CN114780752A - Method, system, equipment and storage medium for establishing federal knowledge graph - Google Patents
Method, system, equipment and storage medium for establishing federal knowledge graph Download PDFInfo
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Abstract
The application discloses a method, a system, equipment and a storage medium for constructing a federated knowledge graph, wherein the method for constructing the federated knowledge graph comprises the following steps: the method comprises the steps of obtaining multi-source heterogeneous data of a target field, generating each multi-source data table based on the multi-source heterogeneous data, conducting classification analysis on each multi-source data table to obtain target map information, generating different entity files and edge relation files based on the target map information, and constructing a target federal knowledge map based on the different entity files and edge relation files. The method and the device solve the technical problem that the knowledge graph is difficult to construct by combining multi-party data due to the fact that various data are dispersed and the data lack correlation.
Description
Technical Field
The application relates to the technical field of internet, in particular to a method, a system, equipment and a storage medium for constructing a federated knowledge map.
Background
Knowledge Graph (knowledgegraph), which is called as knowledgedomain visualization or knowledgedomain mapping map in the book intelligence world, is a series of different graphs displaying the relationship between the Knowledge development process and the structure, and the entities of various data and the corresponding association relation need to be identified in the process of constructing the Knowledge Graph, however, in a large number of application scenarios in the financial industry, because of the lack of a uniform Knowledge framework, various data are relatively dispersed and the lack of association among data, it is difficult to combine multi-party data to construct the Knowledge Graph.
Disclosure of Invention
The application mainly aims to provide a method, a system, equipment and a storage medium for constructing a federated knowledge graph, and aims to solve the technical problem that the knowledge graph is difficult to construct by combining multi-party data due to the fact that various data are dispersed and the data lack correlation.
In order to achieve the above object, the present application provides a federated knowledge graph construction method, which includes:
acquiring multi-source heterogeneous data of a target field, and generating each multi-source data table based on the multi-source heterogeneous data;
classifying and analyzing each multi-source data table to obtain target map information, wherein the target map information comprises map entities of different types, entity attributes, map edges of different types and edge attributes;
generating different entity files and edge relation files based on the target map information;
and constructing a target federal knowledge graph based on the different entity files and the edge relationship files.
Optionally, the step of performing classification analysis on the multi-source data table to obtain target atlas information includes:
in the multi-source data table, in combination with a service scene of a current target field, selecting each target field with query frequency meeting a preset frequency threshold as the map entities of different types, and determining entity attributes corresponding to each map entity;
based on each map entity, selecting target fields associated with the map entities of the same type and different types as map edges of different types in the multi-source data table, and determining edge attributes corresponding to the map edges, wherein the map edges represent association relations among the map entities.
Optionally, the step of generating different entity files and edge relationship files based on the target map information includes:
generating each entity file according to target fields corresponding to different map entities in the target map information;
and generating each side relation file according to the target fields corresponding to different map sides in the target map information.
Optionally, the step of generating each multi-source data table based on the multi-source heterogeneous data includes:
and processing the multi-source heterogeneous data by a preset natural language processing method to generate each multi-source data table.
Optionally, after the step of constructing the target federal knowledge graph based on the different entity documents and the edge relationship documents, the method further comprises:
constructing a visual webpage of the target federal knowledge graph;
acquiring an operation instruction of a target user on the visual webpage;
inquiring target return information corresponding to the operation instruction in the target federal knowledge graph;
and visually drawing in the visual webpage through a preset drawing algorithm based on the target return information.
Optionally, the step of performing visual rendering through a preset rendering algorithm based on the target return information to obtain a target plot drawing includes:
importing the target return information into a preset constructed force guide diagram layout;
and dynamically calling a preset drawing function in the force guide graph layout, and drawing the target return information based on the drawing function.
Optionally, the step of drawing the target return information based on the drawing function to obtain the target plot chart includes:
if the target return information exists in the map entity, node drawing is carried out according to a preset node style based on the map entity data;
and if the target return information has map edges, performing edge drawing according to a preset edge style based on the number of the map edges between map entities.
