CN118014061A - Multi-system knowledge entity management method, device and nonvolatile storage medium - Google Patents

Multi-system knowledge entity management method, device and nonvolatile storage medium Download PDF

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CN118014061A
CN118014061A CN202410026588.1A CN202410026588A CN118014061A CN 118014061 A CN118014061 A CN 118014061A CN 202410026588 A CN202410026588 A CN 202410026588A CN 118014061 A CN118014061 A CN 118014061A
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knowledge entity
knowledge
target
browser
selection mode
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李岩
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Tianyi Telecom Terminals Co Ltd
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Tianyi Telecom Terminals Co Ltd
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

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Abstract

The application discloses a multi-system knowledge entity management method, a device and a nonvolatile storage medium. Wherein the method comprises the following steps: receiving a link request sent by a browser, wherein the link request carries target identification information of a target system accessed by the browser; determining a target knowledge entity selection mode corresponding to the target identification information; determining a target knowledge entity corresponding to the knowledge entity selection mode in a knowledge entity base according to the target knowledge entity selection mode; and sending the target knowledge entity to a browser, wherein the browser is used for displaying the target knowledge entity. The application solves the technical problems of excessive occupied storage space and high operation and maintenance cost caused by adopting each system to independently maintain a corresponding knowledge entity base in the related technology.

Description

Multi-system knowledge entity management method, device and nonvolatile storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for managing multiple system knowledge entities, and a nonvolatile storage medium.
Background
In the related art, different websites or systems typically set up and maintain corresponding knowledge entity libraries separately to manage all the knowledge entities involved in the system. However, knowledge entities among different systems often have partial overlap, so that the overlapped knowledge entities are repeatedly stored, and occupy a large amount of storage space. In addition, the operation and maintenance cost is high, the data distribution is scattered, and the centralized application cannot be realized.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides a multi-system knowledge entity management method, a multi-system knowledge entity management device and a non-volatile storage medium, which at least solve the technical problems of excessive occupied storage space and high operation and maintenance cost caused by adopting each system to independently maintain a corresponding knowledge entity base in the related technology.
According to an aspect of an embodiment of the present application, there is provided a multi-system knowledge entity management method, including: receiving a link request sent by a browser, wherein the link request carries target identification information of a target system accessed by the browser; determining a target knowledge entity selection mode corresponding to the target identification information; determining a target knowledge entity corresponding to the knowledge entity selection mode in a knowledge entity base according to the target knowledge entity selection mode; and sending the target knowledge entity to a browser, wherein the browser is used for displaying the target knowledge entity.
Optionally, determining the selection manner of the knowledge entity corresponding to the system identification information includes: determining a knowledge entity selection mode database, wherein the knowledge entity selection mode database comprises identification information of each system and knowledge entity selection modes corresponding to the identification information; and searching a target knowledge entity selection mode in the knowledge entity selection mode database according to the target identification information.
Optionally, the knowledge entity selection manner of each system in the knowledge entity selection manner database is determined by: acquiring historical browsing data of each system; according to historical browsing data of each system, determining attention degree parameters and association degree parameters of knowledge entities in a knowledge entity base corresponding to each system, wherein the attention degree parameters are used for reflecting the frequency of retrieving the knowledge entities by each system, and the association degree parameters are used for reflecting the semantic association degree among different knowledge entities or the frequency of simultaneously retrieving the knowledge entities; and determining a knowledge entity selection mode of each system according to the attention degree parameters and the association degree parameters of the knowledge entities in the knowledge entity library corresponding to each system.
