CN112182236A - Knowledge graph construction method and system and electronic equipment - Google Patents
Knowledge graph construction method and system and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method, a system and electronic equipment for constructing a knowledge graph, wherein the method comprises the following steps: the method comprises the steps of receiving an input data source through a processing device, wherein the data source comprises a plurality of data tables, the data tables comprise entity tables and relation tables, the processing device adopts a built-in processing unit, generates an ontology structure by automatically analyzing the table structure of the data tables of the input data source, and performs knowledge graph data mapping according to the ontology structure to create a visual knowledge graph. By utilizing the method and the system disclosed by the invention, the map construction process is simplified, the knowledge map can be obtained only by manually and simply reading in and editing the configuration, the cost is reduced, and the efficiency of knowledge map construction is improved, so that the method and the system have obvious technical advantages and technical effects.
Description
Technical Field
The invention relates to the technical field of knowledge graphs, in particular to a method and a system for constructing a knowledge graph and electronic equipment.
Background
With the development of big data technology, the requirements of people on data are not limited to massive traditional data any more, data workers and scientists begin to look at exploring the deeper value of data, knowledge maps are produced at the same time, and the application range of the knowledge maps is wider and wider at present, for example, the knowledge maps can be applied to different fields such as internet finance, human resource management, enterprise management and the like.
Knowledge map (Knowledge Graph) is a series of different graphs displaying Knowledge development process and structure relationship in the book intelligence field, describing Knowledge resources and carriers thereof by using visualization technology, mining, analyzing, constructing, drawing and displaying Knowledge and mutual relation between Knowledge resources and Knowledge carriers. At present, most of domestic manufacturers have research and development centers on constructing knowledge graphs based on ontology structures, namely, manually editing to form an ontology structure and a data specification, and then configuring a data source based on an ontology layer, which obviously forms a specification and a custom practice. However, in actual work, because the service scenes and service requirements of the map are diversified, and the ontology models of different service requirements are different, based on the prior art scheme, a large amount of time and personnel are required to be consumed to be input into manual editing of the ontology models, and meanwhile, the construction reusability of the ontology models is low, so that the construction cost of the knowledge map is high.
In conclusion, the problems of complex process, low efficiency, overhigh construction cost and the like generally exist in the construction of the knowledge graph based on the prior art.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a method, a system and electronic equipment for constructing a knowledge graph, which are used for solving the technical problems of complex process, low efficiency, serious resource waste and the like in the conventional method for constructing the knowledge graph.
In order to achieve the above object, the present invention provides a method for constructing a knowledge graph, wherein a processing device receives an input data source, the data source includes a plurality of data tables, the data tables include an entity table and a relationship table, the processing device adopts a built-in processing unit, generates an ontology structure by automatically analyzing a table structure of the input data source, and performs a data mapping of the knowledge graph according to the ontology structure to create a visual knowledge graph.
The construction method of the knowledge graph comprises the following steps:
step S1: reading in the data source, adopting the processing unit to automatically analyze the table structure of the data table of the data source to obtain table structure information, and generating an ontology structure based on the table structure information;
step S2: reading a data source, mapping map data, and mapping entity data and relationship data in a data table to a corresponding body structure;
step S3: and displaying the entity data and the relation data of the data table for completing the data mapping in a form of corresponding nodes and edges according to the body structure to complete the creation of the knowledge graph.
The step S1 includes the following implementation steps:
step S1-1: the processing equipment reads in the data source, traverses the data source and automatically analyzes to obtain the table structure information of each data table; the table structure information includes entity information and relationship information;
step S1-2: and the processing equipment performs mapping and packaging according to the table structure information of the data table, and converts the table structure information into body structure data for storage.
In the step S1-2, the format of the ontology structure data is JSON format.
The format of the data table contained in the data source comprises MYSQL and CSV.
And when the data table format is MYSQL format, extracting corresponding table structure information through SQL query statements.
When the data table format is the CSV format, the table structure information is read by reading the header information.
The step S1-2 is followed by the following step S1-3: and rendering the body structure in a pre-generated window according to the configuration of a preset body structure to obtain the graph structure of the body structure.
