CN117474091A - Knowledge graph construction method, device, equipment and storage medium - Google Patents

Knowledge graph construction method, device, equipment and storage medium Download PDF

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CN117474091A
CN117474091A CN202311675016.8A CN202311675016A CN117474091A CN 117474091 A CN117474091 A CN 117474091A CN 202311675016 A CN202311675016 A CN 202311675016A CN 117474091 A CN117474091 A CN 117474091A
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data source
data
knowledge
knowledge graph
target
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周权彪
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Agricultural Bank of China
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Agricultural Bank of China
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G—PHYSICS
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
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    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/29—Graphical models, e.g. Bayesian networks
    • G06F18/295—Markov models or related models, e.g. semi-Markov models; Markov random fields; Networks embedding Markov models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
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    • G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
    • G06Q20/401—Transaction verification
    • G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The invention discloses a knowledge graph construction method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring a target data source; the target data source is related information of a customer account in a financial institution; inputting the target data source into a data prediction model to obtain a predicted data source; the prediction data source represents the relevant information of the client account at the future moment; and constructing a knowledge graph based on the predicted data source. The method is utilized: and generating a data source state at the next moment on the basis of the historical data, constructing and generating a knowledge graph by adopting the data source at the next moment, and improving the prediction and discovery capability of the knowledge graph on potential and most-likely money laundering behaviors.