Optionally, the step of determining the selection mode of the knowledge entity of each system according to the attention degree parameter and the association degree parameter of the knowledge entity in the knowledge entity library corresponding to each system includes: determining a first type of knowledge entity according to the attention degree parameter, wherein the first type of knowledge entity is a knowledge entity of which the corresponding attention degree parameter is larger than a preset attention degree parameter threshold; determining a second type of knowledge entity according to the association degree parameter, wherein the association degree parameter is a knowledge entity with the association degree parameter larger than a preset association degree parameter threshold value with the first type of knowledge entity; and determining the first type of knowledge entity and the second type of knowledge entity as target knowledge entities to be selected, thereby obtaining a target knowledge entity selection mode.
Optionally, the step of sending the target knowledge entity to the browser comprises: sending a request path corresponding to the target knowledge entity to a browser; and sending domain name information corresponding to the target knowledge entity to the browser.
Optionally, after the step of determining the target knowledge entity selection mode corresponding to the target identification information, the multi-system knowledge entity management method further includes: acquiring user identity information sent by a browser; determining a historical access record of the user identity information according to the user identity information; and updating the selection mode of the target knowledge entity according to the history access record.
Optionally, the step of updating the selection mode of the target knowledge entity according to the history access record includes: constructing a user portrait according to the history access record; determining a search formula set corresponding to the user image, wherein the search formula set is used for searching a knowledge entity corresponding to the user image in a knowledge entity library; updating the selection mode of the target knowledge entity according to the search set.
According to another aspect of the embodiment of the present application, there is also provided a multi-system knowledge entity management apparatus, including: the first processing module is used for receiving a link request sent by the browser, wherein the link request carries target identification information of a target system accessed by the browser; the second processing module is used for determining a target knowledge entity selection mode corresponding to the target identification information; the third processing module is used for determining a target knowledge entity corresponding to the knowledge entity selection mode in the knowledge entity base according to the target knowledge entity selection mode; and the fourth processing module is used for sending the target knowledge entity to the browser, wherein the browser is used for displaying the target knowledge entity.
According to another aspect of the embodiment of the present application, there is further provided a nonvolatile storage medium, in which a program is stored, wherein when the program runs, a device in which the nonvolatile storage medium is controlled to execute the multi-system knowledge entity management method.
According to another aspect of the embodiment of the present application, there is also provided an electronic device, including: the system comprises a memory and a processor, wherein the processor is used for running a program stored in the memory, and the program runs to execute a multi-system knowledge entity management method.
In the embodiment of the application, a link request sent by a browser is received, wherein the link request carries target identification information of a target system accessed by the browser; determining a target knowledge entity selection mode corresponding to the target identification information; determining a target knowledge entity corresponding to the knowledge entity selection mode in a knowledge entity base according to the target knowledge entity selection mode; the method comprises the steps of sending target knowledge entities to a browser, wherein the browser is used for displaying the target knowledge entities, a unified knowledge entity base is established for a plurality of target systems, and a target knowledge entity selection mode of each target system is determined to select the target knowledge entity corresponding to the target system from a knowledge test question base, so that the aim of avoiding repeated storage of the same knowledge entity is fulfilled, the technical effects of reducing occupied storage space and improving operation and maintenance efficiency are achieved, and the technical problems of excessive occupied storage space and high operation and maintenance cost caused by the fact that each system is adopted to independently maintain a corresponding knowledge entity base in the related art are solved.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a method for managing multiple system knowledge entities, according to an embodiment of the application;
FIG. 2 is a schematic diagram of a browsing interface provided according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating an interaction flow between a browser, a target system and a knowledge base system according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a multi-system knowledge entity management apparatus according to an embodiment of the application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to better understand the embodiments of the present application, technical terms related to the embodiments of the present application are explained as follows:
TADW model (Tri-Modal Autoencoder WITH DEEP WALK): the TADW model is a model for multimodal data (text, images, and attributes) embedded learning. The model can map the multi-modal data to the representation in the low-dimensional space by combining an automatic encoder and a Deep Walk algorithm, and can help us to perform similarity calculation and cluster analysis of the multi-modal data in the low-dimensional space, so that the model is better applied to the feature mining task of the multi-modal data.
