Node level identification system and method for knowledge graph and mind map

Abstract

The invention relates to the technical field of new-generation information, in particular to a node hierarchical identification system and method for a knowledge graph and a mind map, and solves the problems of uniqueness and expansibility of identification of nodes in the mind map and the knowledge graph in a complex application scene by constructing a hierarchical marking structure and a flexible multi-field combination. The method has the following remarkable advantages that rapid retrieval and positioning in a complex scene are supported; field types and hierarchical relationships can be adjusted according to needs; new fields are allowed to be added to adapt to the multi-scene application; the method is suitable for cross-platform multi-user data fusion and cooperation based on the knowledge graph and the mind map, and in this way, the technical problems that in the prior art, the knowledge graph and the mind map are insufficient in node uniqueness, weak in node relevance and poor in expansibility and compatibility are solved.

Description

Technical Field

[0001] The present invention relates to the field of new generation information technology, and in particular to a node level identification system and method for knowledge graphs and mind maps. Background Art

[0002] Knowledge graphs and mind maps are two important technologies for organizing and expressing complex information. In knowledge graphs, nodes usually represent entities or concepts, and edges represent the relationships between these entities or concepts. Mind maps, on the other hand, intuitively display the logical relationships of information through a hierarchical structure of nodes and branches.

[0003] However, when existing knowledge graphs and mind map technologies identify nodes, when referencing related nodes across multiple knowledge graphs or mind maps or in other applications, different nodes are easily confused due to unclear identification, which affects the efficiency of retrieval and analysis. At the same time, the expression of the associated information between nodes is not intuitive enough, and it is difficult to meet the visualization requirements in multiple scenarios. In addition, existing identification methods often lack flexibility, are difficult to adapt to a variety of complex application scenarios, and have poor compatibility with different systems or platforms.

[0004] In summary, existing knowledge graphs and mind maps have problems such as insufficient node uniqueness, weak node correlation, and poor scalability and compatibility. Summary of the invention

[0005] The purpose of the present invention is to provide a node hierarchy identification system and method for knowledge graphs and mind maps, aiming to solve the technical problems of insufficient node uniqueness, weak node correlation, and poor scalability and compatibility in the knowledge graphs and mind maps in the prior art.

[0006] To achieve the above-mentioned purpose, the present invention adopts a node level identification system for knowledge graphs and mind maps, including a hierarchical node marking module, a marking management module, a marking recognition module and a marking association module, wherein the marking management module is connected to the hierarchical node marking module, the marking recognition module and the marking association module are both connected to the marking management module, and the marking association module is also connected to the marking recognition module; The hierarchical node marking module is used to set different components and hierarchical relationships of node markings in different scenarios; The tag management module is responsible for the generation, rewriting and management of tags based on the rules set by the hierarchical node tag module; The tag association module is used to establish a mapping relationship between node tags and nodes in a mind map or knowledge graph in different scenarios; The tag recognition module locates the node from the tag according to the mapping relationship between the tag and the node established by the tag association module.

[0007] Among them, the different components and hierarchical relationships of the node tags set by the hierarchical node tagging module refer to the node tags being composed of different fields, each field representing a different entity, and these entities having inclusion or subordination relationships to form hierarchical relationships between different fields; the field type is not limited to a specific form, and the field type is set according to needs.

[0008] The tag management module is used to generate a unique tag by combining the unique identifiers of entities corresponding to different fields in the tag and their hierarchical relationships; At the same time, the tag management module is also used to reasonably modify the fields corresponding to the corresponding entities according to the needs of the application scenario to ensure the uniqueness of the identification.

[0009] The tag association module uses the node tags to establish a one-to-one correspondence with the associated nodes.

[0010] The tag recognition module parses the devices corresponding to different fields and their hierarchical relationships according to the tags or uses the one-to-one mapping relationship between tags and nodes and the combination of these two methods to efficiently retrieve and locate the nodes represented by the tags in complex application scenarios.

[0011] The present invention also provides a node level identification method for knowledge graphs and mind maps, which is applied to the node level identification system for knowledge graphs and mind maps as described above. The steps include: Based on the hierarchical node marking module, for different scenarios, according to the attributes and hierarchical relationships of the nodes, different components and hierarchical relationships of the node markings are set; Using the tag management module, generating or updating a unique node identification tag according to a preset rule or a modification request; Using the tag association module, accurate association between node tags and nodes in a mind map or knowledge graph is achieved; Through the tag recognition module, the tags are parsed to extract fields with hierarchical relationships to achieve the purpose of locating nodes step by step or locating nodes by tags according to a one-to-one mapping relationship between tags and nodes.

