Knowledge graph construction method and device, storage medium and electronic equipment

Abstract

The application relates to the computer technical field, and provides a knowledge graph construction method and device, a storage medium and an electronic device. The knowledge graph construction method comprises the following steps: displaying a model editing interface, and generating an ontology model of a knowledge graph in response to a model editing operation performed on the model editing interface; displaying an extraction rule configuration interface, and generating an extraction rule in response to an extraction rule configuration operation performed on the extraction rule configuration interface; converting original data into target data by executing the extraction rule; and constructing the knowledge graph according to the target data and the ontology model. The method visualizes the construction process of the knowledge graph, so that an ordinary user can independently construct the knowledge graph by only performing some interface operations, and does not need to rely on a developer to write relevant codes every time for construction, thereby significantly reducing the technical threshold for constructing the knowledge graph and improving the efficiency of constructing the knowledge graph.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a knowledge graph construction method and apparatus, storage medium and electronic device. Background Technology

[0002] Knowledge graphs can be viewed as semantic networks with a graph structure. Because they can effectively express the semantic relationships between various entities in reality, they have been widely used in various fields. However, in current technologies, ordinary users cannot build knowledge graphs independently; they can only submit building requirements to developers, who then write code to implement the knowledge graph construction. Summary of the Invention

[0003] The purpose of this application is to provide a knowledge graph construction method, apparatus, storage medium, and electronic device to improve the above-mentioned technical problems.

[0004] To achieve the above objectives, this application provides the following technical solution: In a first aspect, embodiments of this application provide a knowledge graph construction method, comprising: displaying a model editing interface and, in response to a model editing operation performed on the model editing interface, generating an ontology model of the knowledge graph; displaying an extraction rule configuration interface and, in response to an extraction rule configuration operation performed on the extraction rule configuration interface, generating extraction rules; wherein the extraction rules are mapping rules for converting raw data in a data source into target data in the knowledge graph; converting the raw data into the target data by executing the extraction rules; and constructing the knowledge graph based on the target data and the ontology model.

[0005] The above method constructs a knowledge graph by displaying a model editing interface and an extraction rule configuration interface, and responding to model editing and extraction rule configuration operations performed on the interface. This visualizes the knowledge graph construction process, allowing ordinary users to construct the required knowledge graph independently by performing only some interface operations. This eliminates the need for developers to write related code (e.g., code describing how to extract data) for each construction, thus significantly reducing the technical threshold for constructing knowledge graphs and improving the efficiency of knowledge graph construction.

[0006] In one implementation of the first aspect, the method further includes: displaying an extraction process configuration interface and responding to an extraction process configuration operation executed on the extraction process configuration interface to generate at least one extraction process; wherein each extraction process is a transformation process from raw data in a corresponding data source to target data in the knowledge graph corresponding to that data source, and each extraction process has its own extraction rule configuration interface and contains its own extraction rules; the step of transforming the raw data into the target data by executing the extraction rules includes: transforming the raw data in the corresponding data source of each extraction process into target data corresponding to that data source by executing each extraction process; wherein, when each extraction process is executed, the extraction rules contained in that extraction process are executed.

[0007] In the above implementation, since the original data from different data sources may have different structures, the extraction rules used to extract the original data from different data sources may have different implementation forms. Therefore, different extraction processes can be configured to extract the data separately.

[0008] In one implementation of the first aspect, executing the extraction process includes: constructing a data extraction task based on the extraction process and executing the data extraction task; the method further includes: responding to an extraction process update operation executed on the extraction process configuration interface, generating an updated extraction process; constructing a new data extraction task based on the updated extraction process, and executing the new data extraction task.

[0009] In one implementation of the first aspect, executing the extraction process includes: constructing a data extraction task based on the extraction process and executing the data extraction task; the method further includes: responding to an extraction rule update operation executed on the extraction rule configuration interface, generating an updated extraction rule; and loading the updated extraction rule into the data extraction task corresponding to its extraction process.

[0010] In both of the above implementation methods, since the extraction process or extraction rules can be dynamically changed, users can make timely adjustments when they find that their designed extraction process or extraction rules are unreasonable, thereby quickly and completely verifying the knowledge graph.

[0011] Furthermore, if only some extraction rules are changed, the updated extraction rules only need to be loaded into the data extraction task corresponding to its extraction process. There is no need to rebuild the data extraction task (rebuilding the data extraction task is time-consuming), thus enabling lightweight and real-time adjustment of extraction rule changes.

[0012] In one implementation of the first aspect, the method further includes: displaying a preprocessing rule configuration interface and responding to a preprocessing rule configuration operation performed on the preprocessing rule configuration interface to generate preprocessing rules; the step of converting the original data into the target data by executing the extraction rules includes: preprocessing the original data by executing the preprocessing rules to obtain processed original data; and converting the processed original data into the target data by executing the extraction rules.

[0013] In the above implementation, data preprocessing is achieved by displaying the preprocessing rule configuration interface and responding to the preprocessing rule configuration operations made on the interface. This is beneficial to improving the efficiency of subsequent data extraction or improving the effect of data extraction. Furthermore, the data preprocessing is also implemented in a visual way, making it easy for ordinary users to use.

[0014] In one implementation of the first aspect, the extraction rule includes metadata of the original data, metadata of the target data, and a mapping relationship between the metadata of the original data and the metadata of the target data. The extraction rule configuration interface includes: a first control for selecting metadata of the original data in the extraction rule, a second control for selecting metadata of the target data in the extraction rule, and a third control for selecting the mapping relationship in the extraction rule.

[0015] In the above implementation method, the extraction rules are broken down into three basic components: the metadata of the original data, the metadata of the target data, and the mapping relationship between the two. Three corresponding controls are set on the extraction rule configuration interface, so that users can complete the configuration of the extraction rules by simply performing a selection operation on the controls. The technical threshold is low and the configuration efficiency is high.

[0016] In one implementation of the first aspect, constructing the knowledge graph based on the target data and the ontology model includes: converting the ontology model into a data pattern in a graph database; and storing the target data in the graph database according to the data pattern to obtain the knowledge graph.

[0017] In the above implementation, knowledge graphs are constructed based on graph databases, which facilitates the storage, maintenance, and display of knowledge graphs.

