graph includes node instances and edge instances; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, and the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated.

[0068] In some embodiments, more description about fingerprint subgraphs can be found in Figure 2 And the related description will not be repeated here.

[0069] Figure 6 It is an exemplary module diagram of an exploration system for data rights confirmation according to some embodiments of this specification.

[0070] like Figure 6 As described above, in some embodiments, the exploration system 600 for data rights confirmation may include a query condition acquisition module 610 , a subgraph matching module 620 , and a source exploration module 630 .

[0071] The query condition acquisition module 610 can be used to obtain the query condition of the fingerprint subgraph; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated, and the query condition includes the shape description information of the fingerprint subgraph.

[0072] In some embodiments, more descriptions about the query conditions can be found in step 410 and its related descriptions, which will not be repeated here.

[0073] The subgraph matching module 620 can be used to match one or more subgraphs having the same shape from the knowledge graph based on the query condition of the fingerprint subgraph.

[0074] In some embodiments, more descriptions about matching one or more subgraphs can be found in step 420 and related descriptions, which will not be repeated here.

[0075] The source detection module 630 can be used to determine the source of the knowledge graph based on the similarity between the one or more subgraphs and the fingerprint subgraph.

[0076] In some embodiments, more descriptions about the similarity can be found in step 430 and its related descriptions, which will not be repeated here.

[0077] It should be understood that Figure 5 and Figure 6 The system 500, system 600 and its modules shown can be implemented in various ways. For example, in some embodiments, the device and its modules can be implemented by hardware, software or a combination of software and hardware. Among them, the hardware part can be implemented by dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution device, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and devices can be implemented using computer executable instructions and / or included in a processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the device and its modules of this specification. Not only can the hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, they can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (for example, firmware).

[0078] It should be noted that the above description of the system 500 and the system 600 and their modules is only for convenience of description and does not limit the present specification to the scope of the embodiments. It is understandable that, after understanding the principle of the system, those skilled in the art may arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from the principle. For example, in some embodiments, Figure 6 The sub-graph matching module 610 and the source detection module 620 disclosed in the specification can be different modules in a system, or a module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the protection scope of this specification.

[0079] The beneficial effects that may be brought about by the embodiments of this specification include but are not limited to: (1) by adding a fingerprint subgraph to the knowledge graph, data rights can be confirmed by exploring the subgraph; (2) the nodes and edges in the fingerprint subgraph are determined based on the graph ontology definition data, making it difficult to be discovered or filtered. The fingerprint subgraph has a special shape and data value, making it non-repudiable, thereby ensuring the effect of data rights confirmation.

[0080] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0081] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0082] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0083] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0084] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0085] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0086] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0087] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A marking method for data rights confirmation, comprising adding a fingerprint subgraph to a knowledge graph in the process of generating a knowledge graph based on basic instance data and graph ontology definition data; the graph ontology definition data comprises node definition data and edge definition data, and the knowledge graph comprises node instances and edge instances; the node definition data is used to define the node types contained in the knowledge graph and the attribute fields under each type of node, and the edge definition data is used to define the type of edge between nodes and the attribute fields of the edge of this type; the node instance is an entity or data instance, and the edge instance is the relationship between the node instances; the fingerprint subgraph is configured to determine that the source of the knowledge graph is related to the setting party of the fingerprint subgraph when one or more subgraphs with the same shape are matched from the knowledge graph based on a query condition, and the similarity between at least one of the subgraphs and the fingerprint subgraph meets a preset condition; The fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instances are generated based on the basic instance data and the graph ontology definition data, and the fabricated node instances are defined by the graph ontology definition data but their data values ​​are fabricated.

2. The method of claim 1, wherein: The number of fabricated node instances is a preset value, and / or a preset number of edge connections are set between the fabricated node instances, so that the fingerprint subgraph has a preset shape.

3. The method of claim 2, wherein: The preset shapes include a regular polygon, a rhombus, a snowflake and a horseshoe.

4. The method according to claim 1 or 2, wherein: The edge instances between the seed node instances and the fabricated node instances or the edge instances between the fabricated node instances are defined by the graph ontology definition data but their data values ​​are fabricated.