[0012] Wherein, when the tag management module is used to generate a unique node identification tag according to a preset rule or a modification request: the tag management module is used to combine the unique identifications of entities corresponding to different fields in the tag and their hierarchical relationships to generate a unique node identification; When the tag management module is used to update a unique node identification tag according to a preset rule or a modification request: the tag management module is used to modify a certain field in the node tag to generate a new unique node identification.

[0013] Among them, in the process of locating nodes, when solving node identification in more complex scenarios, the tag identification module is used to parse the devices and entities corresponding to different fields and their hierarchical relationships according to the tags and the one-to-one mapping relationship between the tags and the nodes.

[0014] The node level identification system and method for knowledge graphs and mind maps of the present invention solve the uniqueness and scalability problems of node identification in mind maps and knowledge graphs in complex application scenarios by constructing a hierarchical tag structure and flexible multi-field combinations, and has the following significant advantages: supporting rapid retrieval and positioning in complex scenarios; adjusting field types and hierarchical relationships as needed; allowing new fields to be added to adapt to multi-scenario applications; and being suitable for cross-platform multi-user data fusion and collaboration based on knowledge graphs and mind maps, thereby solving the technical problems of insufficient node uniqueness, weak node correlation, and poor scalability and compatibility in the prior art knowledge graphs and mind maps. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a principle block diagram of the node level identification system for knowledge graphs and mind maps of the present invention.

[0017] Figure 2 It is a schematic diagram of each field of the mind map node mark of the present invention.

[0018] Figure 3 It is a schematic diagram of the tag generation method and rewriting method of the present invention.

[0019] 1-Hierarchical node marking module, 2-marking management module, 3-marking identification module, 4-marking association module. DETAILED DESCRIPTION

[0020] Embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0021] See also Figure 1 , Figure 1 It is a principle block diagram of the node level identification system for knowledge graphs and mind maps of the present invention.

[0022] The present invention provides a node level identification system for knowledge graphs and mind maps, comprising a hierarchical node marking module 1, a marking management module 2, a marking identification module 3 and a marking association module 4, wherein the marking management module 2 is connected to the hierarchical node marking module 1, the marking identification module 3 and the marking association module 4 are both connected to the marking management module 2, and the marking association module 4 is also connected to the marking identification module 3; The hierarchical node marking module is used to set different components and hierarchical relationships of node markings in different scenarios; The tag management module 2 is responsible for the generation, rewriting and management of tags based on the rules set by the hierarchical node tag module; The tag association module 4 is used to establish associations with nodes in a mind map or knowledge graph using node tags in different scenarios, and to establish a one-to-one correspondence with the nodes using different fields in the tags and combinations of fields.

[0023] The tag recognition module 3 locates the node according to the tag based on the mapping relationship between the tag and the node established by the tag association module; The different components and hierarchical relationships of the node tags set by the hierarchical node tag module refer to that the node tags are composed of different fields, each field represents a different entity, and these entities have inclusion or subordination relationships to form hierarchical relationships between different fields; the field type is not limited to a specific form and the field type is set according to needs.

[0024] The tag management module 2 is used to generate a unique tag by combining the unique identifiers of entities corresponding to different fields in the tag and their hierarchical relationships; At the same time, the tag management module 2 is also used to reasonably modify the fields corresponding to the corresponding entities according to the needs of the application scenario to ensure the uniqueness of the identification.

[0025] The tag association module 4 uses different fields in the tag and combinations of fields to establish a one-to-one correspondence with the nodes in different scenarios.

[0026] The tag identification module 3 parses the fields in the tag and their hierarchical relationships based on the mapping relationship between the tag and the node established by the tag association module, and uses these fields and hierarchical relationships to locate the corresponding nodes step by step or locate the corresponding nodes according to the one-to-one mapping relationship between the tag and the node, as well as the combined application of these two methods.

