CN113157947A - Knowledge graph construction method, tool, device and server - Google Patents

Abstract

The specification provides a method, a tool, a device and a server for constructing a knowledge graph. Based on the construction method of the knowledge graph, the data structure type of target source data to be processed can be determined firstly; then, according to a preset construction rule and the data structure type of the target source data, constructing and obtaining a target knowledge extraction unit matched with the target source data; further, the target knowledge extraction unit can be called to specifically process target source data to obtain an entity relationship file which contains a plurality of ternary data sets and meets the requirements; and then, according to the entity relation file, a target knowledge graph associated with the target source data is constructed. Therefore, the operation of the user side can be effectively simplified, the construction difficulty of the knowledge graph is reduced, and the knowledge graph which meets the diversified service requirements and has a good effect can be efficiently and accurately constructed by the user.

Description

Knowledge graph construction method, tool, device and server

technical field

This specification belongs to the field of artificial intelligence technology, and in particular relates to methods, tools, devices and servers for constructing knowledge graphs.

Background technique

Knowledge graph is an important branch of artificial intelligence technology, which plays an important role in machine learning and cognition.

However, the existing knowledge graph construction method has high technical threshold and difficulty for users who need to build a knowledge graph. In addition, based on the existing knowledge graph construction methods, when constructing a knowledge graph, there are often problems such as complex and cumbersome operations, low construction efficiency, and inability to meet the diverse business needs of users.

For the above problems, no effective solution has been proposed yet.

SUMMARY OF THE INVENTION

This specification provides a method, tool, device and server for building a knowledge graph to simplify user-side operations, reduce the difficulty of building a knowledge graph, and enable users to efficiently and accurately build a knowledge graph that meets diverse business needs with better results knowledge graph.

The embodiments of this specification provide a method for constructing a knowledge graph, including:

Get the target source data;

Determine the data structure type of the target source data;

According to the preset construction rule and the data structure type of the target source data, construct a target knowledge extraction unit matching the target source data;

Invoke the target knowledge extraction unit to process the target source data to obtain an entity relationship file that meets the requirements; wherein, the entity relationship file contains a plurality of triple data groups; Two data objects connected by a relationship;

According to the entity relationship file, a target knowledge graph associated with the target source data is constructed.

In some embodiments, the data structure type of the target source data includes at least one of the following: structured data, unstructured data, and semi-structured data.

In some embodiments, constructing a target knowledge extraction unit matching the target source data according to a preset construction rule and the data structure type of the target source data, including:

According to a preset construction rule, a target source operator corresponding to the target source data is selected from a plurality of preset data source operators; wherein, the target source operator is used to access the target source data to the target knowledge extraction unit;

According to the data structure type of the target source data, a matching target data processing structure is determined; wherein, the target data processing structure is used to process the target source data to obtain a plurality of triplet data groups;

Determine and configure a target identification termination operator; wherein, the target identification termination operator is used to extract a required triplet data group from a plurality of triplet data groups output by the target data processing structure to obtain a corresponding entity relationship file ;

The target source operator, the target data processing structure and the target identification termination operator are combined to obtain a target knowledge extraction unit matching the target data source.

In some embodiments, according to the data structure type of the target source data, a matching target data processing structure is determined, including:

In the case that the data structure type of the target source data is determined to be structured data, an initial processing operator is selected from a plurality of preset data processing operators;

The initial processing operator is correspondingly configured to obtain a target processing operator; and the target processing operator is determined as a matching target data processing structure.

In some embodiments, the preset data processing operators include at least one of the following: SQL operators, HIVE operators, and SPARK operators.

In some embodiments, according to the data structure type of the target source data, a matching target data processing structure is determined, including:

In the case that the data structure type of the target source data is determined to be unstructured data or semi-structured data, the preset triplet extraction model is determined as a matching target data processing structure.

In some embodiments, after determining the data structure type of the target source data, the method further includes:

According to the data structure type of the target source data, a recommended knowledge extraction unit is selected from a plurality of preset knowledge extraction units;

presenting the recommended knowledge extraction unit to the user;

The recommended knowledge extraction unit selected by the user is determined as the target knowledge extraction unit.

In some embodiments, constructing a target knowledge graph associated with the target source data according to the entity relationship file, including:

Obtain a definition parameter file about the target knowledge graph; wherein, the definition parameter file includes: definition parameters of data objects and/or definition parameters of data relationships;

According to the entity relationship file and the definition parameter file, a target knowledge graph associated with the target data source is generated by performing data mapping.

In some embodiments, the definition parameter file further includes index definition parameters;

Correspondingly, in the process of constructing the target knowledge graph associated with the target source data according to the entity relationship file, the method further includes:

According to the index definition parameters, a target query index for the target knowledge graph is constructed by using the definition parameters of the data object and/or the definition parameters of the data relationship.

In some embodiments, after constructing the target knowledge graph associated with the target source data according to the entity relationship file, the method further includes:

Receive a target query statement; wherein, the target query statement carries at least the target identifier of the target knowledge graph;

According to the target identification, the graph database is retrieved to determine the target knowledge graph;

In response to the target query statement, a query operation is performed on the target knowledge graph to obtain a corresponding query result;

Feedback the query result.

In some embodiments, the target source data includes a flow record of the customer's transaction data; correspondingly, the query result includes a flow diagram of the target customer's transaction data.

The embodiments of this specification also provide a knowledge graph construction tool, which at least includes: a source data import interface, a first processing interface, and a second processing interface; wherein,

The source data import interface is used to support users to import target source data;

The first processing interface is used to support the user to set the definition parameters of the data objects and/or the definition parameters of the data relationships in the target knowledge graph, so as to generate a definition parameter file about the target knowledge graph;

The second processing interface is used to support the user to determine and combine matching target source operators, target data processing structures, and identification termination operators according to preset construction rules, so as to obtain target knowledge extraction matching the target source data. unit;

The construction tool of the knowledge graph is also used to call the target knowledge extraction unit to process the target source data to obtain the corresponding entity relationship file; and according to the entity relationship file and the definition parameter file, by performing data mapping, generate a The target knowledge graph associated with the target data source.