The application also provides a federated knowledge map construction system, the federated knowledge map construction system is a virtual system, the federated knowledge map construction system includes:
the acquisition module is used for acquiring multi-source heterogeneous data and generating each multi-source data table based on the multi-source heterogeneous data;
the analysis module is used for classifying and analyzing each multi-source data table to obtain target map information, wherein the target map information comprises map entities of different types, entity attributes, map edges of different types and edge attributes;
the generating module is used for generating different entity files and edge relation files based on the target map information;
and the construction module is used for constructing the target federal knowledge graph based on the different entity files and the edge relation files.
Optionally, the analysis module is further configured to;
selecting target fields with query frequency meeting a preset frequency threshold as the different types of map entities in combination with the service scene of the current target field in the multi-source data table, and determining entity attributes corresponding to the map entities;
based on each map entity, selecting target fields associated with the map entities of the same type and different types as map edges of different types in the multi-source data table, and determining edge attributes corresponding to the map edges, wherein the map edges represent association relations among the map entities.
Optionally, the generating module is further configured to;
generating each entity file according to target fields corresponding to different map entities in the target map information;
and generating each edge relation file according to the target fields corresponding to different map edges in the target map information.
Optionally, the obtaining module is further configured to;
and processing the multi-source heterogeneous data by a preset natural language processing method to generate each multi-source data table.
Optionally, the federated knowledge graph building system is further configured to:
constructing a visual webpage of the target federal knowledge graph;
acquiring an operation instruction of a target user on the visual webpage;
inquiring target return information corresponding to the operation instruction in the target federal knowledge graph;
and visually drawing in the visual webpage through a preset drawing algorithm based on the target return information.
Optionally, the federated knowledge graph building system is further configured to:
importing the target return information into a preset constructed force guide diagram layout;
and dynamically calling a preset drawing function in the layout of the force guide graph, and drawing the target return information based on the drawing function.
Optionally, the federated knowledge graph building system is further configured to:
if the target return information has a graph entity, node drawing is carried out according to a preset node style based on the graph entity data;
and if the target return information has map edges, performing edge drawing according to a preset edge style based on the number of the map edges between the map entities.
The application also provides a federal knowledge map construction equipment, which is an entity equipment, the federal knowledge map construction equipment comprises: the system comprises a memory, a processor and a federal knowledge graph building program stored on the memory, wherein the federal knowledge graph building program is executed by the processor to realize the steps of the federal knowledge graph building method.
The present application also provides a storage medium, which is a computer-readable storage medium, on which a federal knowledge graph construction program is stored, and the federal knowledge graph construction program is executed by a processor to implement the steps of the federal knowledge graph construction method as described above.
The application provides a method, a system, equipment and a storage medium for constructing a federated knowledge graph, the method comprises the steps of firstly obtaining multi-source heterogeneous data of a target field, generating each multi-source data table based on the multi-source heterogeneous data, and then classifying and analyzing each multi-source data table to obtain target graph information, wherein the target graph information comprises graph entities, entity attributes, graph edges and edge attributes of different types, further, different entity files and edge relationship files are generated based on the target graph information, then a target federated knowledge graph is constructed based on the different entity files and edge relationship files, classification and analysis are carried out on the data tables corresponding to the multi-source heterogeneous data, and information such as the graph entities, the entity attributes, the graph edges and the edge attributes of different types corresponding to the constructed graph is selected, therefore, different entity files and side relation files are generated, and a target federal knowledge graph containing various data is constructed, so that business personnel can better expand the business based on the associated information of the federal knowledge graph.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present application, the drawings required to be used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art to be able to obtain other drawings without inventive labor based on these drawings.
FIG. 1 is a schematic flow chart of a first embodiment of a federated knowledge graph construction method of the present application;
FIG. 2 is a schematic flow chart diagram of a second embodiment of a federated knowledge graph construction method of the present application;
FIG. 3 is a schematic flow chart of a third embodiment of a federated knowledge graph construction method of the present application;
FIG. 4 is a system framework diagram of the federated knowledge-graph construction method of the present application;
FIG. 5 is a schematic structural diagram of a federated knowledge graph construction device of a hardware operating environment according to an embodiment of the present application;
fig. 6 is a functional module schematic diagram of the federal knowledge graph construction apparatus of the present application.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
In a first embodiment of the federal knowledge graph construction method of the present application, referring to fig. 1, the federal knowledge graph construction method includes:
step S10, multi-source heterogeneous data of a target field are obtained, and each multi-source data sheet is generated based on the multi-source heterogeneous data;
in this embodiment, it should be noted that the target fields include medical, financial, aerospace and other fields, and in this application, the financial fields are used as the target fields to explain, and the multi-source heterogeneous data is data aggregating public affairs, retail and the like through a unified knowledge framework, and the public affairs data includes data information between companies and individuals.
