CN115757821A - Audit problem positioning method, device and equipment based on knowledge graph - Google Patents
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Abstract
The embodiment of the specification provides a method, a device and equipment for positioning audit problems based on a knowledge graph, and is applied to the technical field of big data. The method comprises the following steps: acquiring data to be analyzed; extracting information main bodies corresponding to different categories from data to be analyzed; the information main body is used for embodying auditing problems and/or responsibility objects related to the data to be analyzed; searching the associated data and the associated relation corresponding to the information main body in a knowledge graph; the knowledge graph is used for describing the corresponding relation among all information main bodies; and determining an audit problem positioning result corresponding to the data to be analyzed based on the associated data and the association relation. By constructing the knowledge graph in advance, the method can quickly find corresponding information aiming at the audit problem needing to be positioned through the knowledge graph, reduces the time consumed by manual analysis aiming at the audit problem, ensures timely and effective processing of the audit problem, and is beneficial to normal processing of corresponding services.
Description
Technical Field
The embodiment of the specification relates to the technical field of big data, in particular to a knowledge graph-based audit problem positioning method, device and equipment.
Background
In the actual business processing process, the auditing department is generally mainly responsible for analyzing data to find out problems occurring inside. The purpose of finding a problem is to enforce responsibility for the audit problem, including determining the specific content of the audit problem, the type of problem involved, the subject of responsibility for the problem, etc. After the problem is processed, the problem needs to be rectified and traced to determine whether the problem has been completely corrected.
With the increase of departments, business volumes and the like, when positioning is performed aiming at the content of the audit problem and the responsible person, a large number of departments, products and data are often involved, a large number of data and data need to be consulted in the positioning process, and then great time and resources are consumed. The current manpower searching mode can only be suitable for the condition with few problems, and when the searched audit problems are too many, the positioning and processing of the audit problems can not be effectively ensured, so that the development of other department services is influenced, and the normal work is interfered. Therefore, a method for quickly and effectively locating the audit problem is needed.
Disclosure of Invention
The embodiment of the specification aims to provide a method, a device and equipment for positioning audit problems based on a knowledge graph, so as to solve the problem of how to quickly and effectively position the audit problems.
In order to solve the above technical problem, an embodiment of the present specification provides a method for locating an audit problem based on a knowledge graph, including: acquiring data to be analyzed; extracting information main bodies corresponding to different categories from data to be analyzed; the information main body is used for embodying auditing problems and/or responsibility objects related to the data to be analyzed; searching the associated data and the associated relation corresponding to the information main body in a knowledge graph; the knowledge graph is used for describing the corresponding relation among all information main bodies; and determining an audit problem positioning result corresponding to the data to be analyzed based on the associated data and the association relation.
The embodiment of this description still provides an audit problem positioner based on knowledge-graph, includes: the data to be analyzed acquisition module is used for acquiring data to be analyzed; the information main body extraction module is used for extracting information main bodies corresponding to different categories from the data to be analyzed; the information main body is used for embodying auditing problems and/or responsibility objects related to the data to be analyzed; the data and relation searching module is used for searching the associated data and the associated relation corresponding to the information main body in the knowledge graph; the knowledge graph is used for describing the corresponding relation among all information main bodies; and the audit problem positioning module is used for determining an audit problem positioning result corresponding to the data to be analyzed based on the associated data and the association relation.
An embodiment of the present specification further provides an electronic device, including a memory and a processor; the memory is for storing a computer program/instructions that when executed perform the steps of the above-described knowledge-graph based audit problem locating method.
Embodiments of the present specification also provide a computer readable storage medium having stored thereon a computer program/instructions which, when executed by a processor, implement the steps of the above-described method for locating an audit problem based on a knowledge-graph.
Embodiments of the present description also provide a computer program product comprising a computer program/instructions that, when executed by a processor, implement the steps of the above-described method for locating an audit problem based on a knowledge-graph.