The embodiments of this specification also provide a knowledge graph construction device, including:

The acquisition module is used to acquire the target source data;

A determination module for determining the data structure type of the target source data;

a first building module, configured to build a target knowledge extraction unit matching the target source data according to a preset construction rule and the data structure type of the target source data;

a calling module, configured to call the target knowledge extraction unit to process the target source data, so as to obtain an entity relationship file that meets the requirements; wherein, the entity relationship file contains a plurality of ternary data groups; the ternary data groups At least two data objects connected by a data relationship are included;

The second building module is configured to build a target knowledge graph associated with the target source data according to the entity relationship file.

An embodiment of the present specification further provides a server, including a processor and a memory for storing instructions executable by the processor, where the processor executes the steps of the method for constructing the knowledge graph.

The embodiments of this specification also provide a computer storage medium, which stores computer instructions, and when the instructions are executed, implements the steps of the method for constructing the knowledge graph.

This specification provides a method, tool, device and server for constructing a knowledge graph. Based on the method for constructing a knowledge graph, the data structure type of the target source data to be processed can be determined first; The data structure type of the source data is constructed to obtain a target knowledge extraction unit that matches the target source data and is highly targeted; further, the above target knowledge extraction unit can be called to specifically process the target source data, and the target knowledge extraction unit can be called. The entity relationship file that meets the requirements of the metadata group; and then constructs and obtains the target knowledge graph associated with the target source data according to the above entity relationship file. This can effectively simplify user-side operations, reduce the difficulty of building knowledge graphs, and enable users to efficiently and accurately build knowledge graphs that meet diverse business needs and have better effects.

Description of drawings

In order to explain the embodiments of the present specification more clearly, the following briefly introduces the accompanying drawings that need to be used in the embodiments. The drawings in the following description are only some of the embodiments described in the present specification. In other words, on the premise of no creative labor, other drawings can also be obtained based on these drawings.

FIG. 1 is a schematic diagram of an embodiment of the structural composition of a system to which the method for constructing a knowledge graph provided by an embodiment of the present specification is applied;

2 is a schematic diagram of a knowledge graph construction tool provided by an embodiment of this specification;

3 is a schematic flowchart of a method for constructing a knowledge graph provided by an embodiment of this specification;

4 is a schematic diagram of the structural composition of a server provided by an embodiment of this specification;

5 is a schematic structural diagram of a construction device for a knowledge graph provided by an embodiment of this specification;

FIG. 6 is a schematic diagram of an embodiment of applying the method for constructing a knowledge graph provided by an embodiment of this specification in a specific scenario example.

Detailed ways

In order to make those skilled in the art better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described The embodiments are only some of the embodiments of the present specification, but not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

The embodiments of this specification provide a method for constructing a knowledge graph, and the method can be specifically applied to a system including a server and a terminal device. For details, please refer to Figure 1. Wherein, the server and the terminal device can be connected in a wired or wireless manner to perform specific data interaction.

In this embodiment, the server may specifically include a background server that is applied to the side of the network platform and can implement functions such as data transmission and data processing. Specifically, the server may be, for example, an electronic device with data computing, storage, and network interaction functions. Alternatively, the server may also be a software program running in the electronic device to provide support for data processing, storage and network interaction. In this embodiment, the number of servers included in the server is not specifically limited. The server may specifically be one server, or several servers, or a server cluster formed by several servers.

In this embodiment, the terminal device may specifically include a front-end electronic device that is applied to the user side and can implement functions such as data collection and data transmission. Specifically, the terminal device may be, for example, a desktop computer, a tablet computer, a notebook computer, a smart phone, and the like. Alternatively, the terminal device may also be a software application that can run in the above electronic device. For example, it may be an APP or the like running on a smartphone.

In this embodiment, the above-mentioned server may be specifically connected to a graph database of the network platform, and is used for maintaining and managing the graph database of the network platform. The graph database may specifically store multiple knowledge graphs. Specifically, the above-mentioned terminal device may also be deployed with a knowledge graph construction tool.

In this embodiment, the current user needs to process a batch of data (for example, the transaction data of XX Bank's customers in 2020), so as to construct a collection that meets business needs (for example, it is suitable for analyzing whether customers have illegal transaction risks) When the target knowledge graph is set, a start command for the knowledge graph construction tool can be initiated on the terminal device to start the knowledge graph construction tool.

Correspondingly, the terminal device starts and displays the operation interface of the knowledge graph construction tool to the user. For details, please refer to Figure 2. Then, the user can use the above-mentioned knowledge graph construction tool to process the corresponding target source data through the terminal device, so as to construct a target knowledge graph that meets business requirements.

The operation interface of the displayed knowledge graph construction tool at least includes structures such as an active data import interface, a first processing interface, and a second processing interface.

During the specific implementation, first, the user can use the source data import interface according to the relevant instructions on the operation interface, select and import the pending processing through various import methods such as local file upload, HDFS file import, database table import, third-party data access, etc. A batch of data is used as the target source data.

When importing the target source data, the user can also customize the import method. For example, you can choose to use the source data import interface to display the preview information of the data to be imported through the source data import interface during the process of importing the target source data. The preview information can specifically be a data volume parameter (for example, the number of rows, columns, total number, etc. of the data), or it can be a content parameter of the data (for example, the content keyword of the data, the preview of the first few rows of data in the data, the data name, etc.) Wait). In this way, when the user uses the source data import interface to import the target source data, the user can perform the import operation more accurately according to the preview information to avoid import errors.

Next, the user may use the first processing interface to set definition parameters for defining data objects and/or data relationships in the target knowledge graph to be constructed.

Specifically, the user can set the name, attribute, type, etc. of the data object (or entity object) in the first processing interface as the definition parameters of the data object (for example, the customer's name, the customer's account, the customer's company, etc.). The user can also set the name, attribute, type, etc. of the data relationship as the definition parameters of the data relationship in the first processing interface (for example, the attribution relationship between the data objects, the transfer relationship between the data objects, the debt between the data objects). relationship, etc.).

Correspondingly, the terminal device may receive the definition parameters of the data object and/or the definition parameters of the data relationship set by the user through the above-mentioned first processing interface, and generate a definition parameter file about the target knowledge graph according to the above-mentioned definition parameters.

In the above manner, the user can flexibly complete the construction of the graph structure of the target knowledge graph by generating a definition parameter file of the target knowledge graph by using the first processing interface based on actual business requirements.

