CN112579782B - Data processing method, knowledge management system, electronic device, and readable storage medium - Google Patents
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Abstract
The embodiment of the application provides a data processing method, a knowledge management system, electronic equipment and a readable storage medium, and relates to the technical field of knowledge management. Knowledge extraction is performed on the data line to be converted based on the knowledge extractor, knowledge segments are obtained, and a fact knowledge database is built based on the knowledge segments. And responding to the input operation, and acquiring the original principle data and the original skill data. The principle knowledge database is built based on the original principle data, and the skill knowledge database is built based on the original skill data, so that the knowledge management efficiency is improved, and the working efficiency is improved.
Description
Technical Field
The present application relates to the technical field of knowledge management, and in particular, to a data processing method, a knowledge management system, an electronic device, and a readable storage medium.
Background
With the awareness of data awareness in recent years, many enterprises begin to construct data warehouses and data middles, and some enterprises already have a good data foundation. However, the data itself does not generate knowledge, which is just an impression of the real world in virtual space, and the knowledge is higher than the data, and is more close to the nature of things. After undergoing three stages of informatization, big data age, and data center construction, more and more enterprises begin to realize how much innovation comes mainly from own knowledge assets, whose importance and value exceeds that of data assets and even financial assets. The era of knowledge as an enterprise core asset and core driving force has come, and more enterprise managers begin to pay attention to the operation and management of knowledge. Efficient knowledge management systems are required to be built in enterprises with high knowledge density.
At present, many enterprises still indirectly manage knowledge through staff training, document management and data management, human wisdom is required to extract knowledge from training courseware, documents and data and use the knowledge, and a more efficient knowledge management method is lacked.
Disclosure of Invention
In view of the above, the present application provides a data processing method, a knowledge management system, an electronic device, and a readable storage medium to improve the above-described problems.
In a first aspect, the present application provides a data processing method applied to a knowledge management system, the knowledge management system pre-stores a knowledge extractor, the data processing method comprising:
acquiring data to be converted;
knowledge extraction is carried out on the data line to be converted based on the knowledge extractor, a knowledge segment is obtained, and a fact knowledge database is established based on the knowledge segment;
Responding to input operation, and acquiring original principle data and original skill data;
And establishing a principle knowledge database based on the original principle data, and establishing a skill knowledge database based on the original skill data.
In an alternative embodiment, the knowledge decimator comprises a script decimator, and the data to be converted comprises structure class data; the step of obtaining knowledge segments comprises the steps of:
Acquiring a predefined domain category system, wherein the category system comprises categories, category relations and attributes included in the categories;
Extracting at least one instance in the structural class data and the attribute of each instance by using the script extractor;
mapping all the examples and the attribute of each example into the domain category system to obtain a knowledge fragment.
In an alternative embodiment, the knowledge decimator comprises a pattern decimator, the data to be converted comprises unstructured class data; the step of obtaining knowledge segments comprises the steps of:
Acquiring a predefined domain category system, wherein the category system comprises categories, category relations and attributes included in the categories;
Identifying at least one entity included in the unstructured class data using the model extractor;
classifying all the entities to obtain the category of each entity and the attribute of each entity;
Mapping all the entities and the attribute of each entity into the domain category system to obtain a knowledge fragment.
In an alternative embodiment, the raw skill data includes a domain decision map, and the step of obtaining raw skill data includes:
Responding to input operation to obtain at least one business application scene, wherein each business application scene comprises a plurality of subtasks;
responding to the decomposition operation, decomposing each subtask to obtain at least one minimum task, wherein each minimum task represents a workflow configuration file;
And constructing a domain decision map by using all the business application scenes, all the subtasks and all the minimum tasks according to the inclusion relation, and taking the domain decision map as original skill data.
In an alternative embodiment, the knowledge management system pre-stores a format specification configuration file, and the step of establishing a principle knowledge database based on the raw principle data includes:
based on the format specification configuration file, carrying out data mining on the original principle data to obtain principle knowledge data, wherein the principle knowledge data comprises indexes, rules and models;
and establishing an original knowledge database by using the index, the rule and the model.