The construction method of the knowledge graph further comprises the following steps: and manually modifying and adjusting the acquired graph of the body structure, fusing repeated entities in the body structure to generate a new body structure graph, and storing corresponding new body structure data.
The construction method of the knowledge graph further comprises the following steps: the generated ontology structure is saved to facilitate creation of a new ontology structure based on the saved ontology structure. The scheme supports the storage of the ontology structure model in an expansion mode, so that the established ontology structure is called for multiplexing or is modified and edited in more service scenes, and the creation efficiency of the knowledge graph is further improved.
Step S3 further includes loading the obtained knowledge graph dataset in a pre-generated window according to a preset setting, and rendering the loaded knowledge graph dataset to obtain a graph structure of the knowledge graph dataset.
Based on the same inventive concept, the invention provides a system for constructing a knowledge graph, which comprises the following steps: the system comprises a data input module, an ontology construction module, a map data mapping module and a map visualization module; the data input module, the body construction module, the map data mapping module and the map visualization module are sequentially connected, and the data input module is connected with the map data mapping module;
the data input module: an input for receiving a data source;
the ontology building module: the system comprises a data source, a body structure and a data processing module, wherein the data source is used for inputting data;
the map data mapping module: mapping knowledge graph data according to the body structure;
the map visualization module: reading the mapped data according to the body structure, and displaying the data in a knowledge graph mode in a visual mode;
the ontology construction module adopts the following steps to construct an ontology:
reading in a data source, wherein the data source comprises a plurality of data tables, traversing the data source, and automatically analyzing to obtain table structure information of each data table;
and mapping and packaging according to the table structure information of the data table, converting into body structure data and storing.
Further, the atlas visualization module is configured to load and render the obtained knowledge atlas data set in a pre-generated window according to preset settings, so as to obtain a graph structure of the knowledge atlas data set.
As a preference, the system further comprises: the map configuration module is respectively connected with the map data mapping module and the map visualization module; the map configuration module is used for configuring the style, state and attribute of the displayed nodes and edges containing the knowledge map. The system comprises a map configuration model, and the quality and effect of knowledge map creation can be further improved by configuring the styles of map nodes, edges and the like.
Further comprising: a saving module to save the generated atlas dataset to a memory.
Based on the same inventive concept, the present invention provides an electronic device, comprising a processor and a memory,
the memory is used for storing an executable program;
the processor is used for executing the executable program to realize the construction method of the knowledge graph.
Has the advantages that:
compared with the prior art, one or more technical schemes in the embodiment of the application have the following obvious technical advantages and technical effects:
(1) based on the knowledge graph construction mode disclosed by the invention, the graph construction process is simplified, the efficiency is improved, the repeated labor and the resource waste are reduced, and the method can be more effectively applied to service scenes;
(2) by adopting the system disclosed by the invention, a user only needs to clean the own data to form the node table and the edge table, and the system carries out a small amount of configuration after linking the data source to generate the map. In the process, the user can check and operate the structure data, and experience from attribute configuration to style configuration is achieved in one-stop mode, so that the application of the knowledge graph is more flexible and convenient.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method for constructing an ontology structure of a knowledge-graph according to an embodiment of the invention;
FIG. 2 is a flowchart of step S1 according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a body structure according to an embodiment of the present invention;
FIG. 4 is a schematic view of a visualization window provided in an embodiment of the present invention;
fig. 5 is a block diagram of a system for constructing a knowledge graph according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Based on the problems of high error rate and low efficiency of the current map construction method, the embodiment of the invention provides a knowledge map construction method, an input data source is received through a processing device, the data source comprises a plurality of data tables, the data tables comprise entity tables and relation tables, the processing device adopts a built-in processing unit to generate an ontology structure through automatically analyzing the data table structure of the input data source, and the knowledge map data mapping is carried out according to the ontology structure to create a visual knowledge map.
Compared with the traditional construction mode (generally, a user firstly analyzes the object of the body structure, manually defines the relationship between the nodes of the body structure, then generates the body structure, and constructs a map based on the body structure), the invention automatically completes the construction of the body structure only by reading in the data table, thereby simplifying the construction process of the body structure, reducing the error rate due to full manual editing, improving the construction efficiency and success rate of the body structure, and saving human resources.