Of course, it should be noted that, in the specific implementation, the user can also input specific business requirements in the first processing interface; then the terminal device can automatically generate the target knowledge graph according to the business requirements and the data characteristics of the target source data. definition parameter file.

Then, the user can use the second processing interface to efficiently and simply construct a target knowledge extraction unit that matches the target source data and meets the business requirements according to the preset construction rules and the data structure type of the target source data. Wherein, the above-mentioned target knowledge extraction unit is used for processing the target source data to extract the entity relationship file for generating the target knowledge graph. Specifically, the entity relationship file may contain a plurality of triplet data groups extracted based on the target source data. Each triple data group may include at least two data objects and one data relationship, wherein two data objects in the same triple data group may be connected by a data relationship.

Specifically, as shown in FIG. 2 , the second processing interface may specifically include structures such as a menu bar, a main canvas, and a parameter configuration bar. Among them, in the above-mentioned menu bar, a selection box of data source operator, a selection box of data processing structure, and a selection box of identification termination operator can be displayed. In the above parameter configuration column, a parameter configuration interface for the data source operator, data processing structure, and identification termination operator can be provided.

Further, the above data source operator selection box may specifically include a plurality of preset data source operators for the user to select, for example, a DATAS operator for structured data, a DATAU operator for unstructured data, and the like. The above data processing structure selection box may specifically contain multiple preset data processing structures for the user to select, for example, multiple preset data processing operators (including: SQL operators, HIVE operators) that match structured data , SPARK operator, etc.), pre-trained preset triple extraction model matching unstructured data or semi-structured data, etc. The above identification termination operator selection box may specifically include a plurality of preset identification termination operators for the user to select, for example, MDATAS operators and the like.

Specifically, based on the preset construction rules, the user can select the matching preset data source operator from the menu bar through the above-mentioned second processing interface in combination with the specific business requirements and the data structure type of the target source data. , the preset data processing structure, and the preset identification termination operator; at the same time, the selected preset data source operator, preset data processing structure, and preset identification termination can be selected using the parameter configuration column. operator to obtain the corresponding target source operator, target data processing structure, and target identification termination operator; and then combine the above target source operator, target data processing structure, and target identification termination operator through the main canvas to obtain A target knowledge extraction unit that matches the target source data to meet the user's personalized business needs.

For example, taking the flow record of the target source data as the transaction data of XX Bank's customers in 2020 as an example, first, based on the preset construction rules, considering that the target source data is structured data imported through database tables, you can choose to use The DATAS operator of structured data, and configure the corresponding import parameters to customize the import method of the target source data to obtain the corresponding target source operator. At the same time, you can also choose to use SQL operators suitable for processing structured data, configure the corresponding processing logic, and perform custom settings in the way of knowledge extraction of the target source data to obtain the corresponding target data processing structure. Then, considering the specific business requirements, you can choose to use the MDATAS operator, and configure the corresponding extraction parameters (for example, the identification information of the data object to be extracted, the identification information of the data relationship to be extracted, etc.) The data objects and data relationships are customized and set, and the corresponding target identification termination operator is obtained.

Next, the user can drag the above-mentioned target source operator, target data processing structure, and target identification termination operator into the main canvas, and arrange them in the order of the target source operator, target data processing structure, and target identification termination operator; Then use connecting lines to connect the target source operator and target data processing structure, as well as the target data processing structure and target identification termination operator, to complete the combination and obtain a target knowledge extraction unit that meets the user's business needs and matches the target source data.

In the above manner, the user can use the second processing interface to efficiently and conveniently construct a target knowledge extraction unit that meets the requirements based on actual business requirements.

Of course, it should be noted that, in specific implementation, the above process may also be that the terminal device automatically generates the above target knowledge extraction unit according to the data structure type of target source data and specific business requirements based on preset construction rules.

After obtaining the target knowledge extraction unit, the user can perform a corresponding operation on the knowledge graph construction tool (for example, click the confirmation operation icon) to initiate an operation instruction. The terminal device can respond to the operation instruction initiated by the user, based on the instruction program in the knowledge graph construction tool, call the target instruction extraction unit to process the target source data, so as to efficiently extract the ternary used to construct the target knowledge graph from the target source data. Data group, and then the corresponding entity relationship file.

Further, the terminal device can generate a target knowledge graph associated with the target data source by performing data mapping based on the instruction program in the knowledge graph construction tool and according to the entity relationship file and the definition parameter file.

Through the above methods, users only need to perform simple operations to efficiently and accurately construct target knowledge graphs that meet diverse business needs.

After obtaining the target knowledge graph, the terminal device may display the target knowledge graph to the user for the user to query and use the target knowledge graph.

Users can also use the knowledge graph construction tool to modify and edit the target knowledge graph through the terminal device, and name the target knowledge graph.

In addition, the terminal device can also set a corresponding target identification for the target knowledge graph (for example, the generation number or name of the target knowledge graph can be determined as the target identification corresponding to the target knowledge graph); The knowledge graph is sent to the server. Correspondingly, the server may store the received target knowledge graph carrying the target identifier in the graph database.

Subsequently, when the user needs to query the target knowledge graph again, the terminal device can generate and send the relevant target query statement to the server. The target query statement carries at least a target identifier.

Correspondingly, the server receives the target query sentence; and searches the graph database according to the target identifier carried by the target query sentence, and finds the target knowledge graph for the query indicated by the user. Then, the server can respond to the target query statement, perform a specific query operation on the target knowledge graph, and obtain a corresponding query result; and then feed the query result back to the terminal device.

The terminal device receives and displays the above query result to the user.

In this way, the terminal device can efficiently and conveniently complete the query on the target knowledge graph, and obtain the required query result. Furthermore, the terminal device can perform further data processing according to the query result.

For example, the terminal device can use the flow chart of the target customer's transaction data to further analyze whether the flow of the target customer's transaction data is abnormal; and then use this as a basis to determine whether the target customer is at risk of illegal transactions (for example, money laundering risk, gambling risk, etc. ). As a result, customers who are at risk of illegal transactions can be identified efficiently and accurately.

Referring to FIG. 3 , an embodiment of the present specification provides a method for constructing a knowledge graph. Wherein, when the method is specifically implemented, the following contents may be included.

S301: Acquire target source data.

S302: Determine the data structure type of the target source data.

S303: Build a target knowledge extraction unit matching the target source data according to the preset construction rule and the data structure type of the target source data.