In an alternative embodiment, the raw skill data includes a domain decision map, and the step of building a skill knowledge database based on the raw skill data includes:
acquiring at least one workflow configuration file included in the domain decision map;
acquiring principle knowledge data corresponding to each workflow configuration file from the principle knowledge database, wherein the principle knowledge data comprises indexes, rules and models;
Configuring each index, each rule and each model according to each workflow configuration file to obtain at least one workflow;
A skills knowledge database is built based on all of the workflows.
In a second aspect, the present application provides a data processing method, applied to a knowledge management system, where the knowledge management system pre-stores a knowledge extractor, and further includes a fact knowledge database, a principle knowledge database, and a skill knowledge database;
The fact knowledge database obtains knowledge fragments by acquiring data to be converted, extracting knowledge from the data to be converted based on the knowledge extractor, and establishing the knowledge fragments based on the knowledge fragments;
The principle knowledge database is obtained by responding to input operation, acquiring original principle data and establishing the original principle data;
the skill knowledge database is used for obtaining original skill data by responding to the input operation and is built based on the original skill data;
the data processing method comprises the following steps:
Performing task processing on tasks included in a preset service scene by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain task processing results;
And sending the task processing result to a target system.
In an optional implementation manner, the step of performing task processing on tasks included in a preset service scenario by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result includes:
Extracting at least one target principle knowledge data from the principle knowledge database based on tasks included in a preset service scene;
inputting each target principle knowledge data into each workflow included in the fact knowledge database;
executing each workflow after inputting the target principle knowledge data to obtain an execution result;
And taking all the execution results as task processing results.
In a third aspect, the present application provides a knowledge management system comprising:
The data access module is used for acquiring data to be converted;
The information extraction module is used for carrying out knowledge extraction on the data line to be converted based on the knowledge extractor to obtain knowledge fragments, and establishing a fact knowledge database based on the knowledge fragments;
the man-machine interaction module is used for responding to input operation and acquiring original principle data and original skill data;
The knowledge storage module is used for establishing a principle knowledge database based on the original principle data and establishing a skill knowledge database based on the original skill data;
the knowledge reasoning module is used for performing task processing on tasks included in a preset business scene by utilizing the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain task processing results;
and the knowledge service module is used for sending the task processing result to a target system.
In a fourth aspect, the present application provides an electronic device, the electronic device including a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the processor executing the machine-readable instructions to perform the steps of the data processing method according to any of the preceding embodiments; or performing the steps of the data processing method of any of the preceding embodiments.
In a fifth aspect, the present application provides a readable storage medium storing a computer program which when executed implements the steps of the data processing method according to any one of the foregoing embodiments; or a step of implementing the data processing method of any of the preceding embodiments.
The embodiment of the application provides a data processing method, a knowledge management system, electronic equipment and a readable storage medium, and relates to the technical field of knowledge management. Knowledge extraction is performed on the data line to be converted based on the knowledge extractor, knowledge segments are obtained, and a fact knowledge database is built based on the knowledge segments. And responding to the input operation, and acquiring the original principle data and the original skill data. The principle knowledge database is built based on the original principle data, and the skill knowledge database is built based on the original skill data, so that the knowledge management efficiency is improved, and the working efficiency is improved.
In order to make the above objects, features and advantages of the present application more comprehensible, several embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an electronic device according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application.
Fig. 3 is a schematic diagram of a domain category system according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a domain decision map according to an embodiment of the present application.
FIG. 5 is a second flowchart of a data processing method according to an embodiment of the present application.
FIG. 6 is a functional block diagram of a knowledge management system, provided by an embodiment of the application.
