CN117371532A - Knowledge base management method, system, equipment and medium - Google Patents
Knowledge base management method, system, equipment and medium Download PDFInfo
- Publication number
- CN117371532A CN117371532A CN202311418378.9A CN202311418378A CN117371532A CN 117371532 A CN117371532 A CN 117371532A CN 202311418378 A CN202311418378 A CN 202311418378A CN 117371532 A CN117371532 A CN 117371532A
- Authority
- CN
- China
- Prior art keywords
- knowledge information
- knowledge
- determining
- information
- input
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title abstract description 18
- 238000000034 method Methods 0.000 claims abstract description 39
- 238000012545 processing Methods 0.000 claims description 15
- 238000005516 engineering process Methods 0.000 abstract description 19
- 238000004458 analytical method Methods 0.000 abstract description 7
- 238000000605 extraction Methods 0.000 abstract description 7
- 238000003058 natural language processing Methods 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 15
- 238000010586 diagram Methods 0.000 description 8
- 230000006872 improvement Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 229920001296 polysiloxane Polymers 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 239000010979 ruby Substances 0.000 description 1
- 229910001750 ruby Inorganic materials 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- 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
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/253—Grammatical analysis; Style critique
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- 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
- G06N5/027—Frames
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
Abstract
The application discloses a knowledge base management method, a system, equipment and a medium, wherein the method comprises the following steps: determining knowledge information, and determining an input mode and a storage mode of the knowledge information; the knowledge information is input into a database according to the input mode, and the knowledge information is identified and stored according to the storage mode; and determining a preset timing task, and reading knowledge information of the database according to the timing task. The knowledge information in the knowledge base is learned, arranged and integrated, so that the knowledge information with loose knowledge base is combined and connected. And the knowledge existing in the system is better classified by utilizing knowledge extraction and analysis capabilities, so that the system is more flexible and more accurate compared with the traditional artificial classification. And extracting, representing, integrating and sharing knowledge by using a natural language processing technology, a natural language generating technology, a natural language understanding and reasoning technology of a large language model.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, and a medium for managing a knowledge base.
Background
At present, the knowledge base is useful in a plurality of departments, units and companies, and has a knowledge base which can well accumulate the knowledge of the units, thereby being convenient for new people to inquire and convenient for experience exchange. The traditional knowledge base mostly exists in a mode of sharing documents, network disks and the like, and screening and searching are carried out through file titles or according to file catalogues when knowledge inquiry is carried out. In addition, the knowledge base provides uploading and downloading functions for the owners of the units, so that knowledge classification is confusing, and some workload is brought to system management staff.
Disclosure of Invention
In order to solve the above problems, the present application proposes a knowledge base management method, including: determining knowledge information, and determining an input mode and a storage mode of the knowledge information; inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode; and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
In one example, the knowledge information is input into a database according to the input mode, and the method specifically includes: determining a content format of the knowledge information, determining an input format corresponding to the input mode, and comparing the content format with the input format; if the content format is consistent with the input format, inputting the knowledge information into the database according to the input mode; if the content format is inconsistent with the input format, converting the knowledge information according to the input format, and inputting the converted knowledge information into the database.
In one example, after reading the knowledge information of the database according to the timing task, the method further comprises: pushing the knowledge information to a large language model, and carrying out language processing on the knowledge information through the large language model so as to determine a knowledge graph according to the knowledge information after language processing.
In one example, reading the knowledge information of the database specifically includes: determining a reading format, and inquiring the database according to the reading format to obtain knowledge information corresponding to the reading format.
In one example, the knowledge information includes, but is not limited to, knowledge titles, knowledge content, content formats.
In one example, the entry means includes, but is not limited to, file entry, text recognition entry, manual entry.
In another aspect, the present application further provides a knowledge base management system, including: the input module is used for inputting knowledge information into the system according to a preset input mode; the storage module is used for acquiring the knowledge information from the input module and storing the knowledge information according to a preset storage mode; and the output module is used for extracting the knowledge information according to a preset timing task and outputting the extracted knowledge information.