S304: Invoke the target knowledge extraction unit to process the target source data to obtain an entity relationship file that meets the requirements; wherein, the entity relationship file contains a plurality of triplet data groups; the triplet data group at least includes Two data objects connected by a data relation.

S305: Build a target knowledge graph associated with the target source data according to the entity relationship file.

Through the above embodiments, user-side operations can be effectively simplified, and the difficulty of constructing a knowledge graph can be reduced, so that a user can efficiently and accurately construct a target knowledge graph that meets the user's diverse business needs.

In some embodiments, the above-mentioned target source data may specifically refer to source data used to generate a target knowledge graph required by the user. Corresponding to different application scenarios and different business requirements, the above target source data may specifically be data of different contents. Specifically, for example, in a customer's transaction risk prediction scenario, the above-mentioned target source data may specifically be a flow record of the customer's transaction data (eg, asset data, wealth management data, etc.). For another example, in a scenario of verifying the relationship between historical figures, the above-mentioned target source data may specifically be communication records between different historical figures, and the like.

In some embodiments, the above-mentioned target source data may specifically include data of various types of data structures. Specifically, the data structure type of the target source data may specifically include at least one of the following: structured data, unstructured data, semi-structured data, and the like.

The above-mentioned structured data may specifically refer to a data that satisfies a preset data format (for example, a key-value key-value pair format, etc.). Generally, for a certain structured data, according to the preset data format corresponding to the data, the specific attributes of different data contained in the data can be relatively directly determined. For example, for a data that satisfies the key-value pair format, it can be more directly determined which data in the data is the key value and which data is the value value

The above-mentioned semi-structured data may specifically refer to a data that does not satisfy a preset data format, but still satisfies some other conventional formats (eg, table format, etc.). Usually, for a certain semi-structured data, although the specific attributes of different data contained in the data cannot be determined as directly as structured data; but combined with the corresponding conventional format, through certain semantic analysis processing, it can also be determined. out the specific properties of the different data contained in the data.

The above-mentioned unstructured data may specifically refer to a kind of data that does not meet a preset data format and also does not meet certain conventional formats, for example, a text message in a transaction order. Usually, for unstructured data, semantic analysis processing is required to determine the specific attributes of different data contained in the data.

Through the above embodiments, the knowledge graph construction method provided by the embodiments of this specification can be extended to process target source data of various data structure types, so as to meet the diverse business needs of users.

In some embodiments, when specifically acquiring target source data, for structured data, the target source data can be acquired by any one of the following acquisition methods: acquiring the target source data by uploading a local file; acquiring the target by importing an HDFS file Source data; obtain target source data through database table import, etc.

For semi-structured data and unstructured data, the target source data can be obtained by any of the following acquisition methods: obtain the target source data through local file upload; obtain the target source data through the data provided by the connected third party ; Obtain target source data by receiving data transmitted by other distributed clusters, etc.

In some embodiments, in order to obtain the target source data more efficiently, when obtaining the target source data through HDFS file import, the characteristics of HDFS can be used in advance to record the file path of the target source data (corresponding to HDFS metadata) in the In the database; when you need to obtain the target source data, you can query the database to obtain and use the above file path to directly access and obtain the corresponding target source data. In this way, multiple landings in the data acquisition process can be avoided, and the acquisition efficiency of target source data can be improved.

In some embodiments, during specific implementation, the data structure type of the target source data may be detected and determined according to the data structure characteristics of the target source data.

In some embodiments, in order to better take into account the processing of target source data of multiple different data structure types, the data structure type of the target source data can be determined first; and then different data structures can be distinguished according to the data structure type of the target source data. Types of target source data, for target source data of different data structure types, are processed in a matching manner to construct a target knowledge graph that meets the user's business needs.

Specifically, two categories can be distinguished first: the first category (including structured data) and the second category (including unstructured data and semi-structured data); and then for the above two categories, according to preset construction rules A matching target knowledge extraction unit is constructed; further, the matching target knowledge extraction unit can be used to process the target source data, so as to construct a corresponding target knowledge graph.

In some embodiments, the above-mentioned construction of a target knowledge extraction unit matching the target source data according to the preset construction rules and the data structure type of the target source data, when specifically implemented, may include the following content:

S1: According to a preset construction rule, filter out a target source operator corresponding to the target source data from a plurality of preset data source operators; wherein, the target source operator is used to connect the target source data into the target knowledge extraction unit;

S2: Determine a matching target data processing structure according to the data structure type of the target source data; wherein, the target data processing structure is used to process the target source data to obtain a plurality of triplet data groups;

S3: Determine and configure a target identification termination operator; wherein the target identification termination operator is used to extract a required triplet data group from a plurality of triplet data groups output by the target data processing structure to obtain a corresponding entity relationship documents;

S4: Combine the target source operator, the target data processing structure and the target identification termination operator to obtain a target knowledge extraction unit matching the target data source.

Through the above embodiments, a target knowledge extraction unit that matches the target source data and meets the user's business requirements can be accurately established based on the preset construction rules and in combination with data features such as the data structure type of the target source data.

In some embodiments, according to the data structure type of the target source data, the matching target data processing structure is determined. In specific implementation, the following content may be included: When determining that the data structure type of the target source data is structured data In the case of , select an initial processing operator from a plurality of preset data processing operators; perform corresponding configuration on the initial processing operator to obtain a target processing operator; and determine the target processing operator as a relative The matching target data processing structure.

Through the above-mentioned embodiment, based on the preset construction rules, for the target source data whose data structure type is structured data, a target data processing structure with strong pertinence and relatively matching can be determined.

In some embodiments, according to the user's business requirements, the data characteristics of the target source data, and the user's preferred programming language, etc., a preset data processing operator that meets the requirements can be selected from a plurality of preset data processing operators. sub as the initial processing operator. Further, the initial processing operator can be configured with processing logic according to business requirements, so that a relatively matching target data processing structure can be obtained.

In some embodiments, the preset data processing operator may specifically include at least one of the following: SQL operator, HIVE operator, SPARK operator, and the like.

Of course, it should be noted that the preset data processing operators listed above are only a schematic illustration. During specific implementation, other types of data processing operators may also be introduced as preset data processing operators according to specific conditions and the programming language used.