Icon: 100-an electronic device; 110-memory; a 120-processor; 130-knowledge management system; 131-a data access module; 132-an information extraction module; 133-a human-computer interaction module; 134-a knowledge storage module; 135-a knowledge reasoning module; 136-a knowledge service module; 140-communication unit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
As described in the background, with the awareness of data awareness in recent years, many enterprises begin to construct data warehouse and data middle platform, and some enterprises already have better data foundation. However, the data itself does not generate knowledge, which is just an impression of the real world in virtual space, and the knowledge is higher than the data, and is more close to the nature of things. After undergoing three stages of informatization, big data age, and data center construction, more and more enterprises begin to realize how much innovation comes mainly from own knowledge assets, whose importance and value exceeds that of data assets and even financial assets. The era of knowledge as an enterprise core asset and core driving force has come, and more enterprise managers begin to pay attention to the operation and management of knowledge. Efficient knowledge management systems are required to be built in enterprises with high knowledge density.
At present, many enterprises still indirectly manage knowledge through staff training, document management and data management, human wisdom is required to extract knowledge from training courseware, documents and data and use the knowledge, and a more efficient knowledge management method is lacked.
In view of this, the data processing method, the knowledge management system, the electronic device and the readable storage medium provided by the embodiments of the present application are capable of efficiently completing knowledge management and knowledge application by extracting data, classifying all the data into a real knowledge database, a principle knowledge database and a skill knowledge database, and performing task processing by using the databases. The above-described scheme is explained in detail below.
The above prior art solutions have all the drawbacks that the applicant has obtained after practice and careful study, and therefore the discovery process of the above problems and the solutions presented in the following embodiments of the present application for the above problems should be all contributions to the present application by the applicant in the process of the present application.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The embodiments described below and the keys in the embodiments may be combined with each other without conflict.
Referring to fig. 1 in combination, fig. 1 is a block diagram illustrating an electronic device 100 according to an embodiment of the application. The device may include a processor 120, a memory 110, a knowledge management system 130, and a communication unit 140, the memory 110 storing machine readable instructions executable by the processor 120, the processor 120 and the memory 110 communicating via a bus when the electronic device 100 is running, the processor 120 executing the machine readable instructions and performing a data processing method.
The memory 110, the processor 120, and the communication unit 140 are electrically connected directly or indirectly to each other to realize signal transmission or interaction.
For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The knowledge management system 130 includes at least one software functional module that may be stored in the memory 110 in the form of software or firmware (firmware). The processor 120 is configured to execute executable modules stored in the memory 110, such as software functional modules or computer programs included in the knowledge management system 130.
The Memory 110 may be, but is not limited to, a random access Memory (Random Accessmemory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 120 may be an integrated circuit chip with signal processing capabilities. The processor 120 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.
But also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In an embodiment of the present application, the memory 110 is configured to store a program, and the processor 120 is configured to execute the program after receiving an execution instruction. The method of defining a flow disclosed in any of the embodiments of the present application may be applied to the processor 120, or implemented by the processor 120.
The communication unit 140 is used for establishing a communication connection between the electronic device 100 and other electronic devices through a network, and for transceiving data through the network.
In some embodiments, the network may be any type of wired or wireless network, or a combination thereof. By way of example only, the network may include a wired network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the internet, a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), a wireless local area network (Wireless Local Area Networks, WLAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network (Wide Area Network, WAN), a public switched telephone network (Public Switched Telephone Network, PSTN), a bluetooth network, a ZigBee network, a near field Communication (NEAR FIELD Communication, NFC) network, or the like, or any combination thereof.
In an embodiment of the present application, the electronic device 100 may be, but is not limited to, a smart phone, a personal computer, a tablet computer, and other devices with processing functions.
It will be appreciated that the structure shown in fig. 1 is merely illustrative. The electronic device 100 may also have more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
The steps of the data processing method provided in the embodiment of the present application are described in detail below based on the block diagram of the electronic device 100 shown in fig. 1. Referring to fig. 2 in combination, fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the application. The data processing method is applied to a knowledge management system, the knowledge management system is pre-stored with a knowledge extractor, and the data processing method comprises the following steps:
step S11, obtaining data to be converted.
Step S12, knowledge extraction is performed on the data line to be converted based on the knowledge extractor, knowledge segments are obtained, and a fact knowledge database is built based on the knowledge segments.
Step S13, original principle data and original skill data are acquired in response to input operation.