Specifically, referring to fig. 1, the method for constructing the knowledge graph includes the following steps:
step S1: and reading the data source, adopting the processing unit to automatically analyze the table structure of the data table of the data source to obtain table structure information, and generating a body structure based on the table structure information.
Step S2: and reading the data source, mapping the map data, and mapping the entity data and the relation data in the data table to the corresponding body structure.
Step S3: and displaying the entity data and the relation data of the data table for completing the data mapping in a form of corresponding nodes and edges according to the body structure to complete the creation of the knowledge graph.
According to the method, the ontology structure is generated by automatically analyzing the structure of the data source data table, and the creation of the knowledge graph is further completed based on the ontology structure mapping data, so that the manual intervention configuration process in the whole process is less, the automation of the construction of the knowledge graph is greatly realized, and the labor input is remarkably saved.
Referring to fig. 2, in a more detailed embodiment, the step S1 includes the following steps:
step S1-1: the processing equipment reads in the data source, traverses the data source and automatically analyzes to obtain the table structure information of each data table; the table structure information includes entity information and relationship information.
Step S1-2: and the processing equipment performs mapping and packaging according to the table structure information of the data table, and converts the table structure information into body structure data for storage.
The mapping encapsulation is to convert the acquired table structure information into an ontology structure data format which can be called and recognized when the data mapping is performed during creation of the knowledge graph. The table structure can not be directly used as the ontology structure data after being analyzed, the format requirements of the ontology structure data of different knowledge map generation systems are different, and the analyzed table structure information is converted into the identifiable ontology structure data through mapping and packaging.
Preferably, in step S1-2, the format of the ontology structural data is JSON format. The ontology structures recognizable by different knowledge graph generation systems are different in format, for example, the ontology structure format in some systems is OWL format, JSON format is preferably used in the method, the data storage of the ontology structure in the JSON format is convenient, the data mapping analysis during the knowledge graph generation is convenient, and various database calls are supported.
The format of the data table in the data source may be various, and preferably, the format of the data table included in the data source in this embodiment includes MYSQL and CSV, because the data formats of MYSQL and CSV are easy to parse, and the content of the stored data is rich.
In the step S1-1, when the data table format is MYSQL format, the corresponding table structure information is extracted through SQL query statements. When the data table format is the CSV format, the table structure information is obtained by reading the header information. The extraction method of the table structure information of the data tables of different data formats is different and is adopted according to the specific situation of the data tables.
The ontology structure generated in step S1 may be obtained only by obtaining ontology structure data, or may be a graph structure in which an ontology structure is obtained. Therefore, as an optional implementation manner, the step S1-2 may further include the step S1-3: and rendering the body structure in a pre-generated window according to the configuration of a preset body structure to obtain the graph structure of the body structure.
In the method, the ontology structure is visually displayed in the window, and the visual ontology structure is input from the data source to be obtained by combining the process of automatic analysis, so that the generation efficiency is extremely high, the logical relationship is clear and clear after the ontology structure is visually displayed, and the use experience of a user is improved.
Preferably, the method for constructing the knowledge-graph further comprises the following steps: and manually modifying and adjusting the automatically acquired graph of the body structure, fusing repeated entity nodes in the body structure to generate a new body structure graph, and storing corresponding new body structure data.
One problem that may arise from generating an ontology structure directly by parsing a data table structure is that nodes are duplicated, for example, a natural person may have multiple attributes, such as a corporate shareholder, an actual controller, an employee, and the like, which may be separate entity nodes parsed out from the data table structure, and the ontology structure generated thereby includes multiple points, and if not merged, there will be multiple substantially identical nodes in a knowledge graph generated after mapping corresponding graph data, and multiple different association relationships will be established with the same corporate. Through the combination of the entities, the aims of simplifying the body structure and improving the performance of the body structure are achieved. And the ontology structure is automatically generated by combining data analysis, and on the basis, a high-quality ontology model with clear and simple logic can be established only by a small amount of manual modification, so that an ontology model basis is provided for the creation of a high-quality knowledge graph.