[0018] In one implementation of the first aspect, the step of converting the ontology model into a data schema in a graph database includes: converting the ontology model into an intermediate data schema that is independent of the database; and converting the intermediate data schema into a data schema in the graph database.

[0019] In the above implementation, the ontology model is not directly converted into a data schema in the graph database. Instead, it is first converted into an intermediate data schema (e.g., a data schema described in JSON), and then the intermediate data schema is converted into a data schema in the graph database. Since the intermediate data schema is database-independent, it is easy to adapt to different graph databases, that is, it supports the easy construction of knowledge graphs in different graph databases according to user needs.

[0020] Secondly, embodiments of this application provide a knowledge graph construction apparatus, comprising: an ontology model editing module, configured to display a model editing interface and, in response to model editing operations performed on the model editing interface, generate an ontology model of the knowledge graph; an extraction rule configuration module, configured to display an extraction rule configuration interface and, in response to extraction rule configuration operations performed on the extraction rule configuration interface, generate extraction rules; wherein the extraction rules are mapping rules for converting raw data in a data source into target data in the knowledge graph; an extraction rule execution module, configured to convert the raw data into the target data by executing the extraction rules; and a knowledge graph forming module, configured to construct the knowledge graph based on the target data and the ontology model.

[0021] Thirdly, embodiments of this application provide a computer program product, including computer program instructions, which, when read and executed by a processor, perform the method provided in the first aspect or any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when read and executed by a processor, perform the method provided in the first aspect or any possible implementation thereof.

[0023] Fifthly, embodiments of this application provide an electronic device, including: a memory and a processor, wherein the memory stores computer program instructions, and the computer program instructions are read and executed by the processor to perform the method provided in the first aspect or any possible implementation of the first aspect. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1A flowchart illustrating a knowledge graph construction method provided in this application embodiment; Figure 2 A schematic diagram illustrating the working principle of a knowledge graph construction tool provided in this application embodiment; Figure 3 A schematic diagram of a model editing interface provided in an embodiment of this application; Figure 4 A schematic diagram of an extraction rule configuration interface provided in an embodiment of this application; Figure 5 A schematic diagram of an extraction rule model provided in an embodiment of this application; Figure 6 A schematic diagram of a project configuration interface provided in an embodiment of this application; Figure 7 This is an interface for displaying a knowledge graph using the Nebula database. Figure 8 A schematic diagram of a preprocessing rule configuration interface provided in an embodiment of this application; Figure 9 A schematic diagram of an extraction process configuration interface provided in an embodiment of this application; Figure 10 A module diagram of a knowledge graph construction apparatus provided in an embodiment of this application; Figure 11 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0027] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0028] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0029] Figure 1 A flowchart illustrating a knowledge graph construction method provided in this application embodiment. Figure 2 This is a schematic diagram illustrating the working principle of a knowledge graph construction tool provided in an embodiment of this application. The tool includes three components: an ontology modeling tool, a rule configuration component, and an extraction engine, and can be used to execute... Figure 1 The knowledge graph construction methods in [the context of the text] are introduced in [the text]. Figure 1 The methods and their possible implementations will also be discussed in conjunction with... Figure 2 To elaborate.

[0030] Knowledge graph building tools can be deployed on, but are not limited to, [platforms]. Figure 11 The electronic device shown is described in detail in the following text. Figure 11 The explanation. Furthermore, it should be understood that, Figure 1 The knowledge graph construction method in the middle does not necessarily have to be by Figure 2 The knowledge graph building tools in a framework, such as ontology modeling tools, rule configuration components, and extraction engines, can be distributed across different applications rather than integrated into a single application (knowledge graph building tool). Furthermore, the functionality of these tools may not be entirely consistent with the framework's intended purpose. Figure 2 The method of division in the middle.

[0031] Reference Figure 1 Knowledge graph construction methods include: Step S110: Display the model editing interface and respond to the model editing operations performed on the model editing interface to generate the ontology model of the knowledge graph.

[0032] After users begin building a knowledge graph, they can first enter the model editing interface provided by the ontology modeling tool, and perform model editing operations on the model editing interface to design the ontology model of the knowledge graph they want to build.

[0033] Users who build knowledge graphs can be people or computer programs that can simulate human behavior.

[0034] An ontology model can be a logical graph model that describes which entities (nodes), relations (edges between nodes), and attributes of entities or relations are contained in the knowledge graph. Entities and relations can also be referred to as elements in the ontology model. A knowledge graph can be viewed as the result of populating an ontology model with real data.

[0035] The layout of the model editing interface is not limited. Model editing operations can be visual operations on the model editing interface used to edit the ontology model, including but not limited to one or more operations such as clicking, long pressing, dragging, swiping, and inputting. These operations can be performed by one or more input devices such as a mouse, keyboard, or touch screen. Editing the ontology model here can refer to one or more actions such as creating a new ontology model (elements), modifying an ontology model (elements), and deleting an ontology model (elements).

[0036] The ontology modeling tool responds to user-made model editing operations, generates ontology models of knowledge graphs based on these operations, and displays the latest generated ontology models on the model editing interface, allowing users to view the modeling results in real time.

[0037] Figure 3 This is a schematic diagram of a model editing interface provided in an embodiment of this application. (Refer to...) Figure 3 The middle section displays the ontology model of the knowledge graph designed by the user. The model is named "Test". The model includes three entities: "Company", "Equipment" and "Project", as well as three relationships: "Management", "Registration" and "Application". There is a relationship "Management" between "Company" and "Equipment", a relationship "Registration" between "Company" and "Project", and a relationship "Application" between "Project" and "Equipment".

[0038] exist Figure 3 Right-click in the blank space in the middle. In the pop-up context menu (not shown in the image), you can select "Create Entity." Alternatively, select an already created entity and, in the pop-up context menu (not shown in the image), select "Create Relationships Dependent on That Entity." You can also create relationships by dragging connecting lines between entities. The created entities and relationships will then be displayed in... Figure 3 The left side is displayed. When creating a relationship or entity, or when selecting a relationship or entity, you can also... Figure 3 The right-hand side allows you to add attributes to relationships or entities. For example, you can add attributes such as company name, establishment date, registered capital, and whether it is a joint-stock company to the entity "Company". The operations mentioned above, such as right-clicking and selecting menu items from the right-click menu, belong to the model editing operations mentioned in step S110.