Through the above embodiment, for the target source data whose data structure type is structured data, a variety of optional preset data processing operators can be prepared and provided to obtain a target data processing structure that meets the requirements, so that the matching degree can be obtained. Relatively higher and more accurate target data processing structure.

In some embodiments, determining a matching target data processing structure according to the data structure type of the target source data, in specific implementation, may further include: determining that the data structure type of the target source data is unstructured data Or in the case of semi-structured data, the preset triple extraction model is determined as the matching target data processing structure.

Through the above-mentioned embodiments, based on the preset construction rules, for the target source data whose data structure type is unstructured data or semi-structured data, a more targeted and relatively matching target data processing structure can be determined.

In some embodiments, the above-mentioned preset triplet extraction model may specifically refer to a pre-trained model capable of extracting corresponding triplet data groups from text data based on semantic recognition. Wherein, the above-mentioned triple data group may specifically include two data objects connected by a data relationship.

In some embodiments, in the client's transaction risk prediction scenario, the above-mentioned data object may specifically be the client's name, the client's account, or the client's shareholding company, or the like. The above-mentioned data relationship may specifically be a transfer relationship between data objects, a benefit attribution relationship between data objects, or a debt relationship between data objects, and so on. Of course, the data objects and data relationships listed above are only schematic descriptions. According to specific application scenarios and business requirements, the above-mentioned data objects and data relationships may also be data of other contents.

In some embodiments, before the specific implementation, a preset triplet extraction model can be obtained by training in the following ways: obtaining sample text data; marking two data objects with data relationships in the sample text data, and obtaining the marked sample Text data; use the labeled sample text data for model training to obtain a preset triple extraction model.

In some embodiments, after the data structure type of the target source data is determined, when the method is specifically implemented, the method may further include the following content: extracting units from a plurality of preset knowledge according to the data structure type of the target source data The recommended knowledge extraction unit is screened out from the system; the recommended knowledge extraction unit is displayed to the user; the recommended knowledge extraction unit selected by the user is determined as the target knowledge extraction unit.

Through the above embodiment, before the specific implementation, a plurality of preset knowledge extraction units can be pre-configured according to historical processing records for various relatively common target source data and relatively common business requirements; First, according to the data structure type of the target source data, a preset knowledge extraction unit matching the data structure type of the target source data is selected from a plurality of preset knowledge extraction units as the recommended knowledge extraction unit for the user to select; corresponding , the user only needs to select a recommended knowledge extraction unit that meets his own business requirements from a plurality of existing recommended knowledge extraction units according to specific business requirements as the target knowledge extraction unit. Therefore, the target knowledge extraction unit that meets the user's business requirements can be obtained more efficiently and conveniently.

In some embodiments, after the target knowledge extraction unit is constructed and obtained, the target knowledge extraction unit can be called to process the target source data, so as to extract a triple data group from the target source data; The triple data group associated with the user's business requirements is constructed to obtain an entity relationship file that meets the requirements. Wherein, the above entity relationship file may specifically include a plurality of triple data groups associated with the user's business requirements.

In some embodiments, according to the entity relationship file, the above-mentioned construction of the target knowledge graph associated with the target source data may include the following content during specific implementation:

S1: Obtain a definition parameter file about the target knowledge graph; wherein, the definition parameter file includes: definition parameters of data objects and/or definition parameters of data relationships;

S2: Generate a target knowledge graph associated with the target data source by performing data mapping according to the entity relationship file and the definition parameter file.

Through the above embodiments, the entity relationship file and the definition parameter file can be used to efficiently and accurately construct the target knowledge graph required by the user through data mapping.

In some embodiments, the above-mentioned target knowledge graph may specifically be a graph including a plurality of nodes and connecting edges. Among them, each node corresponds to a data object, and each connection edge corresponds to at least one data relationship. And, the nodes are connected by connecting edges.

In some embodiments, when performing data mapping according to the entity relationship file and the definition parameter file, the data object in the definition parameter file may be mapped to a node according to the entity relationship file, and the data relationship may be mapped to a node. A connected edge is converted into the corresponding graph data, so that the target knowledge graph can be constructed.

In some embodiments, when specifically constructing the target knowledge graph, the attribute information of the node and/or the attribute information of the connecting edge can also be determined according to the entity relationship file and the definition parameter file; The attribute information is labeled by connecting the edges, so that the target knowledge graph with relatively richer data content and better effect can be obtained.

In some embodiments, the definition parameter file may further include: index definition parameters; correspondingly, in the process of constructing a target knowledge graph associated with the target source data according to the entity relationship file, the method In a specific implementation, the method may further include: constructing a target query index for the target knowledge graph by using the definition parameters of the data object and/or the definition parameters of the data relationship according to the index definition parameters.

Through the above embodiment, while constructing the target knowledge graph, the target query index for the target knowledge graph can also be constructed and obtained according to the index definition parameters in the definition parameter file, so that the target query index can be used later to more efficiently use and Query the target knowledge graph.

In some embodiments, during specific implementation, the constructed target knowledge graph may be stored in the graph database together with the corresponding target query index, so as to facilitate subsequent use.

In some embodiments, considering that when constructing multiple target knowledge graphs of target source data with a large amount of data in batches, a large amount of data processing resources are often consumed, which is easy to cause a large amount of damage to the system (or server, terminal device, etc.). The processing load affects the stability of the overall operation of the system. Therefore, when constructing multiple target knowledge graphs in batches, the system can also be set to first estimate whether the data processing amount required for constructing each target knowledge graph is greater than a preset threshold processing amount. The above-mentioned preset threshold processing amount may be specifically determined according to the overall processing performance of the system.

When it is determined that the data processing amount required for the construction of the target knowledge graph is less than the preset threshold processing amount, the system can load the data normally, process and construct the corresponding target knowledge graph.

When it is determined that the amount of data processing required for the construction of the target knowledge graph is greater than or equal to the preset threshold processing, the system can suspend the loading of data and the construction processing of the target knowledge graph, and prompt the user who initiated the target knowledge graph, the target knowledge graph The construction and processing of the graph needs to be approved first, and can be executed normally only after the approval is passed. In addition, the system can also monitor the load status of the system in real time. When the load status of the system is determined to allow the construction and processing of the target knowledge graph, the loaded data is restored and the corresponding target knowledge graph is constructed and processed. Thereby, the stable and reliable operation of the whole system can be protected.