In an alternative embodiment, in said step S1, after generating the ontology structure, the generated ontology structure (ontology structure data or ontology structure graph) is saved so as to create a new ontology structure based on the saved ontology structure. The scheme expansion supports the storage of the ontology structure model, so that the established ontology structure is called for multiplexing or is modified and edited in more future service scenes, the creation efficiency of the knowledge graph is further improved, and the utilization rate of the ontology structure can be improved.
In an alternative embodiment, the step S3 may further include: and loading the acquired knowledge graph data set in a pre-generated window according to a preset configuration, and rendering to acquire a graph structure of the knowledge graph data set. The method realizes the quick generation of the knowledge graph from the input of the data source, has extremely high efficiency and speed in the whole process, and provides very powerful technical support for the construction of the knowledge graph in various service fields.
The above is a description of the flow of steps of the method of the present invention, and for easier understanding, some steps of the method of the present invention are further described below with reference to specific examples.
In order to improve the efficiency of identifying the table structure information of each data source, the data source read in step S1 is a data source obtained by preprocessing in advance, that is, the data table is a data table obtained by preprocessing in advance. For example, the header or body of the data table contains table structure information and mapping data. The table structure information includes entity information and relationship information, the entity information includes the type and number of the entity, the relationship information includes the relationship (or relationship type) object type, object name, associated object and relationship, the object type includes the relationship object and the entity object. The mapping data includes entity data and relationship data, the entity data includes an entity type and an entity name, and the relationship data includes an associated object and a relationship (or referred to as a relationship type).
In general, a vertex table must have an id or field containing { id }, which can be resolved to represent the table primary key. edge represents the relationship type data, edge _ prefix plus { initial node label } plus { relationship type label } plus { end node label }, constituting a class of relationship type table name; the relationship table does not need to have id class primary keys, but must have startnode and endnode, or { start? Are } and { end? The start node mark and the end node mark which can be identified by reflection, and a relation table must have a combined main key, wherein start and end fields are optional.
For example, as an example, a data table in six CSV formats included in the data source of the body structure shown in fig. 3 is generated, where the table structure is edge _ person _ has _ account.csv, edge _ account _ out _ transfer.csv, edge _ transfer _ in _ account.csv, vertex _ person.csv, vertex _ account.csv, and vertex _ transfer.csv.
In the above example, the data table is named according to the resolvable rule, for example, the data table has a prefix indicating the data type and a node identifier, and the table structure information and the mapping data can be obtained according to the table header. The table name of the data table is composed of a vertex _ or edge _ prefix, the vertex _ represents node type data, the edge _ represents edge type data, and the type is a specific name. Csv, namely the node type is named as accout; csv, namely, the relationship type, the relationship name is has (owned), and the associated objects are person and acout.
By reading in the six data header information, the table structure information and the mapping data can be obtained, namely three entities are obtained: person, account, transfer, and three relationships: person _ has _ accout, account _ out _ transfer, transfer _ in _ accout.
The table structure information may also have a broad definition. For example, assume that the expression of the table structure information is: { Table object: table structure information }, table structure information can be obtained for the above data tables: { person son: name, uid }, { account: number, uid }, { transfer: "amount, location id, time }, { has: uid, startNode, endNode, source }, { in: uid, startNode, endNode, balance, create _ time, create _ time, source }, { out: uid, startNode, endNode, balance, source }.
After the table structure information is obtained, the table structure information can be mapped and encapsulated according to a preset format and converted into a JSON type body structure.