5. The method of claim 4, wherein: Preset shapes and fabricated data values ​​are set by the underlying data instance provider.

6. A marking system for data rights confirmation, comprising a fingerprint subgraph adding module: used to add a fingerprint subgraph to a knowledge graph in the process of generating a knowledge graph based on basic instance data and graph ontology definition data; the graph ontology definition data includes node definition data and edge definition data, and the knowledge graph includes node instances and edge instances; the node definition data is used to define the node types contained in the knowledge graph and the attribute fields under each type of node, and the edge definition data is used to define the type of edge between nodes and the attribute fields of the edge of this type; the node instance is an entity or data instance, and the edge instance is an entity or data instance. The relationship between the node instances; the fingerprint subgraph is configured to determine that the source of the knowledge graph is related to the setting party of the fingerprint subgraph when one or more subgraphs with the same shape are matched from the knowledge graph based on the query conditions, and the similarity between at least one of the subgraphs and the fingerprint subgraph meets the preset conditions; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, and the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated.

7. A marking device for data rights confirmation, comprising a processor, wherein the processor is used to execute the marking method for data rights confirmation according to any one of claims 1 to 5.

8. A method for detecting data rights, comprising: Acquire a query condition of a fingerprint subgraph; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; The seed node instance is generated based on the basic instance data and the graph ontology definition data, the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated, and the query condition includes the shape description information of the fingerprint subgraph; Match one or more subgraphs with the same shape from the knowledge graph based on the query conditions of the fingerprint subgraph; Based on the similarity between the one or more subgraphs and the fingerprint subgraph, the source of the knowledge graph is determined; wherein, when the similarity between at least one of the subgraphs and the fingerprint subgraph meets a preset condition, it is determined that the source of the knowledge graph is related to the setting party of the fingerprint subgraph.

9. The method as claimed in claim 8, wherein in the fingerprint subgraph, the edge instance between the seed node instance and the fabricated node instance or the edge instance between the fabricated node instances is defined by the graph ontology definition data but its data value is fabricated.

10. The method according to claim 8 or 9, wherein the similarity is determined jointly based on shape similarity and data value similarity of node instances and / or edge instances.

11. A data rights detection system, comprising: A query condition acquisition module, used to acquire the query condition of a fingerprint subgraph; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; The seed node instance is generated based on the basic instance data and the graph ontology definition data, the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated, and the query condition includes the shape description information of the fingerprint subgraph; A subgraph matching module is used to match one or more subgraphs with the same shape from the knowledge graph based on the query conditions of the fingerprint subgraph; A source detection module, used to determine the source of the knowledge graph based on the similarity between the one or more subgraphs and the fingerprint subgraph; The source detection module is also used to determine that the source of the knowledge graph is related to the setting party of the fingerprint subgraph when the similarity between at least one of the subgraphs and the fingerprint subgraph meets a preset condition.

12. A detection device for data rights confirmation, comprising a processor, wherein the processor is used to execute the detection method for data rights confirmation according to any one of claims 8 to 10.

13. A storage device storing a knowledge graph, wherein the knowledge graph is generated based on basic instance data and graph ontology definition data; the graph ontology definition data includes node definition data and edge definition data, and the knowledge graph includes node instances and edge instances; the node definition data is used to define the node types contained in the knowledge graph and the attribute fields under each type of node, and the edge definition data is used to define the types of edges between nodes and the attribute fields of the edges of this type; the node instance is an entity or data instance, and the edge instance is the relationship between the node instances; The knowledge graph is also embedded with a fingerprint subgraph, which is configured to determine that the source of the knowledge graph is related to the setting party of the fingerprint subgraph when one or more subgraphs with the same shape are matched from the knowledge graph based on query conditions and the similarity between at least one of the subgraphs and the fingerprint subgraph meets a preset condition; the fingerprint subgraph includes one or more seed node instances and one or more fabricated node instances connected to the seed node instances; the seed node instance is generated based on the basic instance data and the graph ontology definition data, and the fabricated node instance is defined by the graph ontology definition data but its data value is fabricated.

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