In some embodiments, after the target knowledge graph associated with the target source data is constructed according to the entity relationship file, when the method is specifically implemented, the method may further include the following content: receiving a target query statement; wherein, the The target query sentence carries at least the target identification of the target knowledge graph; according to the target identification, the graph database is retrieved to determine the target knowledge graph; in response to the target query sentence, a query operation is performed on the target knowledge graph to obtain the corresponding the query result; feedback the query result.

Through the above embodiment, it is possible to efficiently find out the corresponding target knowledge graph from the graph database in response to the target query statement initiated by the user, and perform the query operation, and timely feedback the relevant query result to the user.

In some embodiments, after the target knowledge graph is determined, it can also be detected whether the target query index of the target knowledge graph is stored in the graph database; when the target query index is detected, the target query statement can be responded to, combined with the target query The index can be used to query the target knowledge graph more efficiently and accurately, which can further improve the query efficiency and improve the user's query experience.

In some embodiments, the target source data includes a flow record of the customer's transaction data; correspondingly, the query result includes a flow diagram of the target customer's transaction data.

Specifically, for example, in a client's transaction risk prediction scenario, the transaction data may be capital data, and the query result may be a flow chart of the target client's capital data. During specific implementation, it is possible to analyze whether the flow of the target customer's capital data is abnormal according to the flow diagram of the target customer's capital data, and then it can be judged whether the target customer has corresponding transaction risks (for example, money laundering risk, gambling risk, fraud risk) Wait).

Through the above embodiments, the construction method of the knowledge graph provided by the embodiments of this specification can be better applied to the prediction scenario of the customer's transaction risk, so as to use the constructed target knowledge graph to accurately and efficiently predict the target customer Whether there are corresponding transaction risks.

It can be seen from the above that the construction method of the knowledge graph provided by the embodiments of this specification, based on the construction method of the knowledge graph, can first determine the data structure type of the target source data to be processed; then according to the preset construction rules and target source data The target knowledge extraction unit that matches the target source data is constructed and obtained; further, the above target knowledge extraction unit can be called to specifically process the target source data, and an entity that meets the requirements containing multiple triple data groups can be obtained. relationship file; and then construct a target knowledge graph associated with the target source data according to the entity relationship file. This can effectively simplify user-side operations, reduce the difficulty of building knowledge graphs, and enable users to efficiently and accurately build knowledge graphs that meet diverse business needs and have better effects.

Referring to FIG. 2 , an embodiment of the present specification further provides a knowledge graph construction tool. The knowledge graph construction tool may at least include: a source data import interface, a first processing interface, and a second processing interface; wherein,

The source data import interface can specifically be used to support users to import target source data;

The first processing interface can specifically be used to support the user to set the definition parameters of the data objects and/or the definition parameters of the data relationships in the target knowledge graph, so as to generate a definition parameter file about the target knowledge graph;

The second processing interface can specifically be used to support the user to determine and combine matching target source operators, target data processing structures, and identification termination operators according to preset construction rules, so as to obtain targets matching the target source data. knowledge extraction unit;

The construction tool of the knowledge graph is used to call the target knowledge extraction unit to process the target source data, and obtain the corresponding entity relationship file; and according to the entity relationship file and the definition parameter file, by performing data mapping, generate a The target knowledge graph associated with the data source.

Through the above embodiments, users can use the above knowledge graph construction tools to efficiently and conveniently realize one-stop construction to obtain target knowledge graphs that meet diverse business needs, thereby effectively simplifying user-side operations and reducing the difficulty of building knowledge graphs , which improves the user experience.

Embodiments of the present specification further provide a server, including a processor and a memory for storing instructions executable by the processor. When specifically implemented, the processor may perform the following steps according to the instructions: acquiring target source data; determining data of the target source data structure type; according to the preset construction rule and the data structure type of the target source data, construct a target knowledge extraction unit matching the target source data; call the target knowledge extraction unit to process the target source data to obtain An entity relationship file that meets the requirements; wherein, the entity relationship file contains a plurality of ternary data groups; the ternary data group includes at least two data objects connected by a data relationship; according to the entity relationship file, constructing A target knowledge graph associated with the target source data.

In order to more accurately complete the above instructions, referring to FIG. 4 , the embodiment of this specification also provides another specific server, wherein the server includes a network communication port 401 , a processor 402 and a memory 403 , and the above structure is achieved by Internal cables are connected so that each structure can carry out specific data interaction.

Specifically, the network communication port 401 may be used to acquire target source data.

The processor 402 can specifically be used to determine the data structure type of the target source data; build a target knowledge extraction unit matching the target source data according to a preset construction rule and the data structure type of the target source data; Invoke the target knowledge extraction unit to process the target source data to obtain an entity relationship file that meets the requirements; wherein, the entity relationship file contains a plurality of triple data groups; Two data objects connected by a relationship; build a target knowledge graph associated with the target source data according to the entity relationship file.

The memory 403 may specifically be used to store corresponding instruction programs.

In this embodiment, the network communication port 401 may be a virtual port bound with different communication protocols, so that different data can be sent or received. For example, the network communication port may be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for mail data communication. In addition, the network communication port may also be a physical communication interface or a communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

In this embodiment, the processor 402 may be implemented in any suitable manner. For example, a processor may take the form of, for example, a microprocessor or a processor and a computer readable medium storing computer readable program code (eg software or firmware) executable by the (micro)processor, logic gates, switches, application specific integrated circuits ( Application Specific Integrated Circuit, ASIC), programmable logic controller and embedded microcontroller form, etc. This manual is not limited.

In this embodiment, the memory 403 may include multiple layers. In a digital system, as long as it can store binary data, it can be a memory; in an integrated circuit, a circuit with a storage function without physical form is also called a memory , such as RAM, FIFO, etc.; in the system, the storage device with physical form is also called memory, such as memory stick, TF card, etc.

The embodiments of this specification also provide a computer storage medium based on the above-mentioned method for constructing a knowledge graph, where the computer storage medium stores computer program instructions, and when the computer program instructions are executed, realizes: acquiring target source data; determining a target The data structure type of the source data; according to the preset construction rules and the data structure type of the target source data, construct a target knowledge extraction unit that matches the target source data; call the target knowledge extraction unit to process the target source data to obtain an entity relationship file that meets the requirements; wherein, the entity relationship file contains a plurality of triple data groups; the triple data group includes at least two data objects connected by a data relationship; according to the entity A relationship file to construct a target knowledge graph associated with the target source data.