[0039] The model editing interface can be a webpage or an application interface, as will all the interfaces mentioned below, without further explanation.

[0040] Figure 2 The operation performed by the ontology modeling tool is shown, the contents of which have been described in step S110.

[0041] Step S120: Display the extraction rule configuration interface and respond to the extraction rule configuration operation performed on the extraction rule configuration interface to generate extraction rules.

[0042] After the ontology model is generated, users can then access the extraction rule configuration interface provided by the rule configuration component and perform extraction rule configuration operations on the extraction rule configuration interface to configure the extraction rules used to generate the knowledge graph.

[0043] The extraction rule can be defined as: a mapping rule used to transform the raw data in the data source into the target data in the knowledge graph.

[0044] The data source here refers to the data origin of the knowledge graph. For example, the data source can be a database, a business platform, a messaging system, etc. For a knowledge graph to be built, there can be one or more corresponding data sources, and the data in the data sources is called raw data. Optionally, the extraction rules can use the metadata of the raw data to describe the raw data. For example, the metadata of the raw data can include one or more pieces of information such as what fields are in the raw data, the order of these fields, and the hierarchical relationship. The metadata of the raw data can be in formats such as JSON and XML.

[0045] Target data refers to the specific data contained in the knowledge graph, or the attribute values ​​of elements (entities or relations) in the ontology model. For example, if the entity "company" contains the attribute "company name," then specific company names, such as "Company A" and "Company B," would be two pieces of target data. Optionally, the extraction rules can use the metadata of the target data to describe it. The metadata of the target data can include the attributes of elements in the ontology model. For example, for the entity "company," the company name and establishment date would be two pieces of metadata for the target data.

[0046] A mapping rule expresses a mapping relationship from raw data to target data. This mapping relationship can be a function, whose input includes the raw data and whose output includes the target data. The function can also perform certain processing on the raw data to obtain the target data. If the raw data and target data are described using their respective metadata, then the mapping rule can also be considered to express a mapping relationship from the metadata of the raw data to the metadata of the target data.

[0047] As explained above, when data is described using metadata, extraction rules can be broken down into three basic components: the metadata of the original data, the metadata of the target data, and the mapping relationship between the two. Users can configure extraction rules based on these three components. These components can be obtained by the user themselves or presented on the extraction rule configuration interface (all three can be presented, or only one or two can be presented), allowing the user to choose and thus improving the efficiency of extraction rule configuration.

[0048] The layout of the extraction rule configuration interface is not limited. The extraction rule configuration operation can be a visual operation on the extraction rule configuration interface for configuring extraction rules, including but not limited to one or more operations such as clicking, long pressing, dragging, swiping, and input. These operations can be performed by one or more input devices such as a mouse, keyboard, or touch screen.

[0049] For example, in one alternative, the extraction rule configuration interface includes: a first control for selecting metadata of the original data in the extraction rule, a second control for selecting metadata of the target data in the extraction rule, and a third control for selecting the mapping relationship in the extraction rule. That is, a control is set for each of the three basic contents that constitute the extraction rule mentioned above. Here, the control can refer to functional units on the interface such as input boxes and drop-down lists.

[0050] Figure 4 This is a schematic diagram of an extraction rule configuration interface provided in an embodiment of this application. (Refer to...) Figure 4 The "Create New Entity" on the left indicates that a new instance of an entity will be created (the concept of an instance will be explained later). The "Entity Type" control on the right is a drop-down list. The list contains all the entities included in the ontology model. When the user selects an entity, such as "IP Entity" here, it indicates that the extraction rules for that entity will be configured.

[0051] In the "Attribute Mapping" section below, each line represents an extraction rule configured by the user: The dropdown list "Behavior Log Attributes" can be considered the first control mentioned above, used to select metadata about the raw data from the data source, such as the "Collection IP" and "Collection Time" fields in the diagram. For example, when the extraction rule configuration interface is generated, the metadata of the raw data can be loaded into the dropdown list of the first control.

[0052] Notice, Figure 4 The system assumes that the data source is already configured, so the interface does not include a control for configuring the data source. In practice, if users are required to select the data source for building the knowledge graph, a control for configuring the data source can be added to the extraction rule configuration interface or other configuration interfaces (such as the extraction process configuration interface mentioned later).

[0053] The "Attributes" dropdown list can be considered the second control mentioned above, used to select metadata of target data in the knowledge graph, such as the "IP" and "Time" attributes in the diagram. These two attributes are attributes of the "IP entity" in the ontology model. For example, when the extraction rule configuration interface is generated, the metadata of the target data can be loaded into the dropdown list of the second control.

[0054] The input box "Extraction Rules" can be considered the third control mentioned above, used to select the mapping relationships in the extraction rules (click the lower right corner of the third control and select it in the pop-up window). These mapping relationships are... Figure 4 The middle part contains some pre-defined functions, such as trim and getTime. These functions can be user-defined or third-party. Taking trim(collect_ip) in the figure as an example, it means removing the whitespace before and after the collect_ip field in the original data (i.e., the "collect ip" field configured in the first control) to obtain the corresponding attribute in the "IP entity" (i.e., the "ip" attribute configured in the second control).

[0055] Combination Figure 4 As the example shows, users only need to perform simple selection operations on the first, second, and third controls to complete the configuration of extraction rules. The technical threshold is low and the configuration efficiency is high.

[0056] Let me explain briefly below. Figure 4 In this context, an entity (or relation) can have multiple instances. Each instance represents a set of extraction rules that form that entity (or relation). For example, for an "IP entity," suppose we can extract one "IP entity" from the raw data in two different data sources. The extraction rules forming these two "IP entities" are different; that is, two sets of extraction rules can be configured for the "IP entity," corresponding to two instances of the "IP entity." To distinguish between the different instances, they can be named, such as... Figure 4 As shown. However, for the sake of simplicity, the concepts of entity (or relation) and instance will no longer be deliberately distinguished in the following text.

[0057] The rule configuration component responds to the user's extraction rule configuration operations and generates extraction rules based on these operations. The generated extraction rules can be saved as a file or directly in the memory of the electronic device.