Step S14, a principle knowledge database is built based on the original principle data, and a skill knowledge database is built based on the original skill data.
The extracted knowledge segments are factual data, and may include examples, attributes of the examples, relationships between the examples, categories of the examples, and the like. Examples may be persons, animals, plants or other objects, and when examples are persons, the properties of examples may include height, age, gender, contact, etc. The relationships between the instances may include couples, parents, children, brother-in-law nephew, etc. When the example is an animal, the attributes of the example may include body weight, hair color, species, and the like.
For example, instance one may be Zhang three, and its attributes may include height: 175cm, age: age 30, body weight: 75kg, sex: man, contact way: 152xxxx1234, and the like. Example two may be a plum red, and its attributes may include height: 155cm, age: age 30, body weight: 45kg, sex: female, contact means: 152xxxx4321. The relationship between example one, three and example two is couples.
The raw principle data is principle data, and can comprise indexes, rules and models, wherein the indexes can be different in different business scenes, and reference standards are generally provided for the rules. For example, the index may be temperature, liability ratio, bandwidth pressure value, etc. Rules may be defined manually, typically as criteria for evaluating what action is taken in conjunction with an indicator. The model can be used for extracting the characteristics of the original principle data to obtain indexes for reference.
For example, in the credit application scenario of the financial industry. The metrics may include an example liability ratio, credit score, and the like. The model may include a credit scoring model based on which credits of the instances may be scored to obtain a credit score. The rules may include: the credit application is rejected if the equity ratio of the example is greater than 80%. May further include: if the credit score of the instance is below 20 points (100 points full), the credit application is denied. May further include: if the immediate relatives of the instance are in the credit blacklist, the credit application is denied. For example, a three-way financial institution initiated credit application with wife's red on a black credit list would reject the three-way initiated credit application.
The skill knowledge is constructed for completing tasks included in a certain business scenario. Typically a piece of skill knowledge includes a workflow constructed from the indices, rules, and models in principle knowledge. At least one workflow constitutes a minimum task that a business scenario includes, at least one minimum task constitutes a task, and at least one task constitutes the business scenario.
According to the embodiment of the application, the knowledge is extracted from the data, the fact knowledge database is built by using the extracted knowledge segments, the principle knowledge database is built by using the obtained original principle knowledge, and the skill knowledge database is built by using the obtained original skill knowledge, so that the knowledge management efficiency is improved.
As an alternative embodiment, the knowledge decimator comprises a script decimator, and the data to be converted comprises structure class data. Step S12, knowledge extraction is carried out on the data to be converted based on the knowledge extractor, and knowledge segments are obtained through the following steps:
And obtaining a predefined domain category system, wherein the category system comprises categories, category relations and attributes included in the categories.
At least one instance of the structured class data and attributes of each instance are extracted using a script extractor.
Mapping all the examples and the attribute of each example into a domain category system to obtain a knowledge segment.
Referring to fig. 3 in combination, fig. 3 is a schematic diagram of a domain category system according to an embodiment of the present application. The domain category system can be constructed in advance. As shown in fig. 3, the domain category hierarchy may include people, events, places, items, organizations, intangible (intangible property), and the like. Each of the above categories may also include further attributes, for example, for an organization category, the attributes may include company, local merchant, educational institution, government institution, group, creator, organization member, address, date of establishment, etc. The above attributes may be further classified. For example, communities may include performance communities, sports teams, and the like.
The script extractor can be written based on SQL (Structured Query Language), hiveQL and other script query languages. Knowledge extraction is mainly performed for structured and semi-structured databases.
Thus, the embodiment of the application uses the script type extractor to inquire the related instance and the attribute of the instance from the data to be converted. And mapping the examples and the attributes into a predefined domain category system, so that knowledge segments are obtained, and a fact knowledge database is conveniently built based on the knowledge segments.
In another alternative embodiment, the knowledge decimator comprises a pattern type decimator, and the data to be converted comprises unstructured class data. Step S12, knowledge extraction is performed on the data line to be converted based on the knowledge extractor, and knowledge segments can be obtained through the following steps:
And obtaining a predefined domain category system, wherein the category system comprises categories, category relations and attributes included in the categories.
At least one entity included in the unstructured class data is identified using a model extractor.
And classifying all the entities to obtain the category of each entity and the attribute of each entity.
Mapping all the entities and the attribute of each entity into a domain category system to obtain a knowledge segment.
The model extractor may be a model obtained by performing machine learning in advance using a training data set. The data to be converted can be non-structural data such as images, videos, texts, voices and the like.
And extracting data from the unstructured data through a pattern extractor, so as to identify the entity and the attribute of the entity included in the data to be converted. For example, the image recognition model may be used to perform image recognition on the data to be converted, which is of the type of the image, so as to recognize at least one entity included in the image, and the attribute of the entity.
It should be noted that, similar to the above example, the entity may be a person, an animal, a plant, or other objects, and when the example is a person, the attribute of the example may include height, age, sex, contact, and the like. The relationships between the instances may include couples, parents, children, brother-in-law nephew, etc. When the example is an animal, the attributes of the example may include body weight, hair color, species, and the like.
For example, entity one may be Zhang three, and its attributes may include height: 175cm, age: age 30, body weight: 75kg, sex: man, contact way: 152xxxx1234, and the like. Entity two may be a plum red, and its attributes may include height: 155cm, age: age 30, body weight: 45kg, sex: female, contact means: 152xxxx4321. The relationship between the entity one, three and the entity two is couples.
Thus, in the embodiment of the application, knowledge extraction can be performed on various types of data through different types of knowledge extractors, various types of data can be converted into knowledge, and the knowledge conversion efficiency is improved.
In an alternative embodiment, the original skill data includes a domain decision map, and step S3, obtaining the original skill data may be implemented by:
And responding to the input operation to obtain at least one business application scene, wherein each business application scene comprises a plurality of subtasks.
And responding to the decomposition operation, decomposing each subtask to obtain at least one minimum task, wherein each minimum task represents a workflow configuration file.
And constructing and obtaining a domain decision map by utilizing all business application scenes, all subtasks and all minimum tasks according to the inclusion relation, and taking the domain decision map as the original skill data.
For example, as one possible implementation scenario, for an online marketing domain, a schematic diagram of its domain decision map may be shown in fig. 4.
The online marketing field may include a plurality of business application scenarios, such as advertising, e-commerce marketing, channel sales, member marketing, social marketing, private marketing, and the like. The service application scenarios of advertisement delivery can be divided into three categories: before the start of the activity, during the activity and after the end of the activity.
Each business scenario may be divided into at least one subtask, e.g., the subtask may be broken down into platform composition recommendations and media testing before the start of the campaign. The activity in progress can be decomposed into subtasks such as diagnosis, pre-examination and early warning in delivery. After the activity is finished, the sub-tasks such as activity summary, post-release diagnosis and the like can be decomposed.
Each subtask can be decomposed into at least one minimum task, for example, subtask-platform combination recommendations can be decomposed into minimum tasks such as conventional customer platform combination recommendations and customized customer delivery platform combination recommendations. Subtask-media testing can be broken down into minimum tasks such as conventional new media docking testing, new media backhaul DeviceID (device ID) docking testing, media association backend testing, and the like. Subtask-in-delivery diagnostics can be broken down into minimum tasks such as page click diagnostics, page diagnostics, site diagnostics, media frequency diagnostics, media delivery time diagnostics, customized crowd diagnostics, and general crowd diagnostics. Subtask-pre-inspection can be decomposed into conventional activity data pre-inspection, abnormal flow pre-inspection and back-end flow pre-inspection. Subtask-early warning can be decomposed into minimum tasks such as regular pre-detection in the process of moving, medium feedback deviceID early warning in the process of moving, CTR (Click-Through-Rate) early warning in the process of moving on OTV (on line TV), CTR early warning in the process of playing, finish Rate early warning in the process of moving on Display, reach completion Rate early warning, advertisement putting environment early warning and the like.