In this embodiment, the above-mentioned storage medium includes but is not limited to random access memory (Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), cache (Cache), hard disk (Hard DiskDrive, HDD) or storage Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set according to a standard specified by a communication protocol.

In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer storage medium can be explained in comparison with other embodiments, and will not be repeated here.

Referring to FIG. 5 , at the software level, the embodiments of this specification also provide a knowledge graph construction device, which may specifically include the following structural modules:

Obtaining module 501, which can specifically be used to obtain target source data;

A determination module 502, which can specifically be used to determine the data structure type of the target source data;

The first construction module 503 can be specifically configured to construct a target knowledge extraction unit matching the target source data according to the preset construction rule and the data structure type of the target source data;

The calling module 504 can be specifically configured to call the target knowledge extraction unit to process the target source data to obtain an entity relationship file that meets the requirements; wherein, the entity relationship file contains a plurality of triple data groups; the three The metadata group includes at least two data objects connected by a data relationship;

The second building module 505 may be specifically configured to build a target knowledge graph associated with the target source data according to the entity relationship file.

It should be noted that the units, devices or modules described in the above embodiments may be specifically implemented by computer chips or entities, or by products with certain functions. For the convenience of description, when describing the above device, the functions are divided into various modules and described respectively. Of course, when implementing this specification, the functions of each module can be implemented in the same one or more software and/or hardware, and the modules that implement the same function can also be implemented by a combination of multiple sub-modules or sub-units. The apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

It can be seen from the above that the apparatus for constructing a knowledge graph provided by the embodiments of this specification can effectively simplify user-side operations, reduce the difficulty of constructing a knowledge graph, and enable users to efficiently and accurately construct solutions that meet diverse business needs with better effects. knowledge graph.

In a specific scenario example, the method for constructing a knowledge graph provided in the embodiments of this specification can be used to import data to construct a corresponding knowledge graph. The specific implementation process can be referred to as shown in FIG. 6, including the following steps.

Step 1: Import various types of data (eg, target source data of multiple different data structure types) into the system.

Step 2: Build a graph structure according to the scene requirements (for example, generate a corresponding definition parameter file).

In this scenario example, the construction of the graph structure may include: defining entity information contained in the graph (for example, definition parameters of data objects), relationship information (for example, definition parameters of data relationships), attribute information of entity relationships, and index information . Specifically, for example, the entity VERTEX can be defined to include but not limited to the type label of VERTEX and various attributes of VERTEX; the definition relation EDGE includes but not limited to the type label of EDGE, the starting point type of EDGE, and various attributes of EDGE. For scenarios with specific query requirements, an index (for example, a target query index) can be constructed to improve the efficiency of subsequent queries, which can be specifically constructed on entities, relationships or attributes, and combinations of the three. For example, a point index can be built on VERTEX to improve query efficiency in scenarios that require a query to start with a point.

Step 3: Perform structured processing on various types of data (to obtain corresponding entity relationship files).

In this scenario example, for structured data, you can first select the structured data DATAS operator (for example, the target source operator) to be processed in the main sidebar (in the second processing interface), and drag it into the main canvas. Next, select a data processing operator in the main sidebar, such as an SQL operator, fill in the specific processing logic of the SQL operator (get the target data processing structure) in the configuration sidebar, and click Run the operator. After the operation is successful, select the identification termination operator, fill in the termination operator name MDATAS (get the target identification termination operator), and click the operation operator until the operation is successful.

For unstructured data or semi-structured data, similarly, first drag in the unstructured data DATAU, then select the trained model operator (for example, the preset triple extraction model), click to run the prediction, wait After the operator runs successfully, select the flag to terminate the operator MDATAU until the operation is successful.

Step 4: Perform knowledge mapping on ontology and data.

In this scenario example, first, the data source to be used can be selected as a candidate data source from the full data, and secondly, the ontology model to be mapped can be selected. Then, select an entity VERTEX in the ontology model, and select the data source MDATAS corresponding to VERTEX. Finally, for each attribute of VERTEX, choose a field of MDATAS to map to one of them. And so on, map all entity relationships with entity relationship data source files.

Step 5: Import the graph structure and data into the database (generate the corresponding knowledge graph and store it in the graph database).

In the embodiment of this scenario, when generating and storing the knowledge graph, some configuration information of the graph can also be filled in, including but not limited to the graph name and the like. Then click on the image import button to import the ontology model and data into the knowledge graph in batches.

Step 6: Visually display the map through the visualization module.

In this scenario example, when the user needs to query the graph data, he can fill in and send the corresponding query statement to query the graph data and visualize the query results.

Through the above scenario examples, it is verified that the construction method of the knowledge graph provided by the embodiments of this specification is a one-stop, relatively simple and efficient method, and has the following advantages: First, it is a non-technical method for analyzing and exploring using the knowledge graph technology. The background business personnel provide a simple and efficient graph construction and visual display platform, and proposes ease of use optimization for each link of the construction, which solves the disadvantage that the graph has a large number of available scenarios but a high technical threshold; the second is to comprehensively consider and summarize the extraction. The indispensable aspects of the map construction process are formed into a one-stop map construction system that is simple and easy to use The construction processes are in line with the user's cognitive and cognitive logic, providing new ideas for the creation of graph construction tools; thirdly, for the knowledge extraction module, a DAG-based data processing process is proposed, which is more powerful and easier than common rule models. use.

Although the present specification provides method operation steps as described in the embodiments or flow charts, more or less operation steps may be included based on conventional or non-inventive means. The sequence of steps enumerated in the embodiments is only one of the execution sequences of many steps, and does not represent the only execution sequence. When an actual device or client product is executed, the methods shown in the embodiments or the accompanying drawings may be executed sequentially or in parallel (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, product or device comprising a list of elements includes not only those elements, but also others not expressly listed elements, or also include elements inherent to such a process, method, product or device. Without further limitation, it does not preclude the presence of additional identical or equivalent elements in a process, method, product or apparatus comprising the stated elements. The terms first, second, etc. are used to denote names and do not denote any particular order.