As an example, taking a node structure with person as an ontology structure as an example, the encapsulated data is as follows:
"person":{"source_table":"vertex_person.csv","properties":{"name":{"type":"string","primary":false,"index":false,"display_index":1,"disabled":false,"format":"","source_column":"name"},"uid":{"type":"string","primary":true,"index":false,"display_index":2,"disabled":false,"format":"","source_column":"uid"}}
taking has as an example of the edge structure of the body structure, the encapsulated data is as follows:
"has":{"properties":{"uid":{"type":"string","primary":true,"index":false,"display_index":1,"disabled":false,"format":"","is_start":false,"is_end":false},"startNode":{"type":"string","primary":true,"index":false,"display_index":2,"disabled":false,"format":"","is_start":false,"is_end":false},"endNode":{"type":"string","primary":true,"index":false,"display_index":3,"disabled":false,"format":"","is_start":false,"is_end":false},"source":[{"start":"person","end":"account","source_table":"edge_person_has_account.csv","source_column":{"uid":"uid","startNode":"startNode","endNode":"endNode","__start_node__":"startNode","__end_node__":"endNode"}}],"type":"edge"}。
it should be noted that the naming of the data table described in the embodiment of the present invention is only an example, and in an actual implementation, there may be other implementation manners. For example, for a data table with a data table format of MYSQL format, the table structure information is not obtained by reading the header information, but the corresponding table structure information is extracted by the SQL query statement.
In the above step S1-3, the definable styles include representing entity data and relationship data in different shapes, e.g., circles representing entities, lines representing relationships. Referring to fig. 3, a schematic diagram of a body structure according to an embodiment of the invention is shown, in which a circle represents three entities: people, account numbers, transactions, three relationships are represented by connecting lines and arrows: own, transfer and transfer, wherein "own" represents person _ has _ account, "transfer" represents account _ out _ transfer, and "transfer" represents transfer _ in _ account. The user can preview the body structure in the visual area, and the configuration operation of the shape and the style is carried out, so that the body structure is rendered, and the display of the body structure is richer. As shown in fig. 4, the corresponding configuration may be performed in zone three.
Referring to fig. 5, fig. 5 is a block diagram illustrating a structure of a system for constructing an ontology structure according to an embodiment of the present invention, where the system includes a data input module 601, an ontology constructing module 602, a atlas data mapping module 603, and an atlas visualization module 604, where the data input module 601, the ontology constructing module 602, the atlas data mapping module 603, and the atlas visualization module 604 are sequentially connected, and the data input module 601 is connected to the atlas data mapping module 603.
The data input module 601: receiving an input of a data source;
ontology building module 602: the system comprises a data source, a body structure and a data processing module, wherein the data source is used for inputting data;
map data mapping module 603: mapping the knowledge graph data according to the body structure;
the atlas visualization module 604: and reading the mapped data according to the body structure, and displaying the data in a knowledge graph mode in a visual mode.
The ontology building module 602 builds an ontology by the following steps:
reading in a data source, wherein the data source comprises a plurality of data tables, traversing the data source, and automatically analyzing to obtain the table structure information of each data table;
and mapping and packaging according to the table structure information of the data table, converting into body structure data and storing.
When the atlas visualization module 604 is implemented specifically, the obtained knowledge graph dataset is loaded and rendered in a pre-generated window according to preset settings, so as to obtain a graph structure of the knowledge graph dataset.
In an embodiment, the construction system may further include a map configuration module, which is connected to the map data mapping module and the map visualization module, respectively. The map configuration module is used for configuring the style, state and attribute of the displayed nodes and edges containing the knowledge map.
And configuring the attribute of each field, such as field name, primary key field name, field data type and the like.
In one embodiment, the configuration information is shown in the following table:
in one embodiment, the above construction system may further comprise a saving module for saving the generated atlas data set to a memory.
Please refer to the description of the foregoing method embodiments, and details of the specific execution steps or the extensible execution steps of each module in the system are not described herein.
Referring to fig. 4, fig. 4 shows a schematic diagram of a visualization window of a atlas visualization module in an embodiment of the present invention, as shown in the drawing, a visualization region provided by the system is divided into 3 regions, one region is represented by a solid line box, where a region one displays a data source including a plurality of data tables, and a region two displays an automatically generated ontology structure, and a user may configure and modify attribute information of the ontology structure including a field name, a data format, and a length in a region three, and may also modify style information of the ontology structure and/or the atlas.
Those of ordinary skill in the art will appreciate that the various illustrative modules described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed system may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (16)
1. A construction method of a knowledge graph is characterized in that an input data source is received through a processing device, the data source comprises a plurality of data tables, the data tables comprise entity tables and relation tables, the processing device adopts a built-in processing unit, an ontology structure is generated through automatically analyzing the table structure of the input data source data tables, and the knowledge graph data mapping is carried out according to the ontology structure to create a visual knowledge graph.