As can be seen from the technical solutions provided by the embodiments of the present specification, in the embodiments of the present specification, after the data to be analyzed is obtained, the method can extract different types of information bodies from the data to be analyzed, and further determine, based on the association relationship between different information bodies in the knowledge graph, the association data and the association relationship of the information body corresponding to the current data to be analyzed, and can effectively locate the audit problem through the association data and the association relationship. According to the method, the relation between different information main bodies is combed by constructing the knowledge map in advance, so that the corresponding information can be quickly found through the knowledge map aiming at the audit problem needing to be positioned, the time consumed by manual analysis aiming at the audit problem is reduced, the audit problem is ensured to be timely and effectively processed, and the normal processing of the corresponding service is facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the specification, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a method for locating audit issues based on a knowledge-graph in an embodiment of the present disclosure;
FIG. 2 is a diagram illustrating an information body extraction result according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a knowledge-graph structure according to an embodiment of the present disclosure;
FIG. 4 is a block diagram of an audit problem locating device based on a knowledge graph according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort shall fall within the protection scope of the present specification.
In order to solve the technical problem, an embodiment of the present specification provides a method for locating an audit problem based on a knowledge graph. The executive main body of the audit problem positioning method based on the knowledge graph can be audit problem positioning equipment based on the knowledge graph. The audit problem positioning equipment based on the knowledge graph comprises but is not limited to a server, an industrial personal computer, a PC and the like. As shown in FIG. 1, the audit problem location method based on the knowledge-graph can comprise the following specific implementation steps.
S110: and acquiring data to be analyzed.
The data to be analyzed is the data which needs to be analyzed. In an actual auditing process, when a problem occurs, only batch data associated with the problem, such as all data or all program codes in a corresponding report, can be acquired at an initial stage, and it is difficult to directly locate key information of the problem. Therefore, it is necessary to analyze the data to be analyzed to determine the specific positioning result.
In some embodiments, the data to be analyzed may include at least one of an audit question, an audit procedure, and an audit report. Accordingly, other types of data to be analyzed may be selected according to requirements, and the method is not limited to the above example, and is not limited thereto.
S120: extracting information main bodies corresponding to different categories from data to be analyzed; the information body is used for embodying auditing questions and/or responsibility objects related to the data to be analyzed.
After the data to be analyzed is obtained, information main bodies corresponding to different categories can be extracted from the data to be analyzed, and audit problems are located based on the information main bodies so as to reduce the calculated amount in the processing process.
The information body is used for embodying audit problems and/or responsibility objects related to the data to be analyzed, namely, the information body is limited according to the specific content of the audit problems and related specific related objects.
In some embodiments, the information subject includes at least one of a problem entity, a responsible person entity, and a problem and responsible person relationship. The question entity represents the specific content involved in the audit question and may be a general question type; the responsible person entity can be a responsible person corresponding to the audit problem, can be a responsible person for processing the audit problem, and can also be a corresponding manager or an incident person and the like; the problem and responsible person relationship is used to describe the relationship between the problem and the responsible person. For example, a problem entity may include a problem identification, a problem name, a problem summary, a problem label, problem content, and the like; the responsible person entity can comprise an employee number, an employee name, a number of a department to which the entity belongs, a name of the department to which the entity belongs, a supervision unit and the like; the problem and responsible person relationship may be set up for the specific responsibilities of the responsible person.
In some embodiments, the information extraction subject may further extract structured data and unstructured data from the data to be analyzed, respectively; the structured data comprises data corresponding to a preset structural form, and the unstructured data is data without a fixed structural type.
For example, the structured data may be audit programs, audit questions, bank internal institution data, bank internal line data, bank internal staff data, transaction voucher data, and the like; unstructured data may be audit gross, audit reports, regulations, interview material, interview records, conference summaries, etc.
The format of the structured data and the unstructured data can be set according to specific situations in practical application, and is not limited to the above examples.
After the structured data and the unstructured data are respectively extracted, the structured data can well embody the corresponding information main bodies due to specific fixed structural formats of the structured data, so that the corresponding information main bodies can be directly extracted from the structured data. For example, the structured data may be mapped directly to RDF format.
For unstructured data, a corresponding information body may be determined for data parsing, e.g., may be determined for parsing the semantics of the unstructured data.
In some embodiments, an audit keyword library and an audit rule library may also be utilized to extract information bodies from unstructured data.
The audit keyword library and audit rule library may be pre-constructed libraries. The audit keyword library is used for recording keywords in the audit field, and the audit rule library is used for recording generation rules of the audit program. The audit keyword library and the audit rule library can help to determine the key information in the text, so that the information main body can be quickly positioned based on the audit keyword library and the audit rule library, and the time consumed is reduced while the accuracy of a subsequent comparison result is ensured.