[0058] Figure 5 This is a schematic diagram of an extraction rule model provided in an embodiment of this application. Optionally, the rule configuration component can generate, based on the extraction rules configured by the user on the interface, such as... Figure 5The extraction rule model shown is used as the basis for subsequent steps in executing extraction rules. (Refer to...) Figure 5 The extraction rule model includes four types of objects: Rule, Node, Edge, and Property, with the following specific meanings: Rule is the core object of the extracted rule model. The members of Rule include a combination of multiple Nodes and Edges.

[0059] A Node is a node in the extracted rule model (corresponding to an entity in the ontology model). The members of a Node include its own attribute information (Property) and entity type (type).

[0060] Edge refers to the edge extracted from the rule model (corresponding to the relationship in the ontology model). Edge contains the nodes it connects to (source, target), its own attribute information (Property), and the relationship type (type).

[0061] Property refers to the attribute information of the extraction rule model, which includes the meta-information of the target data (dstKey, corresponding to the attribute of the element in the ontology model) and the expression of the extraction rule (such as trim(collect_ip) mentioned earlier).

[0062] Among them, Rule, Node, and Edge mainly describe the structure of the ontology model, while Property mainly describes how the attribute values ​​of each element in the ontology model are obtained. The extraction rule model integrates the extraction rules configured by the user.

[0063] Figure 2 The operations performed by the rule configuration component are shown, the main content of which has been described in step S120. The following mainly explains some points not previously mentioned. First, Figure 2 In addition to configuring extraction rules, the extraction process is also configured, but configuring the extraction process is an optional step, which will be explained later. Secondly, in Figure 2 In this process, the metadata and ontology model of the raw data from the data source must be input into the rule configuration component (the ontology model is input via a dotted line marked json_schema). This information is then displayed in the extraction rule configuration interface for users to select when configuring extraction rules (see the descriptions of the first and second controls). Finally, Figure 2 The paper also illustrates the operation of physicalizing the ontology model to obtain the graph database schema, which will be explained later.

[0064] Optionally, the rule configuration component can also provide a project configuration interface, allowing users to create projects that configure extraction rules for knowledge graphs. After creating a project on this interface, users can then jump to the extraction rule configuration interface to configure the specific extraction rules for that project.

[0065] Figure 6 This is a schematic diagram of a project configuration interface provided in an embodiment of this application. (Refer to...) Figure 6 On this interface, the user configures two projects: "Temporary Project" and "Test". The "Ontology Name" field contains the name of the ontology model generated in step S110 (e.g., "Test"). This associates the project with the ontology model. The user can access the extraction rule configuration interface for that project by selecting it on the project configuration interface.

[0066] It should be understood that in some implementations, knowledge graph building tools can also automatically create projects based on ontology models, without requiring users to configure projects themselves.

[0067] Step S130: Transform the raw data in the data source into target data in the knowledge graph by executing extraction rules.

[0068] Step S140: Construct a knowledge graph based on the target data and the ontology model.

[0069] Steps S130-S140 are described together. These two steps can be executed by the extraction engine. Executing the extraction rules refers to the process by which the extraction engine obtains raw data from the data source and maps it according to the extraction rules to obtain the target data. Optionally, the extraction engine can obtain raw data from the data source in batches or as a data stream. If the extraction rules are not loaded into memory before execution, they can be loaded into memory first. Figure 2 As shown.

[0070] For example, the extraction rule model obtained in step S120 can be encapsulated into a Flink (a distributed streaming processing framework) task, and the model can be parsed and executed using an extraction engine developed based on Flink. Since Flink can provide high-performance distributed streaming computing capabilities, it can ensure the efficient execution of data extraction.

[0071] In some implementations, the extraction engine automatically executes the configured extraction rules once the user has set them up. In other implementations, the user needs to publish the configured extraction rules; the extraction engine will only execute these rules upon receiving the user's publish request. The publish request can be understood as the user's confirmation of the configured extraction rules. Users can perform the publish request through the extraction rule configuration interface or other interfaces (such as the extraction process configuration interface mentioned later). Specifically, this could be done by clicking a button on the interface to execute the publish request.

[0072] The target data obtained in step S130 is not yet a knowledge graph. As mentioned earlier, the target data is only the attribute values ​​of elements in the ontology model. Therefore, it is necessary to combine it with the graph structure provided by the ontology model to finally construct the required knowledge graph, which is the content of step S140.

[0073] For example, in one implementation, the ontology model can be first converted into a data schema in a graph database, and then the target data can be stored in the graph database according to the data schema to obtain a knowledge graph.

[0074] Graph databases are a type of database that stores data in a graph structure. Since knowledge graphs are also graph structures, building knowledge graphs based on graph databases facilitates their storage, maintenance, and display. Examples of graph databases include Nebula and Neo4j.

[0075] The data schema in a graph database, also known as the graph database schema, can be understood as the representation of an ontology model within the graph database. Alternatively, it can be understood as the graph structure in which target data in a knowledge graph is organized when stored in the graph database. Analogously, in relational databases, the schema is the structure of a data table. Before writing the extracted target data into the graph database, a graph database schema can be created first (similar to creating a data table in a relational database). Then, the target data is written according to the graph database schema, ultimately resulting in the knowledge graph to be constructed.

[0076] exist Figure 2 In this context, the step of converting an ontology model into a graph database schema is called physicalization. This step can be performed by ontology modeling tools, rule configuration components, or extraction engines, while the step of writing the target data into the graph data can be performed by the extraction engine.

[0077] After constructing a knowledge graph in a graph database, the database's functionalities can be used to display and query the knowledge graph. For example, in... Figure 7In this context, the Nebular Explorer interface of the Nebula database can be used to display the knowledge graph.

[0078] In some implementations, the ontology model can be directly converted into a graph database schema; in others, the ontology model can first be converted into an intermediate data schema, and then the intermediate data schema can be converted into a graph database schema. The intermediate data schema is database-independent, thus facilitating adaptation to different graph databases, meaning it supports easily building knowledge graphs in different graph databases according to user needs. For example, the intermediate data schema can be a data schema described using JSON, XML, etc. Figure 2 The middle part is json_schema. Figure 2 When the rule configuration component extracts information from the ontology model (i.e., the meta-information of the target data) in the rule configuration interface, it can display it based on json_schema. Of course, in the alternative solution, it can also display it based on the graph data schema.