The method comprises the steps of obtaining multi-source heterogeneous data of a target field, generating each multi-source data table based on the multi-source heterogeneous data, specifically obtaining multi-source heterogeneous data of public affairs, retail affairs and the like required by map building, analyzing the data of the public affairs, the retail affairs and the like by combining an NLP (natural language processing) method according to a unified knowledge framework, and generating each multi-source data table.
Step S20, classifying and analyzing each multi-source data table to obtain target map information, wherein the target map information comprises map entities of different types, entity attributes, map edges of different types and edge attributes;
in this embodiment, it should be noted that the target map information is a knowledge map schema, and defines a format of the knowledge map data to be added, which is equivalent to a data model corresponding to the field.
Classifying and analyzing each multi-source data table to obtain target map information, specifically, according to a service scene of a current target field, selecting field information with higher query frequency in the current service scene, further classifying and analyzing the field information, further selecting fields meeting entity construction conditions from the classified field information as map entities, further selecting field information for describing map entities from each multi-source data table as entity attributes based on each map entity, additionally, selecting field information capable of being associated with the same map entities and different map entities from each multi-source data table as map edges, and selecting field information for describing the map edges as edge attributes from each multi-source data table, thereby excavating company-company (transaction, investment and other relationships), The company-individual (relationship of legal person, stockholder, director of direction, etc.) and individual-individual (relationship of child, parent, business, etc.) are rich in association relationship.
For example, in a financial loan scene, a is a legal person of company B, the address of company B, the creation time of company B, and company B invests company C, then company B and company C are set as map entities, the creation time and the address are the entity attributes of company B, the company B and the company C are related to form an investment relationship, the investment relationship is used as the map edge, and the investment time, the investment amount, and the like are used as the edge attributes of the map edge.
Step S30, generating different entity files and edge relation files based on the target map information;
in this embodiment, different entity files and edge relationship files are generated based on the target map information, and specifically, corresponding fields of different entities are selected to generate entity files such as individuals, companies, groups, and the like. Meanwhile, corresponding fields of different edges in the schema are selected to generate company-company (relations of transaction, group, private investment, and the like), company-individual (relations of legal, stockholder, director of direction, and the like), and individual-individual (relations of children, parents, business, and the like) edge relation files.
Wherein, step S30: generating different entity files and edge relation files based on the target map information, which specifically comprises the following steps:
step S31, generating each entity file according to the target fields corresponding to different map entities in the target map information;
step S32, generating each of the edge relation files according to the target fields corresponding to different map edges in the target map information.
In this embodiment, specifically, target fields corresponding to different map entities are selected according to the target map information, entity files such as individuals, companies, groups, and the like are respectively generated, and simultaneously, target fields corresponding to different map edges in the target map information are selected to generate a plurality of edge relationship files.
And step S40, constructing a target federal knowledge graph based on the different entity files and the side relation files.
In this embodiment, it should be noted that the knowledgegraph is essentially a semantic network for representing relationships between entities, and the knowledgegraph is composed of a piece of knowledge, each piece of knowledge is represented by an SPO triple (Subject-predict-Object), for example: the nodes in the knowledge graph represent the entities, and the edges are the association relationship between the entities, further, the nodes and the edges can also have corresponding labels, and the labels are the identifiers of the categories corresponding to the nodes and the edges.
Specifically, based on the different entity files and the edge relationship files, nodes, node attributes and node labels required for building the graph are determined, association relations, relationship attributes and relationship labels between the nodes are determined, and further based on the nodes, the node attributes and the node labels, the association relations, the relationship attributes and the relationship labels between the nodes are determined, a target federated knowledge graph containing associations between companies and companies, between companies and individuals, between individuals and individuals is built, for example: the method comprises the steps of connecting to a machine deploying a neo4j database through a ssh command of Linux, copying different entity files and border system files into a neo4j machine through a scp command of Linux, closing neo4j service through a neo4j stop command, further importing the entity files and the border system files through a neo4j-admin import command, and restarting neo4j through a neo4j start command to construct the target knowledge federation map after importing is completed.