With the rapid development of the internet, any website or application system needs a knowledge base management system as a support for propaganda popularization, information retrieval and content release. Knowledge base systems have become an important component of a web site or application system. The method can enable users to easily find information needed by the users and guide the users to better operate and use the website or the application system. However, in the related art, different websites or application systems are required to build different knowledge base management, and the same or similar system functions are regenerated, so that repeated construction of the system is caused, and great labor cost and time are wasted. And secondly, as the systems are distributed and independently deployed, the cost on operation and maintenance is increased, the operation and maintenance work is complicated and the efficiency is low, and the risk of the system is increased. Moreover, due to loose management of data information, the data information cannot be reused, so that the data resource is wasted.
On the other hand, the attention degree of different system users to different knowledge systems and knowledge points in the knowledge base is different, and the demand degree of different knowledge is different, so that the knowledge base system is reconstructed to realize personalized service of knowledge base demand description while the common knowledge base platform is shared and used by multiple system users, so that the different system users can dynamically obtain the attention indication content of the users, and the importance of providing better knowledge service for the users is also important.
In order to solve the above problems, related solutions are provided in the embodiments of the present application, and are described in detail below.
According to an embodiment of the present application, there is provided a method embodiment of a multi-system knowledge entity management method, it should be noted that the steps illustrated in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that illustrated herein.
The method embodiments provided by the embodiments of the present application may be performed in a mobile terminal, a computer terminal, or similar computing device. Specifically, the embodiment of the application provides a multi-system knowledge entity management method which is suitable for a knowledge base system. As shown in fig. 1, the method comprises the steps of:
step S102, receiving a link request sent by a browser, wherein the link request carries target identification information of a target system accessed by the browser;
step S104, determining a target knowledge entity selection mode corresponding to the target identification information;
In the technical solution provided in step S104, determining a manner of selecting a knowledge entity corresponding to the system identification information includes: determining a knowledge entity selection mode database, wherein the knowledge entity selection mode database comprises identification information of each system and knowledge entity selection modes corresponding to the identification information; and searching a target knowledge entity selection mode in the knowledge entity selection mode database according to the target identification information.
In some embodiments of the present application, the knowledge entity selection manner of each system in the knowledge entity selection manner database is determined by: acquiring historical browsing data of each system; according to historical browsing data of each system, determining attention degree parameters and association degree parameters of knowledge entities in a knowledge entity base corresponding to each system, wherein the attention degree parameters are used for reflecting the frequency of retrieving the knowledge entities by each system, and the association degree parameters are used for reflecting the semantic association degree among different knowledge entities or the frequency of simultaneously retrieving the knowledge entities; and determining a knowledge entity selection mode of each system according to the attention degree parameters and the association degree parameters of the knowledge entities in the knowledge entity library corresponding to each system.
Specifically, taking the a system as an example, the historical browsing data of the a system includes historical behavior data of a user logging in the a system. Historical behavior data of the A system user, such as the text content of a knowledge base searched and browsed by the A system user, personal information of the A system user and the like, can be recorded, then the TADW model is utilized to summarize, refine and associate the historical behavior data, a series of knowledge information focused by the A system user is obtained, and a knowledge entity corresponding to the knowledge information is determined in a shared knowledge base platform.
And then determining a degree of attention parameter corresponding to each knowledge entity and a correlation degree parameter between the knowledge entities, wherein the degree of attention parameter is used for representing the interest degree of the A-system user on the knowledge entities. And updating the knowledge system corresponding to the A system in the reconstructed shared knowledge platform according to the attention degree parameter and the association degree parameter.