Description

Knowledge graph construction method, device, equipment and storage medium
Technical Field
The embodiment of the invention relates to the technical field of risk assessment, in particular to a knowledge graph construction method, a knowledge graph construction device, knowledge graph construction equipment and a storage medium.
Background
Knowledge graph is a large-scale structured knowledge network graph library for symbolically describing concepts and their interrelationships in the physical world, describing various entities or concepts and their relationships that exist in the real world. At present, financial institutions such as domestic banks, dealer and the like widely apply knowledge maps to the financial field. However, the current knowledge graph application mainly predicts and discovers the knowledge graph according to the data such as the transaction flow which already occurs, and has hysteresis and limitation.
Disclosure of Invention
The embodiment of the invention provides a knowledge graph construction method, a device, equipment and a storage medium, wherein the data source state of the next moment is generated on the basis of historical data, the knowledge graph is constructed and generated by adopting the data source of the next moment, and the prediction and discovery capability of the knowledge graph on potential and most likely money laundering behaviors are improved.
In a first aspect, an embodiment of the present invention provides a knowledge graph construction method, where the method includes:
acquiring a target data source; the target data source is related information of a customer account in a financial institution;
inputting the target data source into a data prediction model to obtain a predicted data source; the prediction data source represents the relevant information of the client account at the future moment;
and constructing a knowledge graph based on the predicted data source.
In a second aspect, an embodiment of the present invention further provides a knowledge graph construction apparatus, where the apparatus includes:
the acquisition module is used for acquiring a target data source; the target data source is related information of a customer account in a financial institution;
the data source prediction module is used for inputting the target data source into a data prediction model to obtain a predicted data source; the prediction data source represents the relevant information of the client account at the future moment;
and the knowledge graph construction module is used for constructing a knowledge graph based on the prediction data source.
In a third aspect, embodiments of the present disclosure further provide an electronic device, including:
one or more processors;
storage means for storing one or more programs,
and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the knowledge graph construction method provided by the embodiment of the disclosure.
In a fourth aspect, the disclosed embodiments also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing a knowledge-graph construction method that implements the disclosed embodiments.
The invention discloses a knowledge graph construction method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring a target data source; the target data source is related information of a customer account in a financial institution; inputting the target data source into a data prediction model to obtain a predicted data source; the prediction data source represents the relevant information of the client account at the future moment; and constructing a knowledge graph based on the predicted data source. The method is utilized: and generating a data source state at the next moment on the basis of the historical data, constructing and generating a knowledge graph by adopting the data source at the next moment, and improving the prediction and discovery capability of the knowledge graph on potential and most-likely money laundering behaviors.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a flowchart of a knowledge graph construction method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a knowledge graph construction device according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
It will be appreciated that prior to using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, usage range, usage scenario, etc. of the personal information related to the present disclosure in an appropriate manner according to the relevant legal regulations.
For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.
As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, a selection control for the user to select to provide personal information to the electronic device in a 'consent' or 'disagreement' manner can be carried in the popup window.
It will be appreciated that the above-described notification and user authorization process is merely illustrative and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.
It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.
Example 1
Fig. 1 is a flowchart of a knowledge graph construction provided by an embodiment of the present disclosure, where the embodiment of the present disclosure is suitable for providing a solution to the problem that a conventional knowledge graph has insufficient prediction and discovery capabilities for potential and most likely money laundering actions, the method may be performed by a knowledge graph construction apparatus, where the apparatus may be implemented in a form of software and/or hardware, and optionally, may be implemented by an electronic device, where the electronic device may be a mobile terminal, a PC side, a server, or the like.
As shown in fig. 1, a knowledge graph construction method provided by an embodiment of the present disclosure may specifically include the following steps:
s110, acquiring a target data source.
Wherein the target data source is information related to a customer account in the financial institution.
In this embodiment, the target data source is related information of a customer account in the financial institution, where the related information may be customer and account information.
S120, inputting the target data source into the data prediction model to obtain a predicted data source.
The prediction data source characterizes information about the customer account at a future time.
Wherein the data prediction model is a Markov chain state transition matrix model
Specifically, a target data source is input into a data prediction model to obtain a predicted data source.
For example, if the initial state of an event at time 0 is known, using the state transition matrix, the probability that the event is in various possible states at time k after k state transitions can be obtained, so as to obtain the state probability prediction of the event at time k.
On the basis of the embodiment, training the Markov chain state transition matrix model specifically comprises the following steps:
a1 A historical data source is obtained.
The historical data source is the historical related information of the customer account in the financial institution;
b1 Classifying the historical data sources to obtain classified data sources.
c1 Extracting attribute information in the categorized data source.
d1 Building a state transition matrix based on the attribute information for training to obtain a trained Markov chain state transition matrix model.
In this embodiment, classifying the data source may include: structured data, unstructured data, and semi-structured data.
Illustratively, structured data such as relational databases, unstructured data such as pictures, audio, video, and semi-structured data such as encyclopedia, and the like.
Specifically, a historical data source is obtained, and the historical data source is classified through an automatic script of the existing financial system to obtain a classified data source. And establishing a model layer, modeling structured data, unstructured data and semi-structured data, and performing discretization modeling on the client account transaction state and the like. And establishing a Markov chain state transition matrix model. A state transition matrix is established for each attribute of each entity. And training and establishing a state transition matrix aiming at the state model data, and predicting the state of the next time point by using the state transition matrix.
S130, constructing a knowledge graph based on the predicted data source.
And performing data processing based on the predicted data source to obtain candidate knowledge units, processing the candidate knowledge units to obtain target knowledge units, performing data processing on the target knowledge units, and constructing a knowledge graph.
On the basis of the embodiment, the feature data is subjected to standardization processing, and the standard feature data is obtained specifically by the following steps:
a2 Data processing is performed based on the predicted data source to obtain candidate knowledge units.
Specifically, candidate knowledge units are obtained from a predicted data source, and structured information such as entities, relations, entity attributes and the like is automatically extracted from semi-structured and unstructured data through a knowledge extraction technology and stored in a mode layer and a data layer of a knowledge graph.
b2 Processing from the candidate knowledge units to obtain the target knowledge units.
And then processing and integrating the candidate knowledge units to integrate the net-shaped knowledge structure, and for various possible expressions of some entities, eliminating contradiction and ambiguity concepts, eliminating redundancy and error concepts and removing a large amount of redundancy and error information to obtain the target knowledge unit.
c2 Data processing is carried out on the target knowledge unit, and a knowledge graph is constructed.