[0027] For this specific implementation, by constructing a hierarchical marking structure and a flexible multi-field combination, the uniqueness and scalability problems of the identification of nodes in mind maps and knowledge graphs in complex application scenarios are solved, and it has the following significant advantages: supporting rapid retrieval and positioning in complex scenarios; the field type and hierarchical relationship can be adjusted as needed; new fields can be added to adapt to multi-scenario applications; it is suitable for cross-platform multi-user data fusion and collaboration based on knowledge graphs and mind maps, and in this way, the technical problems of insufficient node uniqueness, weak node correlation, and poor scalability and compatibility in the knowledge graphs and mind maps in the prior art are solved.

[0028] See also Figure 2 and Figure 3 , Figure 2 It is a schematic diagram of each field of the mind map node mark of the present invention. Figure 3 The present invention also provides a node level identification method for knowledge graphs and mind maps, which is applied to the node level identification system for knowledge graphs and mind maps as described above. The steps include: S1, based on the hierarchical node marking module 1, for different scenarios, according to the attributes and hierarchical relationships of the nodes, different components and hierarchical relationships of the node markings are set; When the tag management module 2 is used to generate a unique node identification tag according to a preset rule or a modification request: the tag management module 2 is used to combine the unique identifications of entities corresponding to different fields in the tag and their hierarchical relationships to generate a unique node identification; When the tag management module 2 is used to update a unique node identification tag according to a preset rule or a modification request: the tag management module 2 is used to modify a field in the node tag to generate a new unique node identification.

[0029] See also Figure 2 , Figure 2It is a schematic diagram of the fields for marking the mind map nodes of the present invention. The ID field uniquely identifies the nodes in the same mind map, and the field type is a number; the OwnerID field uniquely identifies a user, and the field type is a string; the DeviceID field uniquely identifies the device used when creating the node, and the field type is UUID; MapID uniquely identifies the mind map where the node is located, and the field type is UUID. Among them, UUID is the abbreviation of Universally Unique Identifier. The hierarchical relationship between the various fields is as follows: OwnerID contains DeviceID, which contains MapID, which contains ID. The inclusion here means that the object marked by the upper-level field can accommodate multiple objects marked by the lower-level fields. The fields can be flexibly combined according to the needs of the scene to uniquely mark and identify the nodes of the mind map in the current scene.

[0030] S2, using the tag management module 2, according to preset rules or modification requests, generating or updating a unique node identification tag; See also Figure 3 , Figure 3 It is a schematic diagram of the tag generation method and rewriting method of this embodiment. Select fields to combine to form a unique identifier, and use symbols that will not appear in the selected fields as connectors to connect the fields. Here, the '#' and '~' symbols are used as connectors to generate a unique tag for the node in the corresponding scenario. The tag rewriting method is to parse out each field through a connector, modify or add symbols to the corresponding field, such as appending a random number after the OwnerID field to generate a new OwnerID, or replacing the OwnerID field to generate a new OwnerID, and then reconnect the fields with a connector to generate a new tag.

[0031] S3. Using the tag association module 4, a one-to-one mapping relationship between tags and nodes is established, or identifiers of different fields in the tags are dispersed in corresponding objects or devices and a one-to-one correspondence between them is established.

[0032] S4. By means of the tag recognition module 3, the tags are parsed to extract fields with hierarchical relationships so as to achieve the purpose of locating nodes step by step or locating nodes by tags according to a one-to-one mapping relationship between tags and nodes.

[0033] In the process of locating nodes, when solving node identification in more complex scenarios, the tag identification module 3 is used to parse the devices and entities corresponding to different fields and their hierarchical relationships according to the tags and the one-to-one mapping relationship between the tags and the nodes.

[0034] The tag recognition module 3 uses the delimiter of the mind map node tag to extract the value of each field in the tag, and searches for the corresponding object in the mind map App according to the field value. For example, it searches for a node in a mind map according to the ID value, and then further searches according to the OwnerID in the search result until the unique node identified by the tag is found using the value of each field in the tag; it can also use the mapping between tags and corresponding objects established in advance in the mind map APP to directly obtain the mapped object through the tag.