The embodiment of the application provides a method for establishing a federated knowledge graph, the method comprises the steps of firstly obtaining multi-source heterogeneous data of a target field, generating each multi-source data table based on the multi-source heterogeneous data, and further performing classification analysis on each multi-source data table to obtain target graph information, wherein the target graph information comprises graph entities, entity attributes, graph edges and edge attributes of different types, further, different entity files and edge relationship files are generated based on the target graph information, and then the target federated knowledge graph is established based on the different entity files and edge relationship files, so that classification analysis on the data tables corresponding to the multi-source heterogeneous data is realized, and information such as the graph entities, the entity attributes, the graph edges and the edge attributes of different types corresponding to the established graph is selected, therefore, different entity files and side relation files are generated, and a target federal knowledge map containing various data is constructed, so that business personnel can better expand the business based on the associated information of the federal knowledge map.
Further, referring to fig. 2, step S20: classifying and analyzing the multi-source data table to obtain target map information, which specifically comprises the following steps:
step S21, in the multi-source data table, in combination with the service scene of the current target field, selecting each target field with query frequency meeting a preset frequency threshold as the map entities of different types, and determining entity attributes corresponding to each map entity;
step S22, based on each map entity, selecting each target field associated with the same type of map entity and different types of map entities as the map edges of different types in the multi-source data table, and determining the edge attribute corresponding to each map edge, wherein the map edges represent the association relationship between map entities.
In this embodiment, specifically, in combination with a service scenario of a current target field, a target field with a query frequency exceeding a preset frequency threshold in the current service scenario is selected from the multi-source data table, and then each target field is classified and analyzed to obtain classified field information, and further, from the classified field information, field information meeting entity construction conditions is selected as a graph entity, further, based on each graph entity, a preset number of field information for describing the graph entity is selected as an entity attribute of the graph entity, further, after each graph entity is determined, each multi-source data table is subjected to overall analysis, and field information associated with the same graph entity and different graph entities is selected as graph edges, that is, the graph edges are used for representing an association relationship between the entities, and then selecting the field information with preset quantity for describing the corresponding graph edge as the edge attribute of the graph entity.
Through the scheme, namely, in the multi-source data table, in combination with the service scene of the current target field, each target field with the query frequency meeting the preset frequency threshold is selected as the map entity of different types, and determining entity attributes corresponding to each of the map entities, and based on each of the map entities, selecting target fields associated with the same type of map entities and different types of map entities from the multi-source data table as the different types of map edges, determining edge attributes corresponding to the map edges, wherein, the map edge represents the incidence relation between map entities, realizes the aggregation of various data, determines the entities by combining the service scene of the current target field, and the relevance among the entities is analyzed, so that the federal knowledge graph can be constructed based on the relevance among the entities and the respective entities.
Further, referring to fig. 3, based on the first embodiment in the present application, in another embodiment of the present application, after the step of constructing the target federal knowledge graph based on the different entity documents and the side relationship documents, the method further includes:
step A10, constructing a visual webpage of the target federal knowledge graph;
in this embodiment, it should be noted that, before the visualization web page is constructed, a developer encapsulates the query statement of the target federal knowledge graph into a form of a function interface, so that a user calls the interface to obtain data in the target federal knowledge graph when clicking a front end (visualization web page), for example: and node query, namely, transmitting the name of a specific node from the outside, querying all nodes with the distance of 1 from the central node, classifying according to the edge relation type, and returning the result. And (4) graph algorithm query, namely executing graph algorithms such as community discovery, shortest path, node similarity and the like by generating query statements through the names of a plurality of nodes transmitted from the outside, and returning an execution result.
Step A20, acquiring an operation instruction of a target user on the visual webpage;
in this embodiment, it should be noted that the operation instruction includes an operation instruction such as a single click, a double click, and a right key.