In one example, further comprising: and the large language model module is used for acquiring the knowledge information from the output module, and carrying out language processing on the knowledge information so as to determine a knowledge graph according to the knowledge information after the language processing.
On the other hand, the application also provides knowledge base management equipment, which comprises: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the one knowledge base management device to perform: determining knowledge information, and determining an input mode and a storage mode of the knowledge information; inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode; and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
In another aspect, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions configured to: determining knowledge information, and determining an input mode and a storage mode of the knowledge information; inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode; and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
The knowledge information in the knowledge base is learned, arranged and integrated, so that the knowledge information with loose knowledge base is combined and connected. And the knowledge existing in the system is better classified by utilizing knowledge extraction and analysis capabilities, so that the system is more flexible and more accurate compared with the traditional artificial classification. And extracting, representing, integrating and sharing knowledge by using a natural language processing technology, a natural language generating technology, a natural language understanding and reasoning technology of a large language model.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flowchart of a knowledge base management method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a knowledge base management system according to an embodiment of the present application;
fig. 3 is a schematic diagram of a knowledge base management device in an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
In order to solve the above problems, as shown in fig. 1, the knowledge base management method provided in the embodiments of the present application is applied in a knowledge base management system, as shown in fig. 2, where the management system includes an input module, a storage module, an output module, and a large language model module. The method comprises the following steps:
s101, determining knowledge information, and determining an input mode and a storage mode of the knowledge information.
The system inputs related knowledge information, including knowledge titles and knowledge contents, wherein the knowledge titles can be file names, the knowledge contents comprise text pictures and also can be documents, or the knowledge contents can be directly input on a knowledge input page provided by the system. The knowledge content may be in the form of text information, a document such as doc, txt, pdf, a picture containing text, or voice. The system extracts the text content to be input into the system from the pictures containing the text through the text recognition technology. Or extracting knowledge information in the voice through voice recognition technology.
S102, inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode.
The input mode comprises file uploading, character recognition and manual input. The system determines a knowledge storage mode according to the input mode of knowledge, and stores knowledge information into the system. And simultaneously setting the knowledge information to be in a state to be learned. For example, if text recognition is selected and a picture is uploaded, the picture is stored in the object store and the recognized file is stored in the database of the knowledge base. The knowledge information uploaded in the form of files is subjected to object storage, such as uploaded files, pictures and voices, and basic information of the knowledge is stored in a database, so that information extraction and downloading are facilitated.
S103, determining a preset timing task, and reading the knowledge information of the database according to the timing task.
The system reads knowledge information to be learned in the system at fixed time through preset fixed-time tasks, and pushes the knowledge information to a large language model module for information processing and machine learning. Large Language Models (LLMs) refer to deep learning models trained using large amounts of text data that can generate natural language text or understand the meaning of language text. The large language model may handle a variety of natural language tasks such as text classification, questions and answers, conversations, and the like. Large language models are one of the applications of deep learning, especially in the field of natural language processing. The goal of these models is to understand and generate human language. Such as ChatGPT, is an example of a large language model. Is trained to understand and generate human language for effective dialogue and solution of various questions.
The system large language model module is used for identifying entities in knowledge information, including person names, place names, organizations, time and the like through a named entity technology NER; and extracting information of the knowledge information by using a text analysis technology, wherein the information comprises topics, key information and the like. The knowledge information is extracted, analyzed, classified and arranged by utilizing the knowledge integration capability possessed by the large language model and natural language processing technologies such as grammar analysis technology, text analysis technology, information extraction technology and the like, and the learning of related knowledge is completed and integrated into a knowledge base and a knowledge map of the large language model. Every time knowledge is input, the input text information is identified from the picture through the accessed third party OCR, the information read from the document is used as training data of the model, and a sentence or a section of speech is analyzed through a decoder in the model, for example: for the sentence "I want to become a scientist", the model can understand that this is a sentence representing the subject "I", the predicate "want", the object "become", and the object "scientist", and associate these words to construct a corresponding grammar tree.