The attention degree parameter of the knowledge entity is determined by the accessed frequency and the access time of the knowledge entity, and the specific formula is as follows:
A”＝F*A'+l
Wherein A 'is an updated attention degree parameter, A' is an initial attention degree parameter, l represents the access frequency of the knowledge entity by an A system user, F is a time influence factor, c is the current time, r is the time corresponding to the last access of the knowledge entity by the A system user, time (c, r) is the time difference between the current time and the last access time, hlp is a half attenuation factor, namely, after hlp time, the attention degree of the knowledge entity is attenuated by half, and the attenuation speed is fast and slow. The value hlp can be determined empirically or by itself according to the actual situation.
The degree of association parameter between the knowledge entities may be determined based on the semantic association of semantic features between two knowledge entities or the probability that two knowledge entities are simultaneously accessed by the a-system user.
In some embodiments of the present application, the step of determining the selection manner of the knowledge entity of each system according to the attention degree parameter and the association degree parameter of the knowledge entity in the knowledge entity library corresponding to each system includes: determining a first type of knowledge entity according to the attention degree parameter, wherein the first type of knowledge entity is a knowledge entity of which the corresponding attention degree parameter is larger than a preset attention degree parameter threshold; determining a second type of knowledge entity according to the association degree parameter, wherein the association degree parameter is a knowledge entity with the association degree parameter larger than a preset association degree parameter threshold value with the first type of knowledge entity; and determining the first type of knowledge entity and the second type of knowledge entity as target knowledge entities to be selected, thereby obtaining a target knowledge entity selection mode.
Step S106, determining a target knowledge entity corresponding to the knowledge entity selection mode in the knowledge entity base according to the target knowledge entity selection mode;
In the technical solution provided in step S106, after determining the target knowledge entity selection mode corresponding to the target identification information, the multi-system knowledge entity management method further includes: acquiring user identity information sent by a browser; determining a historical access record of the user identity information according to the user identity information; and updating the selection mode of the target knowledge entity according to the history access record.
In some embodiments of the present application, the step of updating the selection mode of the target knowledge entity according to the history access record includes: constructing a user portrait according to the history access record; determining a search formula set corresponding to the user image, wherein the search formula set is used for searching a knowledge entity corresponding to the user image in a knowledge entity library; updating the selection mode of the target knowledge entity according to the search set.
Specifically, after the user of the a system opens a new round of access behaviors, the knowledge base content information in the knowledge system corresponding to the a system in the shared knowledge base platform is further dynamically acquired in a targeted manner according to the personal behaviors of the current login user of the a system, unlike the step of dynamically updating and reconstructing the related knowledge base content information of different systems according to the historical behavior characteristics of all users of the a system in the fourth step, the step is a further personalized recommendation scheme according to the step of dynamically adjusting and acquiring the knowledge base content returned to the browser. The method comprises the following specific steps:
Firstly, acquiring target search data appointed by a current login user of the A system during the previous round of search and the current round of search, carrying out semantic division on the target search data by adopting a TF-IDF algorithm technology, and extracting key search words; and actively sensing the related information of the current login user of the A system, and constructing user portrait model information by reading and analyzing parameters such as historical key search words, personal information and the like of the current login user of the A system.
And secondly, performing similar fuzzy replacement processing on key characters in the key search words by utilizing a target search word bank of a personalized knowledge system corresponding to the A system in the shared knowledge base platform, and forming a search recommendation search set by utilizing each word subjected to the similar fuzzy replacement processing, wherein the set comprises a plurality of permutation and combination modes of each word, which are formed by combining Boolean operation characters.
Thirdly, calculating the similarity degree between each knowledge content in the personalized knowledge system corresponding to the A system in the shared knowledge base platform and the content in the search recommendation search type set, wherein the similarity degree is specifically shown in the following formula:
Calculating the matching degree and the correlation degree of the n search recommendation retrievals and the j-th knowledge content in the personalized knowledge system corresponding to the A system, and returning the pre-set knowledge content with strong correlation to the browser;
And fourthly, when the pre-set knowledge content with stronger correlation is returned, the system also judges the access state and the access trend of the user by utilizing technologies such as pattern recognition and the like according to the user portrait model information of the current login user of the A system, for example, the current requirements and the current interest points of the user are judged. Judging the interested degree of each item of content which has an association relation with the knowledge content returned to the browser in the personalized knowledge system corresponding to the A system in the shared knowledge base platform, and returning the related content with higher interested degree to the front-end browser interface for display.