Step A30, searching target return information corresponding to the operation command in the target federal knowledge graph;
in this embodiment, it should be noted that the data returned by different operation instructions is different. For example, when a target user is a click instruction, a click coordinate position is detected, whether the click coordinate position belongs to a certain node or a certain side is judged, so that corresponding field information of the node or the side is obtained in the target federal knowledge graph, when the target user is a double-click instruction, a double-click coordinate position is detected, whether the double-click coordinate position belongs to a certain node is judged, when the double-click coordinate position is in the node, the node is expanded, that is, all nodes with the distance of 1 from the node are obtained in the target federal knowledge graph, and a result is returned after classification according to the relationship type of the side between the nodes.
And A40, visually drawing the visual webpage through a preset drawing algorithm based on the target return information.
In this embodiment, it should be noted that the preset drawing algorithm includes a force-directed graph layout algorithm, where the force-directed graph layout algorithm refers to calculating a resultant force of an attractive force and a repulsive force by taking nodes as electric charges and calculating each node, and then moving the position of the node by the resultant force.
Step A40: based on the target return information, performing visual drawing through a preset drawing algorithm to obtain a target plotting drawing, which specifically comprises the following steps:
step A41, importing the target return information into a preset constructed force guide diagram layout;
step A42, dynamically calling a preset drawing function in the force guide graph layout, and drawing the target return information based on the drawing function.
In this embodiment, specifically, a force guide graph layout is created, a drawing function is set in the force guide graph layout, the target return information is further led into the force guide graph layout, the target return information is visually drawn based on the drawing function, and the coordinate position of the node is dynamically adjusted in combination with a preset mechanical simulation model.
Wherein, the step A42: based on the drawing function, drawing the target return information to obtain the target plotting chart, which specifically comprises:
step A421, if the target return information has a map entity, performing node drawing in a preset node style based on the map entity data;
step A422, if the target return information has a map edge, drawing the map edge according to a preset edge style based on the number of the map edges between map entities.
In this embodiment, specifically, when the target return information exists in a graph entity, when a node (graph entity) is drawn, styles such as color, highlight and the like drawn by the node are controlled according to a type of the graph entity, for example, a 2D Canvas state machine is controlled by using an arc function, the graph entity includes entities of an individual and a company, and the graph entity corresponding to the individual and the picture entity corresponding to the company can be drawn by using different colors.
Further, when the target return information has a graph edge, and when the edge (graph edge) is drawn, different drawing modes are adopted for drawing according to the number of edges between nodes, and the specific drawing process is as follows: under the condition of single edge among nodes, drawing a connecting line according to a preset edge style field, for example: the 2D Canvas state machine is controlled to be drawn by adopting a lineTo function, under the condition of multiple edges among nodes, the first edge is drawn and executed by adopting a single-edge method, the other edges are drawn in a preset connecting line drawing mode, a Bessel quadratic curve is drawn by adopting a quadraticCurveTo method, for example, A and B are friends and trade relations, the edge corresponding to the friend relation can be drawn by adopting a straight line, and the edge corresponding to the trade relation can be drawn by adopting a curve.
Through the scheme, the visual webpage of the target federal knowledge graph is constructed, the operation instruction of the visual webpage of the target user is obtained, further, the target returned information corresponding to the operation instruction is inquired in the target federal knowledge graph, and then based on the target returned information, visual drawing is carried out in the visual webpage through a preset drawing algorithm, so that the visual drawing of the knowledge graph is realized, the threshold of using the technology by service personnel is reduced, and the federal knowledge graph can be conveniently and quickly utilized for service expansion for the service personnel with shallow technical bases.
Further, referring to fig. 4, fig. 4 is a system framework diagram of the federal knowledge graph construction method of the present application, specifically, data (multi-source heterogeneous data) of different data sources are collected, a plurality of data tables (multi-source data tables) are generated, based on each multi-source data table, an NLP technique is used to extract and obtain a graph entity and an association relationship between graph entities, further, a plurality of entity files and edge relationship files are generated, so that based on each entity file and edge relationship file, constructing the target federal knowledge graph through the neo4j graph database, storing the target federal knowledge graph, designing an interface function corresponding to a query sentence, and then clicking on a front-end page by a user to trigger a corresponding interface function so as to obtain target return data corresponding to the target federal knowledge graph, and further performing visual rendering on the target return data.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a federal knowledge graph building device of a hardware operating environment according to an embodiment of the present application.