In some specific examples, the audit keyword store may include: information security, internet export, compensation measures, backup policies, recovery policies, sensitive customer data, storage media security, risk identification, suspicious transactions, teller management, emergency plans, consumer rights and interests protection, risk forewarning, payment clearing, business continuation, organizational structures, resource construction, high management personnel, temporary receipt, substitute receipt, defense capability, accounting systems, incident, crisis recovery, outage risk, capital security, stress testing, asset liability management, liquidity management, information sharing, banking, reporting routes, centralized authorization, outsourcing services, country-wide risk, exposure, expense deposit owner = expense owner, fixed asset, scrap approval, purchase review, due diligence, financial, purchase and construction project, depreciation, value added tax, pricing management, and the like.
The audit rule base may include: (1) sentences containing negative adverbs and negative sense words must be key sentences, such as: absence, strict prohibition, ambiguity, inability, deficiency, incompatibility, and the like; (2) sentences containing the bingo structure are all key sentences, such as firewall setting, physical network detection, passing, configuration, closing, opening, execution, establishment and the like; (3) sentences containing words that can be used as both verbs and nouns: approval, registration, authorization, establishment, release and the like; (4) sentences containing some temporal adverbs: regular, timely, least time, as soon as possible, at least, irregular, yearly, quarterly, etc.; (5) sentences containing numbers: 8 hours, within 3 days, three years, two persons, etc.; (6) sentences containing english abbreviations: IRP, ITCP, DRP, ATM, IC, G21, etc. For example, the audit rule base may contain contents of the.
In practical application, the corresponding audit keyword library and audit rule library may be set according to requirements, and are not limited to the above examples, and are not limited thereto.
After obtaining the audit keyword library and the audit rule library, an information subject corresponding to the audit keyword in the audit keyword library can be extracted from the unstructured data, and/or an information subject can be extracted from the unstructured data based on the audit rule in the audit rule library.
The specific extraction process may include (1) entity extraction; an extraction method based on an audit key dictionary and an audit rule base is combined with a statistical-based LSTM _ CRF model; (2) and (3) extracting the relation: and extracting the relation based on the rule and the keyword, and combining with remote supervision learning and a model. (3) Event extraction: and establishing a hierarchical framework based on the extraction of the subject events of the audit problems. In practical application, the above process can be adjusted according to requirements, and is not limited.
Fig. 2 is a schematic diagram of the information body extracted by the above process. The data to be analyzed comprises inherent attributes, auditing programs, systems, auditing reports, meeting summaries, interview records, transaction certificates, meeting summaries and the like, the extracted information main body relates to object parts including mechanisms, persons, conditions, types, processes, links and the like, and the detailed elements comprise COSO elements, main responsible persons, secondary responsible persons, management responsible persons, creators, causes and the like.
The specific process of acquiring the information main body can be adjusted according to the requirements of practical application, and is not limited to this.
S130: searching the associated data and the associated relation corresponding to the information main body in a knowledge graph; the knowledge graph is used for describing the corresponding relation among all information main bodies.
After the information main body corresponding to the data to be analyzed is obtained, the associated data and the associated relation corresponding to the information main body can be searched in the knowledge graph.
The knowledge graph is used for describing corresponding relations among all information main bodies, and the knowledge graph can be obtained by acquiring all data in advance and integrating the relations among the information main bodies in all the data. The relevance between different information main bodies can be visually determined through the knowledge graph, and then the elements related to the audit problem are searched.
The knowledge-graph may be constructed by: firstly, sample data is obtained, information main bodies are extracted from the sample data, the hierarchical relation of each information main body is further determined, and a knowledge graph is constructed in a top-down mode based on the hierarchical relation.
Since the sample data is essentially the same as the data to be analyzed, the extraction of the information subject can be realized in the manner described above. The hierarchical relationship may be determined from the obtained body of information. The hierarchical relationship can be determined based on rules embodied in the data, and can also be set by a manager, and the construction of the knowledge graph can be sequentially completed in a self-determined downward manner according to the determined hierarchical relationship. As shown in fig. 3, a schematic diagram of the constructed knowledge graph is shown, in which different information bodies and relationships between the different information bodies are defined, and the search can be completed quickly and efficiently according to the information body of the data to be analyzed.