Step S108, the target knowledge entity is sent to a browser, wherein the browser is used for displaying the target knowledge entity.
In the technical solution provided in step S108, the step of sending the target knowledge entity to the browser includes: sending a request path corresponding to the target knowledge entity to a browser; and sending domain name information corresponding to the target knowledge entity to the browser.
It should be noted that both the a system and the B system may request the same knowledge base address (e.g., xxx. Hellp. Com), and the operation request does not require any parameters. The final displayed content is knowledge content of each system (A system or B system) and dynamic data related to the user, and the returned domain name request address is unchanged.
In some embodiments of the present application, the request path includes address information corresponding to different systems, which can be used for sharing pages between users. Under the condition that only domain name information corresponding to a target knowledge entity is sent to a browser, as different external systems request the same domain name address to display different content information, according to a user request, a knowledge base system writes a user request source into a cookie, and the cookie is kept for a quite long time, at the moment, if a user logs in through other systems, the request source is refreshed to the cookie, and the display content under the current domain name always displays the knowledge base system related to the user last time.
As an alternative embodiment, a browser interface that presents the obtained target knowledge entity to the user is shown in FIG. 2. After the user obtains the target knowledge entity through the browser, the user can click on a menu item to request related content from the knowledge base. The browser can acquire the related content information and locally refresh the page, and at the moment, the menu information is not updated, but only the content display area is updated, and the url address information of the browser address bar is updated at the same time.
In addition, the user can also initiate a data request to the knowledge base through the url address of the browser address bar. And then the knowledge base acquires a system (A system) associated with the specified content according to the acquired request information.
The embodiment of the application also provides an interaction flow diagram among the browser, the system and the knowledge base system shown in the figure 3. As can be seen from fig. 3, after a user accesses the a system through the browser, a connection request carrying a request for identifying the source of the request as the a system may be sent to the knowledge base system, and then the knowledge base system may acquire content data such as a corresponding target knowledge entity according to the source of the request and return the content data to the browser in the form of a menu list. When the browser determines that it wishes to browse some of the content, the knowledge base system returns specific information for that content. In addition, the user can also directly acquire the related information of the A system by inputting the url information of the request address bar in the browser.
Receiving a link request sent by a browser, wherein the link request carries target identification information of a target system accessed by the browser; determining a target knowledge entity selection mode corresponding to the target identification information; determining a target knowledge entity corresponding to the knowledge entity selection mode in a knowledge entity base according to the target knowledge entity selection mode; the method comprises the steps of sending target knowledge entities to a browser, wherein the browser is used for displaying the target knowledge entities, a unified knowledge entity base is established for a plurality of target systems, and a target knowledge entity selection mode of each target system is determined to select the target knowledge entity corresponding to the target system from a knowledge test question base, so that the aim of avoiding repeated storage of the same knowledge entity is fulfilled, the technical effects of reducing occupied storage space and improving operation and maintenance efficiency are achieved, and the technical problems of too much occupied storage space and high operation and maintenance cost caused by adopting each system to independently maintain a corresponding knowledge entity base in the related art are solved.
The embodiment of the application provides a multi-system knowledge entity management device. Fig. 4 is a schematic structural view of the apparatus, as shown in fig. 4, including: the first processing module 40 is configured to receive a link request sent by the browser, where the link request carries target identification information of a target system accessed by the browser; a second processing module 42, configured to determine a target knowledge entity selection manner corresponding to the target identification information; a third processing module 44, configured to determine, in the knowledge entity base, a target knowledge entity corresponding to the knowledge entity selection manner according to the target knowledge entity selection manner; a fourth processing module 46 is configured to send the target knowledge entity to a browser, where the browser is configured to display the target knowledge entity.