[0035] The tag recognition module 3 can solve the problem of locating mind map nodes in more complex application scenarios, such as: the mind map App receives links from other Apps to locate the nodes of the corresponding mind map and is used to display the nodes of the mind map, and the search results in multiple mind maps are associated through tags in the mind map App, and the nodes corresponding to the search results in different mind maps can be located through tags. The specific method is as follows: extract the tag from the received link information, and then extract the values ​​of each field from the tag according to the separator, and then find the corresponding mind map in the mind map App according to the value of MapID, and the tag recognition module 3 uses the values ​​of other fields in the mind map to locate the corresponding node. The node level identification system and method for knowledge graphs and mind maps of the present invention, when used specifically, solves the uniqueness and scalability problems of node identification in mind maps and knowledge graphs in complex application scenarios by constructing a hierarchical tag structure and a flexible multi-field combination, and has the following significant advantages: supports rapid retrieval and positioning in complex scenarios; can adjust field types and hierarchical relationships as needed; allows new fields to be added to adapt to multi-scenario applications; is suitable for cross-platform multi-user data fusion and collaboration based on knowledge graphs and mind maps, and in this way solves the technical problems of insufficient node uniqueness, weak node correlation, and poor scalability and compatibility in the prior art knowledge graphs and mind maps.

[0036] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A node level identification system for knowledge graphs and mind maps, characterized in that: It includes a hierarchical node marking module, a marking management module, a marking recognition module and a marking association module, wherein the marking management module is connected to the hierarchical node marking module, the marking recognition module and the marking association module are both connected to the marking management module, and the marking association module is also connected to the marking recognition module; The hierarchical node marking module is used to set different components and hierarchical relationships of node markings in different scenarios; The tag management module is responsible for the generation, rewriting and management of tags based on the rules set by the hierarchical node tag module; The tag association module is used to establish a one-to-one correspondence between the node identifiers and the nodes in the mind map or knowledge graph in different scenarios; The tag recognition module locates the node from the tag according to the mapping relationship between the tag and the node established by the tag association module.

2. The node level identification system for knowledge graph and mind map according to claim 1, characterized in that: The different components and hierarchical relationships of the node tags set by the hierarchical node tag module refer to that the node tags are composed of different fields, each field represents a different entity, and there is a containment or subordination relationship between the entities to form a hierarchical relationship between different fields.

3. The node level identification system for knowledge graph and mind map according to claim 1, characterized in that: The tag management module is used to generate a unique tag by combining the unique identifiers of entities corresponding to different fields in the tag and their hierarchical relationships; At the same time, the tag management module is also used to reasonably modify the fields corresponding to the corresponding entities according to the needs of the application scenario to ensure the uniqueness of the identification.

4. The node level identification system for knowledge graph and mind map according to claim 1, characterized in that: The tag association module uses a combination of fields corresponding to different entities to establish a one-to-one correspondence between the unique tags of nodes generated as needed in different scenarios and the nodes.

5. The node level identification system for knowledge graph and mind map according to claim 1, characterized in that: The tag recognition module parses the devices and entities corresponding to different fields and their hierarchical relationships according to the tags or according to the one-to-one mapping relationship between tags and nodes to efficiently retrieve and locate the nodes represented by the tags in complex application scenarios.

6. A node level identification method for knowledge graphs and mind maps, applied to the node level identification system for knowledge graphs and mind maps as claimed in claim 1, characterized in that: The steps include: Based on the hierarchical node marking module, for different scenarios, according to the attributes and hierarchical relationships of the nodes, different components and hierarchical relationships of the node markings are set; Using the tag management module, generating or updating a unique node identification tag according to a preset rule or a modification request; Using the tag association module, accurate association between node tags and nodes in a mind map or knowledge graph is achieved; Through the tag recognition module, the tags are parsed to extract fields with hierarchical relationships to achieve the purpose of locating nodes step by step or locating nodes by tags according to a one-to-one mapping relationship between tags and nodes.

7. The node level identification method for knowledge graph and mind map according to claim 6, characterized in that: When the tag management module is used to generate a unique node identification tag according to a preset rule or a modification request: the tag management module is used to combine the unique identifications of entities corresponding to different fields in the tag and their hierarchical relationships to generate a unique node identification; When the tag management module is used to update a unique node identification tag according to a preset rule or a modification request: the tag management module is used to modify a field in the node tag to generate a new unique node identification.

8. The node level identification method for knowledge graph and mind map according to claim 6, characterized in that: In the process of locating nodes, when solving node identification in more complex scenarios, the tag identification module is used to parse out the devices and entities corresponding to different fields and their hierarchical relationships based on the tags and the one-to-one mapping relationship between the tags and the nodes.

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