[0079] It should be understood that a well-constructed knowledge graph does not necessarily have to be stored in a graph database. It can also be stored in other relational or non-relational databases, or it can be stored in a file instead of a database, and so on.

[0080] Brief summary Figure 1 The proposed method constructs a knowledge graph by displaying a model editing interface and an extraction rule configuration interface, and responding to model editing and extraction rule configuration operations performed on the interface. This visualizes the knowledge graph construction process, allowing ordinary users to independently construct the required knowledge graph by performing only some interface operations, without relying on developers to write related code (e.g., code describing how to extract data) for each construction. Therefore, it significantly reduces the technical threshold for constructing knowledge graphs and improves the efficiency of knowledge graph construction.

[0081] Furthermore, the various interfaces and components required to implement this method can be integrated into a single tool, such as the knowledge graph construction tool mentioned above. This allows users to complete the entire knowledge graph construction process using just one tool, eliminating the need for repeated cross-team communication as in existing technologies. This improves the efficiency of knowledge graph construction and reduces communication costs.

[0082] Building upon the above implementation, the knowledge graph construction method may optionally include: displaying a preprocessing rule configuration interface and responding to preprocessing rule configuration operations performed on the preprocessing rule configuration interface to generate preprocessing rules. This step can be performed by the rule configuration component.

[0083] After the ontology model is generated, users can access the preprocessing rule configuration interface provided by the rule configuration component. On this interface, users can configure preprocessing rules to preprocess the raw data from the data source. The specific preprocessing operation is not limited; for example, it could be filtering out parts of the raw data, transforming the format of the raw data, etc.

[0084] The preprocessing rule configuration interface can be displayed before the extraction rule configuration interface, meaning users can configure the preprocessing rules first, and then configure the extraction rules. The layout of the preprocessing rule configuration interface is not limited. Preprocessing rule configuration operations can be visual operations on the interface used to configure preprocessing rules, including but not limited to one or more operations such as clicking, long-pressing, dragging, swiping, and input. These operations can be performed using one or more input devices such as a mouse, keyboard, or touchscreen.

[0085] Figure 8 This is a schematic diagram of a preprocessing rule configuration interface provided in an embodiment of this application. (Refer to...) Figure 8 The dropdown list "Select Business Behavior Data" is used to select the data source, that is, which data source's raw data will be preprocessed according to the preprocessing rules configured below (optional, the data source is configured here). Figure 4 (This eliminates the need for further configuration; here, "Data Source Logs" was selected as the data source.) The "Filter by Business Behavior Attribute" section details the preprocessing rules. Figure 8 A filtering rule has been configured, the content of which can be described in code as follows: project_id.equals("111") || client_model_info.equalsIgnoreCase("f**a") || collect_time.equalsIgnoreCase("110") || (device_ip.equals("1.*.*.1")) In this context, the symbol || represents the logical OR operation, used to connect the four filtering conditions. `project_id` represents the "project ID" field in the original data; `equals` indicates "exact match"; `client_model_info` represents the "client model information" field in the original data; `equalsIgnoreCase` indicates "case-insensitive matching"; `collect_time` represents the "collection time (hours)" field in the original data; and `device_ip` represents the "device IP" field in the original data. Additionally, in... Figure 8 In the code, the last filter condition (corresponding to the "device IP" field) is implemented as a second-level directory, indicating that this condition is nested. Therefore, an extra layer of parentheses will be added outside of `device_ip.equals("1.*.*.1")`. This filter rule means that any data in the original data that satisfies one of the four filter conditions can be retained for extracting the target data, while the remaining data can be discarded.

[0086] With preprocessing rules configured, step S130 can be implemented as follows: First, by executing the preprocessing rules, the original data in the data source is preprocessed to obtain processed original data; then, by executing the extraction rules, the processed original data is transformed into target data in the knowledge graph. The data preprocessing step can also be performed by the extraction engine.

[0087] It supports configuring preprocessing rules, which can optimize the raw data to a certain extent, thereby improving the efficiency or effect of subsequent data extraction. Furthermore, the data preprocessing is implemented in a visual way, so the technical threshold is not high and it is easy for ordinary users to use.

[0088] Based on the above implementation, optionally, the knowledge graph construction method may also include: displaying an extraction process configuration interface and responding to extraction process configuration operations performed on the extraction process configuration interface to generate at least one extraction process.

[0089] After the ontology model is generated, users can then enter the extraction process configuration interface provided by the rule configuration component and perform extraction process configuration operations on the extraction process configuration interface to configure the extraction process used to generate the knowledge graph.

[0090] Each extraction process is a transformation process from raw data in a corresponding data source to target data in the knowledge graph that corresponds to that data source. For example, if the target data in a knowledge graph needs to be extracted from raw data in two data sources, then two extraction processes can be configured for the construction of the knowledge graph, each corresponding to one of the two data sources.

[0091] Furthermore, each extraction process has its own extraction rule configuration interface and contains its own extraction rules (and may also contain preprocessing rules). The extraction rules contained in an extraction process can be defined as: the mapping rules used to transform the raw data in the corresponding data source of the extraction process into the target data in the knowledge graph corresponding to that data source. The extraction rules have been explained above and will not be repeated here.

[0092] Continuing the example above, each extraction process has its own extraction rule configuration interface, used to configure the extraction rules for each process. Since the raw data from different data sources may have different structures, the extraction rules used to extract data from different data sources may have different implementations. Therefore, different forms of extraction rules can be distinguished, and different extraction processes can be configured to perform extraction separately. From this perspective, an extraction process configured for a knowledge graph can also be viewed as a collection of extraction rules configured for a single data source.

[0093] For example, if the raw data provided by data source X is JSON data and the raw data provided by data source Y is XML data, meaning their data structures are different, the extraction rules for extracting from the raw data of data source X will be different from those for extracting from the raw data of data source Y. Therefore, two corresponding extraction processes can be configured, let's call them process x and process y. In process x, a series of extraction rules for data source X will be configured, and in process y, a series of extraction rules for data source Y will be configured.