In a knowledge output module provided by the system, knowledge in different formats such as characters, pictures, pdf files and the like is obtained through file downloading, file inquiry and knowledge inquiry modes. Traditional knowledge information downloading and searching can be achieved based on query knowledge basic information; knowledge information is acquired in a dialogue form, learning and arrangement of knowledge are dependent on a large language model, dialogue communication is completed based on natural language learning capability provided by the large language model, natural language composed of related knowledge is generated according to dialogue content, and the natural language is output.
As shown in fig. 2, an embodiment of the present application further provides a knowledge base management system, including:
the input module is used for providing a plurality of modes such as file uploading, character recognition, manual input and the like for inputting knowledge and receiving various types of knowledge information, including modes such as files, shared documents or online documents and the like, and providing a function entry for inputting the knowledge information. Inputting knowledge information to be shared and stored into a system;
the storage module is used for providing a plurality of data storage modes of object storage, file storage and relational database and storing knowledge information in different formats recorded by the input module. Data storage services are also provided for large language model modules.
And the output module is used for providing file downloading, file online previewing, file inquiring and knowledge inquiring related function entrance. The method is used for providing traditional file downloading and online document sharing functions, and simultaneously providing a dialogue form to acquire knowledge which is processed and analyzed by a large language model.
And the large language model module supports a plurality of large language models and can also carry out localization deployment based on the open source large language model. The system comprises functions of semantic analysis, text analysis, information extraction and the like, provides knowledge information extraction, knowledge information understanding, knowledge extraction and knowledge output functions, and is a core module of the whole management system. The method is used for carrying out technical processing on the input knowledge, extracting and integrating the knowledge content, and outputting the knowledge through a natural language generation technology. And carrying out language processing on the knowledge information to determine a knowledge graph according to the knowledge information after language processing.
As shown in fig. 3, the embodiment of the present application further provides a knowledge base management device, including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable a knowledge base management apparatus to perform:
determining knowledge information, and determining an input mode and a storage mode of the knowledge information;
inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode;
and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
The embodiments of the present application also provide a nonvolatile computer storage medium storing computer executable instructions configured to:
determining knowledge information, and determining an input mode and a storage mode of the knowledge information;
inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode;
and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable Gate Array, FPGA)) is an integrated circuit whose logic function is determined by the programming of the device by a user. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented by using "logic compiler" software, which is similar to the software compiler used in program development and writing, and the original code before the compiling is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but not just one of the hdds, but a plurality of kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), lava, lola, myHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic controllers, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for the apparatus and medium embodiments, the description is relatively simple, as it is substantially similar to the method embodiments, with reference to the section of the method embodiments being relevant.
The devices and media provided in the embodiments of the present application are in one-to-one correspondence with the methods, so that the devices and media also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media are not described in detail herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application 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 the like) having computer-usable program code embodied therein.
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 application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
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 storage media for a computer 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, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method of knowledge base management, comprising:
determining knowledge information, and determining an input mode and a storage mode of the knowledge information;
inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode;
and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
2. The method according to claim 1, characterized in that the knowledge information is entered into a database according to the way of entry, in particular comprising:
determining a content format of the knowledge information, determining an input format corresponding to the input mode, and comparing the content format with the input format;
if the content format is consistent with the input format, inputting the knowledge information into the database according to the input mode;
if the content format is inconsistent with the input format, converting the knowledge information according to the input format, and inputting the converted knowledge information into the database.
3. The method of claim 1, wherein after reading the knowledge information of the database according to the timing task, the method further comprises:
pushing the knowledge information to a large language model, and carrying out language processing on the knowledge information through the large language model so as to determine a knowledge graph according to the knowledge information after language processing.
4. Method according to claim 1, characterized in that reading the knowledge information of the database, in particular comprises:
determining a reading format, and inquiring the database according to the reading format to obtain knowledge information corresponding to the reading format.
5. The method of claim 1, wherein the knowledge information includes, but is not limited to, knowledge titles, knowledge contents, content formats.
6. The method of claim 1, wherein the means of entry includes, but is not limited to, file entry, text recognition entry, manual entry.