Those skilled in the art also know that, in addition to implementing the controller in the form of pure computer-readable program code, the controller can be implemented as logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded devices by logically programming the method steps. The same function can be realized in the form of a microcontroller, etc. Therefore, such a controller can be regarded as a hardware component, and the devices included therein for realizing various functions can also be regarded as a structure within the hardware component. Or even, the means for implementing various functions can be regarded as both a software module implementing a method and a structure within a hardware component.

This specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

From the description of the above embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solutions of this specification can be embodied in the form of software products in essence, and the computer software products can be stored in storage media, such as ROM/RAM, magnetic disks, optical disks, etc., including several instructions to make a A computer device (which may be a personal computer, a mobile terminal, a server, or a network device, etc.) executes the methods described in various embodiments or some parts of the embodiments in this specification.

The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. This specification can be used in numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable electronics, network PCs, minicomputers, mainframe computers, including the above Distributed computing environment of any system or device, etc.

Although this specification has been described by way of examples, those of ordinary skill in the art will recognize that there are many modifications and changes to this specification without departing from the spirit of the specification, and it is intended that the appended claims include such modifications and changes without departing from the spirit of the specification.

Claims (15)

1. A method for constructing a knowledge graph, comprising:

acquiring target source data;

determining a data structure type of target source data;

constructing a target knowledge extraction unit matched with the target source data according to a preset construction rule and the data structure type of the target source data;

calling the target knowledge extraction unit to process the target source data to obtain an entity relationship file meeting the requirements; wherein, the entity relation file comprises a plurality of ternary data groups; the ternary data set at least comprises two data objects connected through a data relationship;

and constructing a target knowledge graph associated with the target source data according to the entity relationship file.

2. The method of claim 1, wherein the data structure type of the target source data comprises at least one of: structured data, unstructured data, semi-structured data.

3. The method of claim 2, wherein constructing a target knowledge extraction unit matched with the target source data according to a preset construction rule and a data structure type of the target source data comprises:

screening a target source operator corresponding to target source data from a plurality of preset data source operators according to a preset construction rule; the target source operator is used for accessing the target source data to a target knowledge extraction unit;

determining a matched target data processing structure according to the data structure type of the target source data; the target data processing structure is used for processing target source data to obtain a plurality of ternary data groups;

determining and configuring a target identifier termination operator; the target identification termination operator is used for extracting a ternary data group meeting the requirement from a plurality of ternary data groups output by the target data processing structure so as to obtain a corresponding entity relationship file;

and combining the target source operator, the target data processing structure and the target identification termination operator to obtain a target knowledge extraction unit matched with the target data source.

4. The method of claim 3, wherein determining a matching target data processing structure based on the data structure type of the target source data comprises:

screening an initial processing operator from a plurality of preset data processing operators under the condition that the data structure type of the target source data is determined to be structured data;

correspondingly configuring the initial processing operator to obtain a target processing operator; and determining the target processing operator as a matched target data processing structure.

5. The method of claim 4, wherein the predetermined data processing operator comprises at least one of: SQL operator, HIVE operator and SPARK operator.

6. The method of claim 3, wherein determining a matching target data processing structure based on the data structure type of the target source data comprises:

and under the condition that the data structure type of the target source data is determined to be unstructured data or semi-structured data, determining a preset triple extraction model as a matched target data processing structure.

7. The method of claim 1, wherein after determining the data structure type of the target source data, the method further comprises:

screening recommended knowledge extraction units from a plurality of preset knowledge extraction units according to the data structure type of the target source data;

presenting the recommended knowledge extraction unit to a user;

and determining the recommended knowledge extraction unit selected by the user as the target knowledge extraction unit.

8. The method of claim 1, wherein constructing a target knowledge-graph associated with the target source data from the entity relationship file comprises:

acquiring a definition parameter file related to a target knowledge graph; wherein the defining parameter file comprises: defining parameters of data objects and/or defining parameters of data relations;

and generating a target knowledge graph associated with the target data source by performing data mapping according to the entity relation file and the definition parameter file.

9. The method of claim 8, wherein the definition parameter file further comprises an index definition parameter;

correspondingly, in the process of constructing the target knowledge graph associated with the target source data according to the entity relationship file, the method further comprises the following steps:

and constructing a target query index aiming at the target knowledge graph by using the definition parameters of the data objects and/or the definition parameters of the data relation according to the index definition parameters.

10. The method of claim 1, wherein after constructing a target knowledge-graph associated with the target source data from the entity relationship file, the method further comprises:

receiving a target query statement; the target query statement at least carries a target identification of a target knowledge graph;

retrieving a graph database according to the target identification to determine a target knowledge graph;

responding to the target query statement, and performing query operation on the target knowledge graph to obtain a corresponding query result;

and feeding back the query result.

11. The method of claim 10, wherein the target source data comprises a running record of the customer's transaction data; correspondingly, the query result comprises a flow chart of the transaction data of the target customer.

12. A knowledge graph building tool, comprising at least: the system comprises a source data import interface, a first processing interface and a second processing interface; wherein,

the source data import interface is used for supporting a user to import target source data;

the first processing interface is used for supporting a user to set definition parameters of data objects and/or definition parameters of data relations in the target knowledge graph so as to generate a definition parameter file related to the target knowledge graph;

the second processing interface is used for supporting a user to determine and combine a matched target source operator, a target data processing structure and an identifier termination operator according to a preset construction rule so as to obtain a target knowledge extraction unit matched with the target source data;

the knowledge graph construction tool is also used for calling a target knowledge extraction unit to process target source data to obtain a corresponding entity relationship file; and generating a target knowledge graph associated with the target data source by performing data mapping according to the entity relationship file and the definition parameter file.

13. An apparatus for constructing a knowledge graph, comprising:

the acquisition module is used for acquiring target source data;

the determining module is used for determining the data structure type of the target source data;

the first construction module is used for constructing a target knowledge extraction unit matched with the target source data according to a preset construction rule and the data structure type of the target source data;

the calling module is used for calling the target knowledge extraction unit to process the target source data so as to obtain an entity relationship file meeting the requirements; wherein, the entity relation file comprises a plurality of ternary data groups; the ternary data set at least comprises two data objects connected through a data relationship;

and the second construction module is used for constructing a target knowledge graph associated with the target source data according to the entity relationship file.

14. A server comprising a processor and a memory for storing processor-executable instructions which, when executed by the processor, implement the steps of the method of any one of claims 1 to 11.

15. A computer storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 11.

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