Specifically, data processing is performed on the target knowledge unit, and a knowledge graph is constructed.
Based on the above embodiment, the data extraction is performed based on the predicted data source in the embodiment of the present invention, and the obtaining of the candidate knowledge unit includes the following steps:
a21 Data extraction is performed based on unstructured data and semi-structured data in the predicted data source, and entity information is obtained.
The entity information comprises entity names, relationships among the entities and attribute information of the entities.
Specifically, extracting the entity, namely automatically identifying the named entity from the data layer; extracting the relationship, namely extracting the association relationship between the entities from the data layer, and utilizing information extraction methods such as pattern matching, statistical machine learning and the like to link the entities through the relationship so as to form a net-shaped knowledge structure; attribute extraction, i.e., collecting attribute information of a particular entity from different data sources.
And adopting a data mining method to directly mine a relation mode between the entity attribute and the attribute value from the text, thereby realizing the positioning of the attribute name and the attribute value in the text.
a22 Processing the entity information to obtain candidate knowledge units.
Specifically, entity information is processed to obtain candidate knowledge units.
The invention discloses a knowledge graph construction method, which comprises the following steps: acquiring a target data source; the target data source is the relevant information of the customer account in the financial institution; inputting the target data source into a data prediction model to obtain a predicted data source; predicting relevant information of the data source representation client account at future time; and constructing a knowledge graph based on the predicted data source. The method is utilized: and generating a data source state at the next moment on the basis of the historical data, constructing and generating a knowledge graph by adopting the data source at the next moment, and improving the prediction and discovery capability of the knowledge graph on potential and most-likely money laundering behaviors.
Example two
Fig. 2 is a schematic structural diagram of a knowledge graph construction device according to an embodiment of the present invention, where, as shown in fig. 2, the device includes: the system comprises an acquisition module 210, a data source prediction module 220 and a knowledge graph construction module 230;
an acquisition module 210, configured to acquire a target data source; the target data source is related information of a customer account in a financial institution;
the data source prediction module 220 is configured to input the target data source into a data prediction model to obtain a predicted data source; the prediction data source represents the relevant information of the client account at the future moment;
a knowledge graph construction module 230, configured to construct a knowledge graph based on the prediction data source.
The technical scheme provided by the embodiment of the disclosure is that the method is utilized: and generating a data source state at the next moment on the basis of the historical data, constructing and generating a knowledge graph by adopting the data source at the next moment, and improving the prediction and discovery capability of the knowledge graph on potential and most-likely money laundering behaviors.
Further, the data source prediction module 220 may be configured to:
the data prediction model is a Markov chain state transition matrix model.
Further, the device also comprises; a model training module;
the model training module may be for:
acquiring a historical data source; the historical data source is historical related information of a customer account in a financial institution;
classifying the historical data sources to obtain classified data sources;
extracting attribute information in the classified data sources;
and building a state transition matrix based on the attribute information to train, and obtaining a trained Markov chain state transition matrix model.
Further, the knowledge graph construction module 230 may be configured to:
performing data processing based on the predicted data source to obtain candidate knowledge units;
processing from the candidate knowledge units to obtain target knowledge units;
and carrying out data processing on the target knowledge unit to construct a knowledge graph.
Further, the knowledge graph construction module 230 may be configured to:
the predicted data source comprises: structured data, unstructured data, and semi-structured data.
Further, the knowledge graph construction module 240 may be configured to: performing data extraction based on unstructured data and semi-structured data in the predicted data source to obtain entity information; the entity information comprises entity names, relationships among the entities and attribute information of the entities;
and processing the entity information to obtain candidate knowledge units.
The device can execute the method provided by all the embodiments of the invention, and has the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in this embodiment can be found in the methods provided in all the foregoing embodiments of the invention.
Example III
Fig. 3 presents a schematic view of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as a knowledge graph construction method.
In some embodiments, the knowledge graph construction method may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the knowledge graph construction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the knowledge-graph construction method in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. The knowledge graph construction method is characterized by comprising the following steps of:
acquiring a target data source; the target data source is related information of a customer account in a financial institution;
inputting the target data source into a data prediction model to obtain a predicted data source; the prediction data source represents the relevant information of the client account at the future moment;
and constructing a knowledge graph based on the predicted data source.
2. The method of claim 1, wherein the data prediction model is a markov chain state transition matrix model.
3. The method of claim 2, wherein training the markov chain state transition matrix model comprises:
acquiring a historical data source; the historical data source is historical related information of a customer account in a financial institution;
classifying the historical data sources to obtain classified data sources;
extracting attribute information in the classified data sources;
and building a state transition matrix based on the attribute information to train, and obtaining a trained Markov chain state transition matrix model.
4. The method of claim 1, wherein constructing a knowledge-graph based on the predicted data source comprises:
performing data processing based on the predicted data source to obtain candidate knowledge units;
processing from the candidate knowledge units to obtain target knowledge units;
and carrying out data processing on the target knowledge unit to construct a knowledge graph.
5. The method of claim 4, wherein the predicting data sources comprises: structured data, unstructured data, and semi-structured data.
6. The method of claim 5, wherein extracting data based on the predicted data source to obtain candidate knowledge units comprises:
performing data extraction based on unstructured data and semi-structured data in the predicted data source to obtain entity information; the entity information comprises entity names, relationships among the entities and attribute information of the entities;
and processing the entity information to obtain candidate knowledge units.
7. The knowledge graph construction device is characterized by comprising:
the acquisition module is used for acquiring a target data source; the target data source is related information of a customer account in a financial institution;
the data source prediction module is used for inputting the target data source into a data prediction model to obtain a predicted data source; the prediction data source represents the relevant information of the client account at the future moment;
and the knowledge graph construction module is used for constructing a knowledge graph based on the prediction data source.
8. The apparatus of claim 7, wherein the knowledge-graph construction module is further configured to:
performing data processing based on the predicted data source to obtain candidate knowledge units;
processing from the candidate knowledge units to obtain target knowledge units;
and carrying out data processing on the target knowledge unit to construct a knowledge graph.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the knowledge-graph construction method of any one of claims 1-6.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the knowledge-graph construction method of any one of claims 1-6 when executed.
CN202311675016.8A 2023-12-07 2023-12-07 Knowledge graph construction method, device, equipment and storage medium Pending CN117474091A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117668259A (en) * 2024-02-01 2024-03-08 华安证券股份有限公司 Knowledge-graph-based inside and outside data linkage analysis method and device

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117668259A (en) * 2024-02-01 2024-03-08 华安证券股份有限公司 Knowledge-graph-based inside and outside data linkage analysis method and device
CN117668259B (en) * 2024-02-01 2024-04-26 华安证券股份有限公司 Knowledge-graph-based inside and outside data linkage analysis method and device

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