2. A method of constructing a knowledge-graph as claimed in claim 1, comprising the steps of:
step S1: reading in the data source, adopting the processing unit to automatically analyze the table structure of the data table of the data source to obtain table structure information, and generating an ontology structure based on the table structure information;
step S2: reading a data source, mapping map data, and mapping entity data and relationship data in a data table to a corresponding body structure;
step S3: and displaying the entity data and the relation data of the data table for completing the data mapping in a form of corresponding nodes and edges according to the body structure to complete the creation of the knowledge graph.
3. The method for constructing a knowledge-graph as claimed in claim 2, wherein said step S1 comprises the following implementation steps:
step S1-1: the processing equipment reads in the data source, traverses the data source and automatically analyzes to obtain the table structure information of each data table;
step S1-2: and the processing equipment performs mapping and packaging according to the table structure information of the data table, and converts the table structure information into body structure data for storage.
4. The method for constructing a knowledge-graph according to claim 3, wherein in the step S1-2, the format of the ontology structural data is JSON format.
5. The method for constructing a knowledge graph according to claim 3, wherein the data source comprises data tables in a format including MYSQL and CSV.
6. The method of knowledge-graph construction according to claim 5, wherein when the data table format is MYSQL format, the corresponding table structure information is extracted by SQL query statement.
7. The method of knowledge-graph construction according to claim 5 wherein the table structure information is read by reading header information when the data table format is the CSV format.
8. The method for constructing a knowledge-graph as claimed in claim 3, wherein the step S1-2 is followed by the step S1-3 of: and rendering the body structure in a pre-generated window according to the configuration of a preset body structure to obtain the graph structure of the body structure.
9. The method of constructing a knowledge-graph according to any one of claims 1 to 8, further comprising the steps of: and manually modifying and adjusting the acquired graph of the body structure, fusing repeated entities in the body structure to generate a new body structure graph, and storing corresponding new body structure data.
10. The method of knowledge-graph construction as claimed in claim 2, further comprising the steps of: the generated ontology structure is saved to facilitate creation of a new ontology structure based on the saved ontology structure.
11. The method for constructing a knowledgegraph according to claim 9, wherein the step S3 further comprises loading and rendering the obtained knowledgegraph dataset according to a preset setting in a pre-generated window to obtain a graph structure of the knowledgegraph dataset.
12. A system for constructing a knowledge graph, comprising: the system comprises a data input module, an ontology construction module, a map data mapping module and a map visualization module; the data input module, the body construction module, the map data mapping module and the map visualization module are sequentially connected, and the data input module is connected with the map data mapping module;
the data input module: an input for receiving a data source;
the ontology building module: the system comprises a data source, a body structure and a data processing module, wherein the data source is used for inputting data;
the map data mapping module: mapping knowledge graph data according to the body structure;
the map visualization module: reading the mapped data according to the body structure, and displaying the data in a knowledge graph mode in a visual mode;
the ontology construction module adopts the following steps to construct an ontology:
reading in a data source, wherein the data source comprises a plurality of data tables, traversing the data source, and automatically analyzing to obtain table structure information of each data table;
and mapping and packaging according to the table structure information of the data table, converting into body structure data and storing.
13. The system for constructing a knowledgegraph as claimed in claim 12, wherein the graph visualization module is configured to load and render the obtained knowledgegraph dataset according to a preset setting in a pre-generated window to obtain a graph structure of the knowledgegraph dataset.
14. The system for constructing a knowledge-graph of claim 13, further comprising: the map configuration module is respectively connected with the map data mapping module and the map visualization module;
the map configuration module is used for configuring the style, state and attribute of the displayed nodes and edges containing the knowledge map.
15. The system for constructing a knowledge-graph according to any one of claims 12 to 13, further comprising: a saving module to save the generated atlas dataset to a memory.
16. An electronic device, comprising a processor and a memory,
the memory is used for storing an executable program;
the processor is used for executing the executable program to realize the construction method of the knowledge-graph of any one of claims 1-11.
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