As shown in fig. 5, the federal knowledge graph building apparatus may include: a processor 1001, such as a CPU, a memory 1005, and a communication bus 1002. The communication bus 1002 is used for realizing connection communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM memory or a non-volatile memory such as a disk memory. The memory 1005 may alternatively be a memory device separate from the processor 1001 described above.
Optionally, the federal knowledge graph build device can further include a rectangular user interface, a network interface, a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, a WiFi module, and the like. The rectangular user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also comprise a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WIFI interface).
Those skilled in the art will appreciate that the federated knowledge map building apparatus architecture illustrated in FIG. 5 does not constitute a limitation of the federated knowledge map building apparatus, and may include more or fewer components than those illustrated, or some components in combination, or a different arrangement of components.
As shown in fig. 5, the memory 1005, which is a type of computer storage medium, may include an operating system, a network communication module, and a federal knowledge graph build program. The operating system is a program that manages and controls the hardware and software resources of the federated knowledge graph building apparatus, supporting the operation of the federated knowledge graph building program as well as other software and/or programs. The network communication module is used for realizing communication among components in the memory 1005 and communication with other hardware and software in the federal knowledge graph building system.
In the federal knowledge graph building apparatus shown in fig. 5, the processor 1001 is configured to execute the federal knowledge graph building program stored in the memory 1005 to implement the steps of any one of the above-described methods of constructing a federal knowledge graph.
The specific implementation of the federal knowledge graph construction device of the application is basically the same as that of each embodiment of the federal knowledge graph construction method, and details are not repeated herein.
In addition, please refer to fig. 6, fig. 6 is a schematic diagram of functional modules of the federal knowledge graph construction apparatus in the present application, and the present application further provides a federal knowledge graph construction system, which includes:
the acquisition module is used for acquiring multi-source heterogeneous data and generating each multi-source data table based on the multi-source heterogeneous data;
the analysis module is used for classifying and analyzing each multi-source data table to obtain target map information, wherein the target map information comprises map entities of different types, entity attributes, map edges of different types and edge attributes;
the generating module is used for generating different entity files and edge relation files based on the target map information;
and the construction module is used for constructing the target federal knowledge graph based on the different entity files and the side relation files.
Optionally, the analysis module is further configured to;
in the multi-source data table, in combination with a service scene of a current target field, selecting each target field with query frequency meeting a preset frequency threshold as the map entities of different types, and determining entity attributes corresponding to each map entity;
based on each map entity, selecting target fields associated with the map entities of the same type and different types as map edges of different types in the multi-source data table, and determining edge attributes corresponding to the map edges, wherein the map edges represent association relations among the map entities.
Optionally, the generating module is further configured to;
generating each entity file according to target fields corresponding to different map entities in the target map information;
and generating each edge relation file according to the target fields corresponding to different map edges in the target map information.
Optionally, the obtaining module is further configured to;
and processing the multi-source heterogeneous data by a preset natural language processing method to generate each multi-source data table.
Optionally, the federated knowledge graph building system is further configured to:
constructing a visual webpage of the target federal knowledge graph;
acquiring an operation instruction of a target user on the visual webpage;
inquiring target return information corresponding to the operation instruction in the target federal knowledge graph;
and visually drawing in the visual webpage through a preset drawing algorithm based on the target return information.
Optionally, the federated knowledge graph building system is further configured to:
importing the target return information into a preset constructed force guide diagram layout;
and dynamically calling a preset drawing function in the force guide graph layout, and drawing the target return information based on the drawing function.
Optionally, the federated knowledge graph building system is further configured to:
if the target return information exists in the map entity, node drawing is carried out according to a preset node style based on the map entity data;
and if the target return information has map edges, performing edge drawing according to a preset edge style based on the number of the map edges between map entities.
The specific implementation of the federal knowledge graph construction system of the application is basically the same as that of each embodiment of the federal knowledge graph construction method, and details are not repeated herein.
The present application provides a storage medium, which is a computer-readable storage medium, and the computer-readable storage medium stores one or more programs, which are also executable by one or more processors for implementing the steps of the federal knowledge graph construction method in any one of the above.