[0094] The extraction process configuration interface can be displayed before the extraction rule configuration interface. This means users can configure the extraction process first, and then configure the extraction rules under that process. Optionally, if a project configuration interface is also provided, the extraction process configuration interface can be displayed after the project configuration interface and before the extraction rule configuration interface. This means users can configure the extraction project corresponding to the knowledge graph first, then configure the extraction process under that project, and then configure the extraction rules under that process. Alternatively, if a preprocessing rule configuration interface is also provided, the extraction process configuration interface can be displayed after the project configuration interface and before the preprocessing rule configuration interface. The extraction rule configuration interface can be displayed after the preprocessing rule configuration interface. This means users can configure the extraction project corresponding to the knowledge graph first, then configure the extraction process under that project, then configure the preprocessing rules under that process, and then configure the extraction rules under that process.

[0095] The layout of the extraction process configuration interface is not limited. The extraction process configuration operation can be a visual operation on the extraction process configuration interface for configuring the extraction process, including but not limited to one or more operations such as clicking, long pressing, dragging, swiping, and input. These operations can be performed by one or more input devices such as a mouse, keyboard, or touch screen.

[0096] Figure 9 This is a schematic diagram of an extraction process configuration interface provided in an embodiment of this application. (Refer to...) Figure 9 The left side is Figure 6The "Test" project is configured in the settings. Users can access it by selecting this project in the project configuration interface. Figure 9 The extraction process configuration interface. Figure 9 The interface currently has two extraction processes configured, targeting "Business 1" and "Business 2" data sources respectively. Clicking "More" on the right side of the extraction process will take you to the extraction rule configuration interface (or preprocessing rule configuration interface) for that process, allowing you to further configure the extraction rules (or preprocessing rules) for that process. Note that while the core information to be included in the extraction process is the data source and the ontology model (or project) targeted by the extraction process, other information can also be included, such as... Figure 9 As shown.

[0097] With an extraction process configured, step S130 can be implemented as follows: by executing each extraction process, the raw data in the corresponding data source of each extraction process is transformed into target data corresponding to that data source. The extraction process can be executed by an extraction engine. When each extraction process is executed, the extraction rules contained in that process are executed (if there are preprocessing rules, the preprocessing rules are executed first, then the extraction rules). If the extraction process is not loaded into memory before execution, it can be loaded into memory first before execution, such as... Figure 2 As shown.

[0098] In some implementations, once the user configures the extraction process (including its subordinate extraction rules), the extraction engine will automatically execute the extraction process. In other implementations, after the user configures the extraction process (including its subordinate extraction rules), it also needs to be published. The extraction engine will only execute the extraction process after it detects the user's publication operation. (See reference...) Figure 9 Users can click the publish button in the upper right corner to perform the publishing operation.

[0099] In one implementation, data extraction tasks can be constructed based on extraction processes (e.g., one data extraction task can be constructed for each extraction process), and the extraction processes are executed by executing these data extraction tasks. Note that since extraction rules belong to the extraction processes, the data extraction tasks also contain information about the extraction rules belonging to the corresponding extraction process. For example, the data extraction task here could be a jobgraph in Flink, and the task execution engine could be the extraction engine of a knowledge graph construction tool. As mentioned earlier, this engine can be developed based on Flink, thus enabling it to execute jobgraphs.

[0100] After configuring the extraction process or rules, users are likely to update them, such as editing or adding new elements based on the actual extraction results. These two scenarios are described below: (1) Extraction process update When users need to update the extraction process, they can perform update operations on the extraction process configuration interface, such as changing the data source of an existing extraction process or creating a new extraction process. The extraction engine can respond to these update operations and generate the updated extraction process. For example, the extraction engine can listen for update operations, and when an operation is detected (or, if the extraction process requires user publishing, it can listen for user publishing operations), it will generate the updated extraction process.

[0101] Then, the extraction engine can build new data extraction tasks based on the updated extraction process and execute the new data extraction tasks, so that the updated extraction process can be applied.

[0102] (2) Extraction rule update Note that although the extraction rules can be considered part of the extraction process, the update of the extraction rules here is not considered an update of the extraction process. Simply put, the update in (1) occurs on the extraction process configuration interface, and the update in (2) occurs on the extraction rule configuration interface.

[0103] When users need to update extraction rules under an existing extraction process, they can perform extraction rule update operations on the extraction rule configuration interface. These operations include modifying existing extraction rules (such as changing the metadata of the original data, the metadata of the target data, mapping relationships, etc.) and creating new extraction rules. The extraction engine can respond to extraction rule update operations and generate updated extraction rules. For example, the extraction engine can listen for extraction rule update operations, and when an operation is detected (or, if the extraction rule requires user publishing, it can listen for user publishing operations), it generates updated extraction rules.

[0104] Then, the extraction engine can load the updated extraction rules into the data extraction task corresponding to its extraction process. This way, when the data extraction task continues to execute, the updated extraction rules will take effect. The data extraction task can be already loaded into memory. If the updated extraction rule modifies an existing rule, then loading here can mean replacing the existing corresponding rule under the data extraction task; if the updated extraction rule is a new rule, then loading here can mean adding the updated extraction rule to the data extraction task.

[0105] In the configuration update methods of (1) and (2), since the extraction process or extraction rules can be dynamically changed, users can make timely adjustments when they find that the extraction process or extraction rules they designed are unreasonable, thereby quickly and completely verifying the knowledge graph, instead of waiting until the knowledge graph is built before verification and iteration. This allows for timely discovery of problems in the knowledge graph design, improvement of construction quality, and increased construction efficiency.

[0106] Furthermore, in (2), since only some extraction rules are changed, the updated extraction rules only need to be loaded into the data extraction task corresponding to the extraction process to which they belong. There is no need to rebuild the data extraction task (rebuilding the data extraction task may be time-consuming), thus enabling the action of changing extraction rules to be lightweight and the adjustment to be real-time.

[0107] Figure 10 A block diagram of the knowledge graph construction apparatus 200 provided in an embodiment of this application. (Refer to...) Figure 10 The knowledge graph construction device 200 includes: The ontology model editing module 210 is used to display the model editing interface and respond to the model editing operation performed on the model editing interface to generate the ontology model of the knowledge graph. Extraction rule configuration module 220 is used to display extraction rule configuration interface and respond to extraction rule configuration operation performed on the extraction rule configuration interface to generate extraction rules; wherein, the extraction rule is a mapping rule used to transform the original data in the data source into target data in the knowledge graph; Extraction rule execution module 230 is used to transform the original data into the target data by executing the extraction rules; The knowledge graph forming module 240 is used to construct the knowledge graph based on the target data and the ontology model.

[0108] In one implementation of the knowledge graph construction device 200, the device further includes an extraction process configuration module for displaying an extraction process configuration interface and responding to extraction process configuration operations executed on the extraction process configuration interface to generate at least one extraction process; wherein, each extraction process is a transformation process from raw data in a corresponding data source to target data in the knowledge graph corresponding to that data source, and each extraction process has its own extraction rule configuration interface and contains its own extraction rules; the extraction rule execution module 230 transforms the raw data into the target data by executing the extraction rules, including: transforming the raw data in the corresponding data source of each extraction process into target data corresponding to that data source by executing each extraction process; wherein, when each extraction process is executed, the extraction rules contained in the extraction process are executed.

[0109] In one implementation of the map construction device 200, the extraction rule execution module 230 executes the extraction process, including: constructing a data extraction task based on the extraction process and executing the data extraction task; the extraction rule execution module 230 is further configured to: respond to an extraction process update operation executed on the extraction process configuration interface, generate an updated extraction process; construct a new data extraction task based on the updated extraction process and execute the new data extraction task.

[0110] In one implementation of the map construction device 200, the extraction rule execution module 230 executes the extraction process, including: constructing a data extraction task based on the extraction process and executing the data extraction task; the extraction rule execution module 230 is further configured to: respond to an extraction rule update operation executed on the extraction rule configuration interface, generate an updated extraction rule; and load the updated extraction rule into the data extraction task corresponding to its extraction process.

[0111] In one implementation of the map construction device 200, the device further includes a preprocessing rule configuration module for displaying a preprocessing rule configuration interface and generating preprocessing rules in response to preprocessing rule configuration operations performed on the preprocessing rule configuration interface; the extraction rule execution module 230 converts the raw data into the target data by executing the extraction rules, including: preprocessing the raw data by executing the preprocessing rules to obtain processed raw data; and converting the processed raw data into the target data by executing the extraction rules.

[0112] In one implementation of the map construction device 200, the extraction rules include metadata of the original data, metadata of the target data, and a mapping relationship between the metadata of the original data and the metadata of the target data. The extraction rule configuration interface includes: a first control for selecting metadata of the original data in the extraction rules, a second control for selecting metadata of the target data in the extraction rules, and a third control for selecting the mapping relationship in the extraction rules.

[0113] It should be noted that the metadata of the target data in the above embodiments may include the attribute information of elements in the ontology model.

[0114] In one implementation of the knowledge graph construction device 200, the knowledge graph forming module 240 constructs the knowledge graph based on the target data and the ontology model, including: converting the ontology model into a data pattern in the graph database; and storing the target data in the graph database according to the data pattern to obtain the knowledge graph.

[0115] In one implementation of the graph construction device 200, the knowledge graph forming module 240 converts the ontology model into a data pattern in the graph database, including: converting the ontology model into an intermediate data pattern independent of the database; and converting the intermediate data pattern into a data pattern in the graph database.

[0116] The knowledge graph construction device 200 provided in this application embodiment has been described in the foregoing method embodiment in terms of its implementation principle and technical effects. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the method embodiment.

[0117] Figure 11 This is a structural diagram of the electronic device 300 provided in an embodiment of this application. (Refer to...) Figure 11 The electronic device 300 includes a processor 310, a memory 320, and a communication interface 330. These components are interconnected and communicate with each other via a communication bus 340 and / or other forms of connection mechanism (not shown).

[0118] The processor 310 includes one or more (only one is shown in the figure), which can be an integrated circuit chip with signal processing capabilities. The processor 310 can be a general-purpose processor, including a Central Processing Unit (CPU), a Microcontroller Unit (MCU), a Network Processor (NP), or other conventional processors; it can also be a special-purpose processor, including a Neural-network Processing Unit (NPU), a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. Furthermore, when there are multiple processors 310, some can be general-purpose processors and others can be special-purpose processors.

[0119] The memory 320 includes one or more (only one is shown in the figure), which may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The processor 310 and other possible components may access the memory 320 to read and / or write data therein.

[0120] Specifically, one or more computer program instructions may be stored in the memory 320, and the processor 310 may read and run these computer program instructions to implement the knowledge graph construction method provided in the embodiments of this application.

[0121] Communication interface 330 includes one or more (only one is shown in the figure) that can be used to communicate directly or indirectly with other devices for data exchange. Communication interface 330 may include interfaces for wired and / or wireless communication.

[0122] Understandable. Figure 11 The structure shown is for illustrative purposes only; the electronic device 300 may also include components that are more advanced than those shown. Figure 11 The more or fewer components shown, or having the same Figure 11 Different structures are shown. For example, if electronic device 300 does not need to communicate with other devices, communication interface 330 can be omitted, and so on.

[0123] Figure 11 The components shown can be implemented using hardware, software, or a combination thereof. Electronic device 300 may be a physical device, such as a server, PC, laptop, tablet, or mobile phone, or a virtual device, such as a virtual machine or virtualization container. Furthermore, electronic device 300 is not limited to a single device; it can also be a combination of multiple devices or a cluster of numerous devices.

[0124] This application also provides a computer program product, which includes computer program instructions that are read and executed by a processor of an electronic device to perform the knowledge graph construction method provided in this application.

[0125] This application also provides a computer-readable storage medium storing computer program instructions. These instructions are read and executed by a processor of an electronic device to perform the knowledge graph construction method provided in this application. For example, the computer-readable storage medium can be implemented as follows: Figure 11 The memory 320 in the electronic device 300.