In some embodiments, the information entities can be effectively extracted through learning models, such as HMM hidden markov models, CRF conditional random factories, and the like, the construction of entity relationships can be completed through semantic analysis techniques, and the knowledge graph can be displayed in a triple (entity-relationship-entity) manner, so that the construction of the knowledge graph can also be completed.
The specific construction process can be set according to actual conditions, and is not described herein.
S140: and determining an audit problem positioning result corresponding to the data to be analyzed based on the associated data and the association relation.
After the associated data and the associated relationship are determined according to the knowledge graph, a corresponding network relationship can be constructed by using the corresponding relationship between the associated data and the associated relationship. The incidence relation can be used for reflecting the relation between different main bodies, and the incidence data can locate the specific details for the audit problem, so that the audit problem can be effectively located through the incidence data and the incidence relation.
For example, the knowledge graph can be used for extracting characteristic information, subject words, events and association relations from various data such as audit problems, retrieval data, systems, conference summary, transaction certificates, interview records and the like, and performing association, conduction and reasoning, so that the cause of the audit problems can be examined from a comprehensive view angle, the association relations in all aspects of the audit problems can be deeply excavated, and the situation is prevented.
In practical application, other manners may be adopted to locate the audit problem according to requirements, which is not limited to the above example and is not described herein again.
Based on the introduction of the embodiment of the audit problem positioning method based on the knowledge graph, it can be seen that the method can extract different types of information main bodies from the data to be analyzed after the data to be analyzed is obtained, further determine the associated data and the associated relation of the information main body corresponding to the current data to be analyzed based on the associated relation between the different information main bodies in the knowledge graph, and effectively position the audit problem through the associated data and the associated relation. According to the method, the knowledge graph is constructed in advance, and the relation among different information main bodies is combed, so that the corresponding information can be quickly found through the knowledge graph aiming at the auditing problem needing to be positioned, the time consumed by manual analysis aiming at the auditing problem is reduced, the timely and effective treatment of the auditing problem is ensured, and the normal treatment of the corresponding service is facilitated.
An audit problem positioning device based on a knowledge graph in the embodiment of the specification is introduced based on a method for positioning an audit problem based on a knowledge graph corresponding to fig. 1. The audit problem positioning device based on the knowledge graph can be arranged on audit problem positioning equipment based on the knowledge graph. As shown in FIG. 4, the audit problem location device based on the knowledge-graph comprises the following modules.
A data to be analyzed obtaining module 410, configured to obtain data to be analyzed.
An information subject extraction module 420, configured to extract information subjects corresponding to different categories from data to be analyzed; the information body is used for embodying auditing problems and/or responsibility objects related to the data to be analyzed.
A data and relation searching module 430, configured to search a knowledge graph for associated data and associated relations corresponding to the information subject; the knowledge graph is used for describing the corresponding relation among all information main bodies.
An audit question positioning module 440, configured to determine an audit question positioning result corresponding to the data to be analyzed based on the association data and the association relation.
Based on the method for positioning audit problems based on the knowledge graph corresponding to fig. 1, the embodiment of the present specification provides an audit problem positioning device based on the knowledge graph. The knowledge-graph-based audit issue locating device may include a memory and a processor.
In this embodiment, the memory may be implemented in any suitable manner. For example, the memory may be a read-only memory, a mechanical hard disk, a solid state disk, a usb flash disk, or the like. The memory may be used to store computer programs/instructions.
In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller and embedded microcontroller, and so forth. The processor may execute the computer program instructions to implement a method for locating an audit problem based on a knowledge-graph as corresponding to fig. 1.
Based on the method for locating audit problems based on a knowledge graph corresponding to fig. 1, embodiments of the present specification provide a computer-readable storage medium having stored thereon a computer program/instructions. The computer-readable storage medium can be read by a processor based on an internal bus of a device, and the program instructions in the computer-readable storage medium are implemented by the processor.
In this embodiment, the computer-readable storage medium may be implemented in any suitable manner. The computer-readable storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), a Memory card (Memory card), and the like. The computer storage medium stores computer program instructions. The computer program instructions, when executed, implement the program instructions or modules of the embodiments corresponding to fig. 1 of the present specification.
In this embodiment, the processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. In particular, the processor, when arranged on the electronic device, may perform the method steps in the embodiment corresponding to fig. 1.