The specific implementation of the computer-readable storage medium of the present application is substantially the same as the embodiments of the federated knowledge graph construction method described above, and is not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A method for constructing a federated knowledge graph is characterized by comprising the following steps:
the method comprises the steps of obtaining multi-source heterogeneous data of a target field, and generating each multi-source data table based on the multi-source heterogeneous data;
classifying and analyzing each multi-source data table to obtain target map information, wherein the target map information comprises map entities of different types, entity attributes, map edges of different types and edge attributes;
generating different entity files and edge relation files based on the target map information;
and constructing a target federal knowledge graph based on the different entity files and the edge relationship files.
2. The federal knowledge graph construction method as claimed in claim 1, wherein said step of classifying and analyzing each of said multi-source data sheets to obtain target graph information comprises:
in the multi-source data table, in combination with a service scene of a current target field, selecting each target field with query frequency meeting a preset frequency threshold as the map entities of different types, and determining entity attributes corresponding to each map entity;
based on each map entity, selecting target fields associated with the map entities of the same type and different types as map edges of different types in the multi-source data table, and determining edge attributes corresponding to the map edges, wherein the map edges represent association relations among the map entities.
3. The federated knowledge graph construction method of claim 1, wherein the step of generating different entity documents and side relationship documents based on the target graph information includes:
generating each entity file according to target fields corresponding to different map entities in the target map information;
and generating each edge relation file according to the target fields corresponding to different map edges in the target map information.
4. The federal knowledge graph building method as defined in claim 1, wherein said step of generating each multi-source data sheet based on said multi-source heterogeneous data comprises:
and processing the multi-source heterogeneous data by a preset natural language processing method to generate each multi-source data table.
5. The federated knowledge graph building method of claim 1, wherein after the step of building a target federated knowledge graph based on the different entity documents and side relationship documents, further comprises:
constructing a visual webpage of the target federal knowledge graph;
acquiring an operation instruction of a target user on the visual webpage;
inquiring target return information corresponding to the operation instruction in the target federal knowledge graph;
and visually drawing in the visual webpage through a preset drawing algorithm based on the target return information.
6. The federal knowledge graph construction method as claimed in claim 5, wherein said step of performing a visual rendering based on said target return information by a preset rendering algorithm to obtain a target rendering map comprises:
importing the target return information into a preset constructed force guide diagram layout;
and dynamically calling a preset drawing function in the layout of the force guide graph, and drawing the target return information based on the drawing function.
7. The federal knowledge graph construction method as defined in claim 6, wherein said step of plotting said target return information based on said plotting function to obtain said target plot chart comprises:
if the target return information has a graph entity, node drawing is carried out according to a preset node style based on the graph entity data;
and if the target return information has map edges, performing edge drawing according to a preset edge style based on the number of the map edges between map entities.
8. The federated knowledge graph construction system is characterized by comprising the following steps:
the acquisition module is used for acquiring multi-source heterogeneous data and generating each multi-source data table based on the multi-source heterogeneous data;
the analysis module is used for classifying and analyzing each multi-source data table to obtain target map information, wherein the target map information comprises map entities of different types, entity attributes, map edges of different types and edge attributes;
the generating module is used for generating different entity files and edge relation files based on the target map information;
and the construction module is used for constructing the target federal knowledge graph based on the different entity files and the edge relation files.
9. The utility model provides a federation knowledge map construction equipment, its characterized in that federation knowledge map construction equipment includes: a memory, a processor, and a federal knowledge graph build program stored in the memory,
the federal knowledge graph construction program being executed by the processor for implementing the method of constructing a federal knowledge graph as claimed in any of claims 1 to 7.
10. A storage medium which is a computer-readable storage medium having stored thereon a federal knowledge graph construction program, the federal knowledge graph construction program being executed by a processor for implementing the method of constructing a federal knowledge graph as claimed in any one of claims 1 to 7.
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| CN115329612A (en) * | 2022-10-17 | 2022-11-11 | 中国电子科技集团公司信息科学研究院 | Signal processing heterogeneous integrated micro-system knowledge graph construction method and simulation method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115329612A (en) * | 2022-10-17 | 2022-11-11 | 中国电子科技集团公司信息科学研究院 | Signal processing heterogeneous integrated micro-system knowledge graph construction method and simulation method |
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