Subtask-activity summary can be decomposed into minimum tasks such as advertisement putting environment summary, abnormal flow summary, leads completion rate summary, KPI completion rate summary taking Reach as KPI, conventional activity completion rate summary and the like. Subtask-post-delivery diagnosis can be decomposed into minimum tasks such as media (advertisement form) coverage rate diagnosis, back-end leads acquisition channel diagnosis, back-end target crowd accurate customer delivery strength diagnosis, conventional customer delivery strength diagnosis, industry media exposure click diagnosis and the like.
Therefore, for various business scenes in different fields, the method can be used for obtaining the field decision map, so that the technical knowledge database can be generated conveniently. And meanwhile, a global view is provided for staff in the enterprise, so that knowledge sharing and propagation are facilitated.
In an alternative embodiment, in step S14, the knowledge management system pre-stores the format specification configuration file, and the establishment of the principle knowledge database based on the original principle data may be implemented by the following steps:
and carrying out data mining on the original principle data based on the format specification configuration file to obtain principle knowledge data, wherein the principle knowledge data comprises indexes, rules and models.
And establishing an original knowledge database by using the indexes, the rules and the models.
The format specification configuration file may be any one of model specifications PMML (predictive model markup language ), ONNX (open neural network exchange, open Neural Network Exchange), and specification DRL (Drools rule description language) of rules.
Alternatively, the principle knowledge database may also be configured to build the original knowledge database using the index, rule, and model by receiving key elements input by a user (e.g., a knowledge engineer) based on the template, and acquiring principle knowledge data including the index, rule, and model based on the key elements.
In an alternative embodiment, the original skill data includes a domain decision map, step S4, and building a skill knowledge database based on the original skill data may be implemented by:
at least one workflow profile included in the domain decision map is obtained.
And acquiring principle knowledge data corresponding to each workflow configuration file from a principle knowledge database, wherein the principle knowledge data comprises indexes, rules and models.
And configuring each index, rule and model according to each workflow configuration file to obtain at least one workflow.
A skills knowledge database is built based on the entire workflow.
The workflow configuration file is the minimum task shown in fig. 4. For example, in a credit application scenario in the financial industry, a workflow derived based on a workflow profile may be based on first looking up from a facts database whether the applicant or the immediate relatives of the applicant are in a credit blacklist, if neither the applicant nor the immediate relatives of the applicant are in the credit blacklist, performing a fraud-countercheck, after the check passes, performing a rating process on the application credit of the applicant in combination with the personal credit status of the applicant, and finally ending the workflow.
Therefore, based on the skill knowledge database, workflows in different business application scenes can be called, and corresponding workflows can be called from the skill knowledge database directly according to the business scenes in actual application, so that tasks of corresponding business scenes can be completed. The knowledge operation efficiency is improved, and the working efficiency is also improved.
The application provides a data processing method which is applied to a knowledge management system, wherein the knowledge management system is pre-stored with a knowledge extractor and further comprises a fact knowledge database, a principle knowledge database and a skill knowledge database.
The fact knowledge database is obtained by obtaining data to be converted, carrying out knowledge extraction on the data line to be converted based on a knowledge extractor, obtaining knowledge fragments, and establishing based on the knowledge fragments.
The principle knowledge database is obtained by responding to input operation, acquiring original principle data and establishing the original principle data. The skill knowledge database is obtained by responding to input operation, acquiring original skill data and establishing the skill knowledge database based on the original skill data.
Referring to fig. 5 in combination, fig. 5 is a second flowchart of a data processing method according to an embodiment of the application. The method comprises the following steps:
and S21, performing task processing on tasks included in a preset business scene by utilizing the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain task processing results.
Step S22, the task processing result is sent to the target system.
In an alternative embodiment, the step of performing task processing on the task included in the preset service scenario by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result includes:
and extracting at least one target principle knowledge data from a principle knowledge database based on tasks included in a preset service scene.
The target principle knowledge data are input into workflows included in the fact knowledge database.
Executing each workflow after inputting the target principle knowledge data to obtain an execution result;
And taking all the execution results as task processing results.
It will be appreciated that the principle of the above method steps is similar to that of the data processing method shown in fig. 2, and will not be described here again.
Based on the same inventive concept, please refer to fig. 6 in combination, fig. 6 is a functional block diagram of a knowledge management system according to an embodiment of the present application. The embodiment of the application also provides a knowledge management system corresponding to the data processing method shown in fig. 2 and 5, wherein the system comprises:
the data access module 131 is configured to obtain data to be converted.
The information extraction module 132 is configured to extract knowledge of the data line to be converted based on the knowledge extractor, obtain a knowledge segment, and build a fact knowledge database based on the knowledge segment.
The man-machine interaction module 133 is configured to obtain the original principle data and the original skill data in response to the input operation.
The knowledge storage module 134 is configured to build a principle knowledge database based on the raw principle data and build a skill knowledge database based on the raw skill data.
The knowledge reasoning module 135 is configured to perform task processing on tasks included in the preset business scenario by using the principle knowledge database, the skill knowledge database and the fact knowledge database, so as to obtain a task processing result.
The knowledge service module 136 is configured to send the task processing result to the target system.
Because the principle of solving the problem in the embodiment of the present application is similar to the data processing method shown in fig. 2 and 5 in the embodiment of the present application, the implementation principle of the system may refer to the implementation principle of the method, and the repetition is omitted.
The embodiment of the application also provides a readable storage medium, wherein a computer program is stored in the readable storage medium, and the computer program realizes the steps of the data processing method when being executed.
In summary, the embodiments of the present application provide a data processing method, a knowledge management system, an electronic device, and a readable storage medium, by acquiring data to be converted. Knowledge extraction is performed on the data line to be converted based on the knowledge extractor, knowledge segments are obtained, and a fact knowledge database is built based on the knowledge segments. And responding to the input operation, and acquiring the original principle data and the original skill data. The principle knowledge database is built based on the original principle data, and the skill knowledge database is built based on the original skill data, so that the knowledge management efficiency is improved, and the working efficiency is improved.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present application should be included in the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A data processing method, applied to a knowledge management system, the knowledge management system pre-storing a knowledge extractor, the knowledge extractor including a script extractor and a model extractor, the data processing method comprising:
acquiring data to be converted; the data to be converted comprises structural data and non-structural data;
Acquiring a predefined domain category system, wherein the category system comprises categories, category relations and attributes included in the categories;
Extracting at least one instance in the structural class data and the attribute of each instance by using the script extractor; mapping all the examples and the attribute of each example into the domain category system to obtain a knowledge fragment;
Identifying at least one entity included in the unstructured class data using the model extractor; classifying all the entities to obtain the category of each entity and the attribute of each entity; mapping all the entities and the attribute of each entity into the domain category system to obtain a knowledge fragment;
Establishing a fact knowledge database based on the knowledge segments;
Responding to input operation, acquiring original principle data and at least one business application scene, wherein each business application scene comprises a plurality of subtasks, constructing and obtaining a domain decision map according to each subtask, and taking the domain decision map as original skill data;
And establishing a principle knowledge database based on the original principle data, and establishing a skill knowledge database based on the original skill data.
2. The data processing method according to claim 1, wherein the step of constructing a domain decision map based on each of the subtasks, and using the domain decision map as the original skill data comprises:
responding to the decomposition operation, decomposing each subtask to obtain at least one minimum task, wherein each minimum task represents a workflow configuration file;
And constructing a domain decision map by using all the business application scenes, all the subtasks and all the minimum tasks according to the inclusion relation, and taking the domain decision map as original skill data.
3. The data processing method according to claim 1, wherein the knowledge management system pre-stores a format specification profile, and the step of creating a principle knowledge database based on the raw principle data comprises:
based on the format specification configuration file, carrying out data mining on the original principle data to obtain principle knowledge data, wherein the principle knowledge data comprises indexes, rules and models;
and establishing an original knowledge database by using the index, the rule and the model.
4. The data processing method of claim 1, wherein the raw skill data comprises a domain decision map, and wherein the step of building a skill knowledge database based on the raw skill data comprises:
acquiring at least one workflow configuration file included in the domain decision map;
acquiring principle knowledge data corresponding to each workflow configuration file from the principle knowledge database, wherein the principle knowledge data comprises indexes, rules and models;
Configuring each index, each rule and each model according to each workflow configuration file to obtain at least one workflow;
A skills knowledge database is built based on all of the workflows.
5. The data processing method is characterized by being applied to a knowledge management system, wherein the knowledge management system is pre-stored with a knowledge extractor and further comprises a fact knowledge database, a principle knowledge database and a skill knowledge database; the knowledge extractor comprises a script type extractor and a model type extractor;
The fact knowledge database acquires a predefined domain class system by acquiring data to be converted, wherein the class system comprises classes, class relations and attributes contained in the classes, at least one instance and the attributes of each instance in structural class data are extracted by using the script extractor, all the instances and the attributes of each instance are mapped into the domain class system to obtain knowledge fragments, at least one entity contained in unstructured class data is identified by using the model extractor, all the entities are classified to obtain the class of each entity and the attributes of each entity, all the entities and the attributes of each entity are mapped into the domain class system to obtain knowledge fragments, and the knowledge fragments are established based on the knowledge fragments;
The principle knowledge database is obtained by responding to input operation, acquiring original principle data and establishing the original principle data;
The skill knowledge database acquires at least one business application scene by responding to the input operation, each business application scene comprises a plurality of subtasks, a domain decision map is constructed and obtained according to each subtask, the domain decision map is used as original skill data, and the domain decision map is constructed and obtained based on the original skill data;
the data processing method comprises the following steps:
Performing task processing on tasks included in a preset service scene by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain task processing results;
And sending the task processing result to a target system.
6. The data processing method according to claim 5, wherein the step of performing task processing on tasks included in a preset service scenario by using the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain a task processing result includes:
Extracting at least one target principle knowledge data from the principle knowledge database based on tasks included in a preset service scene;
inputting each target principle knowledge data into each workflow included in the fact knowledge database;
executing each workflow after inputting the target principle knowledge data to obtain an execution result;
And taking all the execution results as task processing results.
7. A knowledge management system, wherein the knowledge management system is pre-stored with knowledge
An knowledge extractor, the knowledge extractor comprising a script extractor and a model extractor, comprising:
The data access module is used for acquiring data to be converted; the data to be converted comprises structural data and non-structural data;
The information extraction module is used for obtaining a predefined domain category system, wherein the category system comprises categories, category relations and attributes included in the categories; extracting at least one instance in the structural class data and the attribute of each instance by using the script extractor; mapping all the examples and the attribute of each example into the domain category system to obtain a knowledge fragment; identifying at least one entity included in the unstructured class data using the model extractor; classifying all the entities to obtain the category of each entity and the attribute of each entity; mapping all the entities and the attribute of each entity into the domain category system to obtain a knowledge fragment; establishing a fact knowledge database based on the knowledge segments;
The human-computer interaction module is used for responding to input operation, acquiring original principle data and at least one business application scene, wherein each business application scene comprises a plurality of subtasks, constructing and obtaining a domain decision map according to each subtask, and taking the domain decision map as original skill data;
The knowledge storage module is used for establishing a principle knowledge database based on the original principle data and establishing a skill knowledge database based on the original skill data;
the knowledge reasoning module is used for performing task processing on tasks included in a preset business scene by utilizing the principle knowledge database, the skill knowledge database and the fact knowledge database to obtain task processing results;
and the knowledge service module is used for sending the task processing result to a target system.
8. An electronic device comprising a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is in operation, the processor executing the machine-readable instructions to perform the steps of the data processing method of any of claims 1-4; or performing the steps of the data processing method of any of claims 5-6.
9. A readable storage medium, wherein the readable storage medium stores a computer program which when executed performs the steps of the data processing method of any one of 1 to 4; or steps implementing the data processing method of any of claims 5-6.
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