Based on the method for locating audit problems based on a knowledge graph corresponding to fig. 1, embodiments of the present specification further provide a computer program product including computer programs/instructions. The computer program product may be a program written in a corresponding computer program language, stored in a corresponding storage device in a programmed manner, and transmittable via a computer network. The computer program product may be executed by a processor. In an embodiment of the present specification, the computer program product, when executed, implements the program instructions or modules of the method for locating an audit problem based on a knowledge-graph, as described in the corresponding embodiment of fig. 1.
It should be noted that the method, the device and the equipment for locating the audit problem based on the knowledge graph can be applied to the technical field of big data, and can also be applied to other technical fields except the technical field of big data, which is not limited to this.
In addition, the operations of acquiring, processing, storing, forwarding and the like of all data including user data in the method, the device and the equipment for positioning the audit problems based on the knowledge graph meet the relevant regulations of national laws and regulations.
While the process flows described above include operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, tape storage, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that may be used to store information that may be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of 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, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may 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 memory storage devices.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for locating audit problems based on knowledge graph is characterized by comprising the following steps:
acquiring data to be analyzed;
extracting information main bodies corresponding to different categories from data to be analyzed; the information main body is used for embodying auditing problems and/or responsibility objects related to the data to be analyzed;
searching the associated data and the associated relation corresponding to the information main body in a knowledge graph; the knowledge graph is used for describing the corresponding relation among all information main bodies;
and determining an audit problem positioning result corresponding to the data to be analyzed based on the associated data and the association relation.
2. The method of claim 1, wherein the data to be analyzed includes at least one of audit questions, audit procedures, audit reports.
3. The method of claim 1, wherein said extracting information bodies corresponding to different categories from data to be analyzed comprises:
extracting structured data and unstructured data from the data to be analyzed respectively; the structured data comprises data corresponding to a preset structural form;
directly extracting a corresponding information main body based on the structured data;
the unstructured data is parsed to determine corresponding bodies of information.
4. The method of claim 3, wherein prior to extracting information bodies corresponding to different categories from the data to be analyzed, further comprising:
respectively constructing an audit keyword library and an audit rule library;
correspondingly, the parsing the unstructured data to determine a corresponding information subject includes:
and extracting an information body corresponding to the audit keyword in the audit keyword library from the unstructured data, and/or extracting an information body from the unstructured data based on the audit rule in the audit rule library.
5. The method of claim 1, wherein the knowledge-graph is constructed by:
acquiring sample data;
extracting an information main body from the sample data;
determining the hierarchical relation of each information subject;
and constructing the knowledge graph in a top-down mode based on the hierarchical relation.
6. The method of claim 1, wherein the body of information includes at least one of a problem entity, a responsible person entity, and a problem and responsible person relationship.
7. A knowledge-graph-based audit problem locating device is characterized by comprising:
the data to be analyzed acquisition module is used for acquiring data to be analyzed;
the information main body extraction module is used for extracting information main bodies corresponding to different categories from the data to be analyzed; the information main body is used for embodying auditing problems and/or responsibility objects related to the data to be analyzed;
the data and relation searching module is used for searching the associated data and the associated relation corresponding to the information main body in the knowledge graph; the knowledge graph is used for describing the corresponding relation among all information main bodies;
and the audit problem positioning module is used for determining an audit problem positioning result corresponding to the data to be analyzed based on the associated data and the association relation.
8. An electronic device comprising a memory and a processor; characterized in that the memory is used to store a computer program/instructions which, when executed, implement the steps of the method according to any one of claims 1-6.
9. A computer-readable storage medium, having stored thereon a computer program/instructions, characterized in that the computer program/instructions, when executed, implement the steps of the method according to any of claims 1-6.
10. A computer program product comprising computer program/instructions, characterized in that the computer program/instructions, when executed, implement the steps of the method according to any of claims 1-6.
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| CN117670264A (en) * | 2024-02-01 | 2024-03-08 | 武汉软件工程职业学院(武汉开放大学) | Automatic flow processing system and method for accounting data |
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| CN117670264A (en) * | 2024-02-01 | 2024-03-08 | 武汉软件工程职业学院(武汉开放大学) | Automatic flow processing system and method for accounting data |
| CN117670264B (en) * | 2024-02-01 | 2024-04-19 | 武汉软件工程职业学院(武汉开放大学) | Automatic flow processing system and method for accounting data |
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