[0126] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for constructing a knowledge graph, characterized in that, include: Display the model editing interface and respond to the model editing operations performed on the model editing interface to generate the ontology model of the knowledge graph; Display the extraction rule configuration interface and respond to the extraction rule configuration operation performed on the extraction rule configuration interface to generate extraction rules; wherein, the extraction rule is a mapping rule used to transform the raw data in the data source into the target data in the knowledge graph; By executing the extraction rules, the raw data is transformed into the target data. The extraction rules are executed based on an extraction rule model, which is encapsulated as a Flink task. The extraction rule model includes four types of objects: Rule, Node, Edge, and Property. Rule is the core object of the extraction rule model. The members of Rule include a combination of multiple Nodes and Edges. Node is a node of the extraction rule model, including its own attribute information and entity type, corresponding to the entity in the ontology model. Edge is an edge of the extraction rule model, including the nodes it connects to, its own attribute information, and relation type, corresponding to the relation in the ontology model. Property refers to the attribute information of the extraction rule model, including the meta-information of the target data and the expression of the extraction rules. The knowledge graph is constructed based on the target data and the ontology model; The method further includes: Display the extraction process configuration interface and respond to the extraction process configuration operation executed on the extraction process configuration interface to generate at least one extraction process; wherein, each extraction process is a transformation process from raw data in a corresponding data source to target data in the knowledge graph corresponding to the data source, and each extraction process has its own extraction rule configuration interface and contains its own extraction rules. The process of transforming the raw data into the target data by executing the extraction rules includes: By executing each extraction process, the raw data in the corresponding data source of each extraction process is transformed into target data corresponding to that data source; wherein, when each extraction process is executed, the extraction rules contained in that extraction process are executed. The extraction process includes: A data extraction task is constructed based on the extraction process, and the data extraction task is executed. The method further includes: In response to the extraction rule update operation performed on the extraction rule configuration interface, the updated extraction rule is generated. The updated extraction rules are loaded into the data extraction task corresponding to their respective extraction process. If the updated extraction rules are modifications to the original rules, then loading means replacing the original corresponding rules under the data extraction task. If the updated extraction rules are new rules, then loading means adding the updated extraction rules to the data extraction task. The extraction process further includes: A data extraction task is constructed based on the extraction process, and the data extraction task is executed. The method further includes: In response to the extraction process update operation performed on the extraction process configuration interface, an updated extraction process is generated. A new data extraction task is constructed based on the updated extraction process, and the new data extraction task is executed.

2. The knowledge graph construction method according to claim 1, characterized in that, The method further includes: Display the preprocessing rule configuration interface and respond to the preprocessing rule configuration operation performed on the preprocessing rule configuration interface to generate preprocessing rules; The process of transforming the raw data into the target data by executing the extraction rules includes: By executing the preprocessing rules, the original data is preprocessed to obtain the processed original data; By executing the extraction rules, the processed raw data is transformed into the target data.

3. The knowledge graph construction method according to claim 1, characterized in that, The extraction rules include metadata of the original data, metadata of the target data, and a mapping relationship between the metadata of the original data and the metadata of the target data. The extraction rule configuration interface includes: A first control for selecting metadata of the original data in the extraction rule, a second control for selecting metadata of the target data in the extraction rule, and a third control for selecting the mapping relationship in the extraction rule.

4. The knowledge graph construction method according to any one of claims 1-3, characterized in that, The step of constructing the knowledge graph based on the target data and the ontology model includes: Convert the ontology model into a data schema in a graph database; The target data is stored in the graph database according to the data pattern to obtain the knowledge graph.

5. The knowledge graph construction method according to claim 4, characterized in that, The step of converting the ontology model into a data schema in a graph database includes: The ontology model is converted into an intermediate data schema that is independent of the database; The intermediate data pattern is converted into a data pattern in the graph database.

6. A knowledge graph construction device, characterized in that, include: The ontology model editing module is used to display the model editing interface and respond to the model editing operations performed on the model editing interface to generate the ontology model of the knowledge graph. An extraction rule configuration module is used to display an extraction rule configuration interface and respond to extraction rule configuration operations performed on the extraction rule configuration interface to generate extraction rules; wherein, the extraction rule is a mapping rule used to transform raw data in the data source into target data in the knowledge graph; An extraction rule execution module is used to transform the raw data into the target data by executing the extraction rules. The extraction rules are executed based on an extraction rule model, which is encapsulated as a Flink task. The extraction rule model includes four types of objects: Rule, Node, Edge, and Property. Rule is the core object of the extraction rule model, and its members include combinations of multiple Nodes and Edges. Node refers to a node in the extraction rule model, including its own attribute information and entity type, corresponding to an entity in the ontology model. Edge refers to an edge in the extraction rule model, including the nodes it connects to, its own attribute information, and relation type, corresponding to a relation in the ontology model. Property refers to the attribute information of the extraction rule model, including the metadata of the target data and the expression of the extraction rule. A knowledge graph forming module is used to construct the knowledge graph based on the target data and the ontology model; The extraction process configuration module is used to display the extraction process configuration interface and respond to the extraction process configuration operation executed on the extraction process configuration interface to generate at least one extraction process; wherein, each extraction process is a transformation process from raw data in a corresponding data source to target data in the knowledge graph corresponding to the data source, and each extraction process has its own extraction rule configuration interface and contains its own extraction rules. The extraction rule execution module is used to transform the original data in the corresponding data source of each extraction process into target data corresponding to that data source by executing each extraction process; wherein, when each extraction process is executed, the extraction rules contained in that extraction process are executed. The extraction rule execution module is used to construct a data extraction task based on the extraction process and execute the data extraction task; respond to the extraction rule update operation executed on the extraction rule configuration interface, generate the updated extraction rule; and load the updated extraction rule into the data extraction task corresponding to its extraction process. If the updated extraction rule is a modification of the original rule, then loading means replacing the original corresponding rule under the data extraction task; if the updated extraction rule is a new rule, then loading means adding the updated extraction rule to the data extraction task. The extraction rule execution module is used to construct a data extraction task based on the extraction process and execute the data extraction task; respond to the extraction process update operation executed on the extraction process configuration interface and generate an updated extraction process; construct a new data extraction task based on the updated extraction process and execute the new data extraction task.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when read and executed by a processor, perform the method as described in any one of claims 1-5.

8. An electronic device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer program instructions, which are read and executed by the processor to perform the method of any one of claims 1-5.

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