In some embodiments of the present application, the determining, by the second processing module 42, the manner in which the knowledge entity corresponding to the system identification information is selected includes: determining a knowledge entity selection mode database, wherein the knowledge entity selection mode database comprises identification information of each system and knowledge entity selection modes corresponding to the identification information; and searching a target knowledge entity selection mode in the knowledge entity selection mode database according to the target identification information.
In some embodiments of the present application, the knowledge entity selection manner of each system in the knowledge entity selection manner database is determined by: acquiring historical browsing data of each system; according to historical browsing data of each system, determining attention degree parameters and association degree parameters of knowledge entities in a knowledge entity base corresponding to each system, wherein the attention degree parameters are used for reflecting the frequency of retrieving the knowledge entities by each system, and the association degree parameters are used for reflecting the semantic association degree among different knowledge entities or the frequency of simultaneously retrieving the knowledge entities; and determining a knowledge entity selection mode of each system according to the attention degree parameters and the association degree parameters of the knowledge entities in the knowledge entity library corresponding to each system.
In some embodiments of the present application, the step of determining, by the second processing module 42, a selection manner of a knowledge entity of each system according to the attention degree parameter and the association degree parameter of the knowledge entity in the knowledge entity library corresponding to each system includes: determining a first type of knowledge entity according to the attention degree parameter, wherein the first type of knowledge entity is a knowledge entity of which the corresponding attention degree parameter is larger than a preset attention degree parameter threshold; determining a second type of knowledge entity according to the association degree parameter, wherein the association degree parameter is a knowledge entity with the association degree parameter larger than a preset association degree parameter threshold value with the first type of knowledge entity; and determining the first type of knowledge entity and the second type of knowledge entity as target knowledge entities to be selected, thereby obtaining a target knowledge entity selection mode.
In some embodiments of the present application, after the step of determining the target knowledge entity selection mode corresponding to the target identification information, the multi-system knowledge entity management apparatus is further configured to: acquiring user identity information sent by a browser; determining a historical access record of the user identity information according to the user identity information; and updating the selection mode of the target knowledge entity according to the history access record.
In some embodiments of the present application, the step of updating the selection manner of the target knowledge entity by the third processing module 44 according to the history access record includes: constructing a user portrait according to the history access record; determining a search formula set corresponding to the user image, wherein the search formula set is used for searching a knowledge entity corresponding to the user image in a knowledge entity library; updating the selection mode of the target knowledge entity according to the search set.
In some embodiments of the present application, the step of the fourth processing module 46 sending the target knowledge entity to the browser includes: sending a request path corresponding to the target knowledge entity to a browser; and sending domain name information corresponding to the target knowledge entity to the browser.
Note that each module in the multi-system knowledge entity management apparatus may be a program module (for example, a set of program instructions for implementing a specific function), or may be a hardware module, and for the latter, it may be represented by the following form, but is not limited thereto: the expression forms of the modules are all a processor, or the functions of the modules are realized by one processor.
According to an embodiment of the present application, there is provided a nonvolatile storage medium. The nonvolatile storage medium stores a program, wherein the device where the nonvolatile storage medium is controlled to execute the following multi-system knowledge entity management method when the program runs: receiving a link request sent by a browser, wherein the link request carries target identification information of a target system accessed by the browser; determining a target knowledge entity selection mode corresponding to the target identification information; determining a target knowledge entity corresponding to the knowledge entity selection mode in a knowledge entity base according to the target knowledge entity selection mode; and sending the target knowledge entity to a browser, wherein the browser is used for displaying the target knowledge entity.