7. A knowledge base management system, comprising:
the input module is used for inputting knowledge information into the system according to a preset input mode;
the storage module is used for acquiring the knowledge information from the input module and storing the knowledge information according to a preset storage mode;
and the output module is used for extracting the knowledge information according to a preset timing task and outputting the extracted knowledge information.
8. The system of claim 7, further comprising:
and the large language model module is used for acquiring the knowledge information from the output module, and carrying out language processing on the knowledge information so as to determine a knowledge graph according to the knowledge information after the language processing.
9. A knowledge base management apparatus, comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the knowledge base management apparatus to perform:
determining knowledge information, and determining an input mode and a storage mode of the knowledge information;
inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode;
and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
10. A non-transitory computer storage medium storing computer-executable instructions, the computer-executable instructions configured to:
determining knowledge information, and determining an input mode and a storage mode of the knowledge information;
inputting the knowledge information into a database according to the input mode, and identifying and storing the knowledge information according to the storage mode;
and determining a preset timing task, and reading the knowledge information of the database according to the timing task.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311418378.9A CN117371532A (en) | 2023-10-27 | 2023-10-27 | Knowledge base management method, system, equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311418378.9A CN117371532A (en) | 2023-10-27 | 2023-10-27 | Knowledge base management method, system, equipment and medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117371532A true CN117371532A (en) | 2024-01-09 |
Family
ID=89407506
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311418378.9A Pending CN117371532A (en) | 2023-10-27 | 2023-10-27 | Knowledge base management method, system, equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117371532A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117573852A (en) * | 2024-01-17 | 2024-02-20 | 深圳市伊登软件有限公司 | Task processing method, device, equipment and medium for intelligent office |
-
2023
- 2023-10-27 CN CN202311418378.9A patent/CN117371532A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117573852A (en) * | 2024-01-17 | 2024-02-20 | 深圳市伊登软件有限公司 | Task processing method, device, equipment and medium for intelligent office |
| CN117573852B (en) * | 2024-01-17 | 2024-03-22 | 深圳市伊登软件有限公司 | Task processing method, device, equipment and medium for intelligent office |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115952272B (en) | Method, device and equipment for generating dialogue information and readable storage medium | |
| CN110263158B (en) | Data processing method, device and equipment | |
| CN113221555B (en) | Keyword recognition method, device and equipment based on multitasking model | |
| CN117371532A (en) | Knowledge base management method, system, equipment and medium | |
| CN111695345A (en) | Method and device for recognizing entity in text | |
| CN113887227B (en) | Model training and entity identification method and device | |
| CN112417093B (en) | Model training method and device | |
| CN108446266B (en) | Statement splitting method, device and equipment | |
| CN112199951A (en) | Event information generation method and device | |
| CN116628198A (en) | Training method and device of text generation model, medium and electronic equipment | |
| CN117332282B (en) | Knowledge graph-based event matching method and device | |
| CN115617973B (en) | Information acquisition method based on intelligent data processing | |
| CN115756449B (en) | Page multiplexing method and device, storage medium and electronic equipment | |
| CN113887234B (en) | Model training and recommending method and device | |
| de Campos et al. | An integrated system for managing the andalusian parliament's digital library | |
| CN117591661B (en) | Question-answer data construction method and device based on large language model | |
| CN117390293B (en) | Information recommendation method, device, medium and equipment for dispute cases | |
| CN117271611B (en) | Information retrieval method, device and equipment based on large model | |
| CN115017915B (en) | Model training and task execution method and device | |
| CN116501852B (en) | Controllable dialogue model training method and device, storage medium and electronic equipment | |
| CN117787418A (en) | Risk identification method and device, storage medium and electronic equipment | |
| CN117827173A (en) | Title generation model training method and device | |
| CN118035830A (en) | Credential information identification model generation and credential information identification method, device and equipment | |
| CN117593003A (en) | Model training method and device, storage medium and electronic equipment | |
| CN117931672A (en) | Query processing method and device applied to code change |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |