CN114020852A - Knowledge graph display method and device - Google Patents
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Abstract
The invention provides a display method and a device of a knowledge graph, wherein the method comprises the following steps: writing service data in a Neo4j database in real time by adopting a Flink real-time flow; the problem of slow data writing in the prior art can be solved by utilizing a visual interface of a graph database to display the knowledge graph.
Description
Technical Field
The invention relates to the field of big data, in particular to a display method and device of a knowledge graph.
Background
Extracting service data for processing, organically organizing fragmented data by establishing associated links among the data to provide convenience for searching, mining, analyzing and the like, storing and representing knowledge in a graph form, utilizing a graph-based data structure of a graph, and comprising nodes (points) and edges (edges), with the assistance of the graph, a search engine can return more accurate and structured information according to semantic information behind user query.
In the prior art, Neo4j is generally adopted for implementation, a query language used in Neo4j supports nested query and conditional query, a visual interface is good, the defects are that the query capability is limited (the requirement for daily query functions is not problematic), and the efficiency of writing data in Neo4j in real time is slow.
Disclosure of Invention
The invention mainly aims to provide a method and a device for displaying a knowledge graph, which are used for solving the problem that data writing is slow in the prior art.
In order to achieve the above object, according to an aspect of the present invention, there is provided a display method of a knowledge graph, including: writing service data in a Neo4j database in real time by adopting a Flink real-time flow; and displaying the knowledge graph by using a visual interface of the graph database.
Optionally, before displaying the knowledge-graph by using the visualization interface of the graph database, the method further comprises: and establishing a relationship between the nodes for the service data in the graph database by utilizing the expansibility of the graph database.
Optionally, after creating the nodes and the relationships between the nodes for the traffic data in the graph database, the method further includes: and establishing a knowledge graph comprising the established nodes and the relationships among the nodes.
Optionally, creating a relationship between nodes for the service data in the graph database by using the extensibility of the graph database, including: creating nodes for the service data in a graph database; deleting the created nodes in the graph database; performing fuzzy matching in a graph database; and matching the relationship between the nodes in the graph database.
Optionally, by using a Flink real-time stream, when data is written in the Neo4j graph database in real time, the service data may be parsed by using a json util tool class, and after processing, the data is written in the graph database in real time by using a Flink-DataStream: analyzing original service data by using a custom tool; analyzing required data fields and data from original service data according to a service data structure; cleaning and filtering the data by using a Flink operator according to a service cleaning rule, and testing the accuracy and uniqueness of the check data; and writing the obtained result data into a Neo4j graph database in real time by using a Flink real-time flow function so as to query the data in the graph database by sentences and display the data.
Optionally, before writing data in Neo4j graph database in real time using Flink real time stream, the method further comprises: defining a funnation implementation method and logic codes; defining a main class, processing data by using Flink-DataStream and writing the data into Neo4j in real time; defining a custom tool class for analyzing Json data; defining a connection tool class; and defining a task parameter setting tool class.
Optionally, before writing data in Neo4j graph database in real time using Flink real time stream, the method further comprises: and setting by adopting a checkpoint mechanism so as to save the state of the stream processing operation.
In order to achieve the above object, according to an aspect of the present invention, there is also provided a knowledge-graph displaying apparatus, including: the writing module is used for writing business data in a Neo4j graph database in real time by adopting a Flink real-time stream; and the display module is used for displaying the knowledge graph by using a visual interface of the graph database.
Optionally, the display module is further configured to create a relationship between the nodes for the service data in the graph database by using the extensibility of the graph database before displaying the knowledge graph by using the visualization interface of the graph database.
Optionally, the presentation module is further configured to, after creating the node and the relationship between nodes for the service data in the graph database, establish a knowledge graph including the established node and the relationship between nodes.
Optionally, the display module is further configured to create a node for the service data in the graph database when creating a relationship between nodes and a node for the service data in the graph database by using the extensibility of the graph database; deleting the created nodes in the graph database; performing fuzzy matching in a graph database; and matching the relationship between the nodes in the graph database.
Optionally, the writing module is further configured to use a Flink real-time stream, when data is written in the Neo4j database in real time, parse the service data using the json util tool class, and write the data into the database in real time using the Flink-DataStream after processing: analyzing original service data by using a custom tool; analyzing required data fields and data from original service data according to a service data structure; cleaning and filtering the data by using a Flink operator according to a service cleaning rule, and testing the accuracy and uniqueness of the check data; and writing the obtained result data into a Neo4j graph database in real time by using a Flink real-time flow function so as to query the data in the graph database by sentences and display the data.
Optionally, the writing module is further configured to define a funnation implementation method and logic codes before writing data in the Neo4j graph database in real time by using a Flink real-time stream; defining a main class, processing data by using Flink-DataStream and writing the data into Neo4j in real time; defining a custom tool class for analyzing Json data; defining a connection tool class; and defining a task parameter setting tool class.
Optionally, the writing module is further configured to set, before writing data in the Neo4j graph database in real time by using the Flink real-time stream, using a checkpoint mechanism to save a state of the stream processing runtime.
According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the above method through the computer program.
According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the steps of any of the embodiments of the method described above.
By applying the technical scheme of the invention, business data is written in a Neo4j graph database in real time by adopting a Flink real-time stream; by utilizing a visual interface of a graph database to display the knowledge graph, the Flink-DataStream has the capability of processing business data at high speed in real time, so that the problem of slow data writing in the prior art can be solved.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 shows a flow diagram of a method of knowledge-graph presentation in accordance with the present invention; and
fig. 2 shows a schematic view of a presentation apparatus of a knowledge-graph according to the invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions of the present invention better understood, 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances for describing embodiments of the invention herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In order to solve the mentioned problems, according to an aspect of the present application, there is provided a method of displaying a knowledge-graph, as shown in fig. 1, comprising:
and step S102, writing service data in a Neo4j database in real time by adopting a Flink real-time flow.
The programming model can be divided into three parts, namely a Data Source Operator, a Data conversion Operator and a Data stream output Operator, wherein the Data Source Operator, the Data conversion Operator and the Data stream output Operator are used for processing Data in real time.
Neo4j is a high-performance NOSQL graph database that stores structured data on a network to form graphs rather than directly in tables, and Neo4j can also be considered as a high-performance graph engine with all the features of a sophisticated database, such as: transactions, indexes, etc. programmers work under an object-oriented, flexible network architecture rather than a strict, static table, but they can enjoy all the benefits of having a full transactional nature, an enterprise-level database. Two basic data types are contained in a graph: nodes and Relationships, wherein the Nodes and the Relationships comprise attributes in the form of keys and values, and the Nodes are connected through the Relationships defined by the Relationships to form a relational network structure.
And (3) node: the basic elements that make up a graph are nodes and relationships, which in Neo4j may both contain attributes, nodes are often used to represent some entities, but dependencies may also represent entities.
The relationship is as follows: relationships between nodes are an important part of graph databases, and many associated data such as node sets, relationship sets and their attribute sets can be found through the relationships.
The characteristics of Neo4j are as follows: full ACID support, high availability, nodes and relationships that easily scale to billions of levels), high speed retrieval of data through traversal tools.
Optionally, by using a Flink real-time stream, when data is written in the Neo4j graph database in real time, the service data may be parsed by using a json util tool class, and after processing, the data is written in the graph database in real time by using a Flink-DataStream: analyzing original service data by using a custom tool; analyzing required data fields and data from original service data according to a service data structure; cleaning and filtering the data by using a Flink operator according to a service cleaning rule, and testing the accuracy and uniqueness of the check data; and writing the obtained result data into a Neo4j graph database in real time by using a Flink real-time flow function so as to query the data in the graph database by sentences and display the data.
Optionally, before writing data in Neo4j graph database in real time using Flink real time stream, the method further comprises: defining a funnation implementation method and logic codes; defining a main class, processing data by using Flink-DataStream and writing the data into Neo4j in real time; defining a custom tool class for analyzing Json data; defining a connection tool class; and defining a task parameter setting tool class. Writing a Flink code according to requirements, creating nodes and relations and writing data in real time:
1) the user-defined funnation implementation method and the logic code are as follows:
public class Neo4jSink extends RichSinkFunction<Dict>{}；
2) the main class processes the data using Flink and writes to Neo4j in real time:
public class DaRunFaNeo4j{}；
3) and analyzing the custom tool class of the Json data:
public class JsonUtil{}；
4) self-defining a connection tool class:
public class ZxDataUtil{}；
5) self-defining task parameter setting tool class:
public class FlinkUtil{}。
optionally, before writing data in Neo4j graph database in real time using Flink real time stream, the method further comprises: and setting by adopting a checkpoint mechanism so as to save the state of the stream processing operation.
Analyzing the processed data, writing the data in real time by using a Flink-DataStream, setting by adopting a checkpoint mechanism, and storing the state of the stream processing operation:
public static StreamExecutionEnvironment getEnv(){
StreamExecutionEnvironment env＝StreamExecutionEnvironment.getExecutionEnvironment()；
enable checkpoint, specifying the time interval (unit: ms, default 500 ms) to trigger checkpoint, default is env.
// set Checkpoint timeout to default to 10 minutes
env.getCheckpointConfig().setCheckpointTimeout(18PA03060)；
If there is a more recent save point, whether to rollback the job to that checkpoint
env.getCheckpointConfig().setPreferCheckpointForRecovery(true)；
// set number of checkpoint failures that can be allowed
env.getCheckpointConfig().setTolerableCheckpointFailureNumber(3)；
At least once
env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.AT_LEAST_ONCE)；
return env；
}
Adopting JsonUtil tool class to analyze data and processing the real-time written data by using Flink-DataStream:
and step S104, displaying the knowledge graph by using a visual interface of the graph database.
Optionally, before displaying the knowledge-graph by using the visualization interface of the graph database, the method further comprises: and establishing a relationship between the nodes for the service data in the graph database by utilizing the expansibility of the graph database.
Optionally, after creating the nodes and the relationships between the nodes for the traffic data in the graph database, the method further includes: and establishing a knowledge graph comprising the established nodes and the relationships among the nodes.
Optionally, creating a relationship between nodes for the service data in the graph database by using the extensibility of the graph database, including: creating nodes for the service data in a graph database; deleting the created nodes in the graph database; performing fuzzy matching in a graph database; and matching the relationship between the nodes in the graph database.
Creating nodes and establishing relationships
And performing conventional operation in the graph database by using different sentences according to different requirements.
Deleting a certain node:
match p ═ (pro: Property { name: "detailed in commodity" }) < [ r ] - (n) delete r, pro
Fuzzy matching:
MATCH (n: product) where n.name ═ Mongolian
And (3) association matching:
MATCH w ═ product: [ b: "Brand' ] - > (Brand: Brand) with product, Brand MATCH p: (product: product) - [ c:" first class classification "] - > (class1: FirstCategory) with product, class1, Brand MATCH q ═ product: [ ww:" second class "] - > (class2: second class) return Brand, class1, class2, product.
The method can be used for performing real-time streaming data processing based on Flink and performing node expansion and data association linkage based on Neo4 j.
The purpose of the invention is as follows: 1) adopting Flink-DataStream to process service data in real time to ensure that the data is written continuously; 2) a Neo4j graph database is used to extend more nodes and relationships.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
In order to achieve the above object, according to an aspect of the present invention, there is also provided a knowledge-graph displaying apparatus, as shown in fig. 2, including:
the writing module 21 is configured to write service data in a Neo4j graph database in real time by using a Flink real-time stream; and the display module 23 is used for displaying the knowledge graph by using a visual interface of the graph database.
Optionally, the display module is further configured to create a relationship between the nodes for the service data in the graph database by using the extensibility of the graph database before displaying the knowledge graph by using the visualization interface of the graph database.
Optionally, the presentation module is further configured to, after creating the node and the relationship between nodes for the service data in the graph database, establish a knowledge graph including the established node and the relationship between nodes.
Optionally, the display module is further configured to create a node for the service data in the graph database when creating a relationship between nodes and a node for the service data in the graph database by using the extensibility of the graph database; deleting the created nodes in the graph database; performing fuzzy matching in a graph database; and matching the relationship between the nodes in the graph database.
Optionally, the writing module is further configured to use a Flink real-time stream, when data is written in the Neo4j database in real time, parse the service data using the json util tool class, and write the data into the database in real time using the Flink-DataStream after processing: analyzing original service data by using a custom tool; analyzing required data fields and data from original service data according to a service data structure; cleaning and filtering the data by using a Flink operator according to a service cleaning rule, and testing the accuracy and uniqueness of the check data; and writing the obtained result data into a Neo4j graph database in real time by using a Flink real-time flow function so as to query the data in the graph database by sentences and display the data.
Optionally, the writing module is further configured to define a funnation implementation method and logic codes before writing data in the Neo4j graph database in real time by using a Flink real-time stream; defining a main class, processing data by using Flink-DataStream and writing the data into Neo4j in real time; defining a custom tool class for analyzing Json data; defining a connection tool class; and defining a task parameter setting tool class.
Optionally, the writing module is further configured to set, before writing data in the Neo4j graph database in real time by using the Flink real-time stream, using a checkpoint mechanism to save a state of the stream processing runtime.
The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In the description of the present invention, it is to be understood that the orientation or positional relationship indicated by the orientation words such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc. are usually based on the orientation or positional relationship shown in the drawings, and are only for convenience of description and simplicity of description, and in the case of not making a reverse description, these orientation words do not indicate and imply that the device or element being referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore, should not be considered as limiting the scope of the present invention; the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for displaying a knowledge graph is characterized by comprising the following steps:
writing service data in a Neo4j database in real time by adopting a Flink real-time flow;
and displaying the knowledge graph by using a visual interface of the graph database.
2. The method of claim 1, wherein prior to displaying a knowledge-graph using a visualization interface of the graph database, the method further comprises:
and establishing a relationship between nodes for the service data in the graph database by utilizing the expansibility of the graph database.
3. The method of claim 2, wherein after creating nodes and relationships between nodes for traffic data in the graph database, the method further comprises:
establishing the knowledge-graph comprising the established nodes and relationships between the nodes.
4. The method according to claim 2, wherein using extensibility of said graph database to create nodes and relationships between nodes for traffic data in said graph database comprises at least one of:
creating nodes for the service data in the graph database;
deleting the created nodes in the graph database;
performing fuzzy matching in the graph database;
and matching the relationship between the nodes in the graph database.
5. The method according to claim 1, wherein writing data in Neo4j graph database in real time using Flink real time stream comprises:
analyzing original service data by using a custom tool;
analyzing required data fields and data from original service data according to a service data structure;
cleaning and filtering the data by using a Flink operator according to a service cleaning rule, and testing the accuracy and uniqueness of the check data;
and writing the obtained result data into a Neo4j graph database in real time by using a Flink real-time flow function so as to query the data in the graph database by sentences and display the data.
6. The method according to claim 5, wherein before writing data in real-time in Neo4j graph database using Flink real-time streaming, the method further comprises:
defining a funnation implementation method and logic codes;
defining a main class, processing data by using Flink-DataStream and writing the data into Neo4j in real time;
defining a custom tool class for analyzing Json data;
defining a connection tool class;
and defining a task parameter setting tool class.
7. The method according to claim 5, wherein before writing data in real-time in Neo4j graph database using Flink real-time streaming, the method further comprises:
and setting by adopting a checkpoint mechanism so as to save the state of the stream processing operation.
8. A knowledge graph display apparatus, comprising:
the writing module is used for writing business data in a Neo4j graph database in real time by adopting a Flink real-time stream;
and the display module is used for displaying the knowledge graph by utilizing the visual interface of the graph database.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein the program when executed performs the method of any of the preceding claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the method of any of the preceding claims 1 to 7 by means of the computer program.
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