Fig. 5 shows a block diagram of a hardware architecture of a computer terminal (or mobile device) for implementing a multi-system knowledge entity management method. As shown in fig. 5, the computer terminal 50 (or mobile device 50) may include one or more processors 502 (shown in the figures as 502a, 502b, … …,502 n) (the processor 502 may include, but is not limited to, a microprocessor MCU, a programmable logic device FPGA, etc.) a memory 504 for storing data, and a transmission module 506 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 5 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the computer terminal 50 may also include more or fewer components than shown in FIG. 5, or have a different configuration than shown in FIG. 5.
It should be noted that the one or more processors 502 and/or other data processing circuits described above may be referred to herein generally as "data processing circuits. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated, in whole or in part, into any of the other elements in the computer terminal 50 (or mobile device). As referred to in embodiments of the application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination connected to the interface).
The memory 504 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the multi-system knowledge entity management method in the embodiment of the application, and the processor 502 executes the software programs and modules stored in the memory 504 to perform various functional applications and data processing, i.e. implement the multi-system knowledge entity management method described above. Memory 504 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 504 may further comprise memory located remotely from the processor 502, which may be connected to the computer terminal 50 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 506 is used to receive or transmit data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 50. In one example, the transmission device 506 includes a network adapter (Network Interface Controller, NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 506 may be a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 50 (or mobile device).
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the related art or all or part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application, which are intended to be comprehended within the scope of the present application.

Claims (10)

1. A method for managing multiple system knowledge entities, comprising:
receiving a link request sent by a browser, wherein the link request carries target identification information of a target system accessed by the browser;
Determining a target knowledge entity selection mode corresponding to the target identification information;
Determining a target knowledge entity corresponding to the knowledge entity selection mode in a knowledge entity base according to the target knowledge entity selection mode;
And sending the target knowledge entity to the browser, wherein the browser is used for displaying the target knowledge entity.
2. The method for managing multiple system knowledge entities according to claim 1, wherein said determining a knowledge entity selection manner corresponding to said system identification information comprises:
determining a knowledge entity selection mode database, wherein the knowledge entity selection mode database comprises identification information of each system and a knowledge entity selection mode corresponding to the identification information;
and searching the target knowledge entity selection mode according to the target identification information in the knowledge entity selection mode database.
3. The method of claim 2, wherein the knowledge entity selection manner of each system in the knowledge entity selection manner database is determined by:
Acquiring historical browsing data of each system;
according to the historical browsing data of each system, determining attention degree parameters and association degree parameters of knowledge entities in the knowledge entity library corresponding to each system, wherein the attention degree parameters are used for reflecting the frequency of the retrieval of the knowledge entities by each system, and the association degree parameters are used for reflecting the semantic association degree among different knowledge entities or the frequency of the simultaneous retrieval;
and determining the selection mode of the knowledge entity of each system according to the attention degree parameter and the association degree parameter of the knowledge entity in the knowledge entity library corresponding to each system.
4. The method for managing multiple system knowledge entities as set forth in claim 3, wherein said step of determining a selection manner of said knowledge entities of each system according to said attention degree parameter and said association degree parameter of the knowledge entities in said knowledge entity base corresponding to each system comprises:
Determining a first type of knowledge entity according to the attention degree parameter, wherein the first type of knowledge entity is a corresponding knowledge entity of which the attention degree parameter is larger than a preset attention degree parameter threshold;
Determining a second type of knowledge entity according to the association degree parameter, wherein the association degree parameter is a knowledge entity with the association degree parameter with the first type of knowledge entity being larger than a preset association degree parameter threshold;
and determining the first type of knowledge entity and the second type of knowledge entity as the target knowledge entity to be selected, thereby obtaining the target knowledge entity selection mode.
5. The method of claim 1, wherein the step of sending the target knowledge entity to the browser comprises:
sending a request path corresponding to the target knowledge entity to the browser; and
And sending domain name information corresponding to the target knowledge entity to the browser.
6. The method for managing multiple system knowledge entities according to claim 1, wherein after said step of determining a target knowledge entity selection method corresponding to said target identification information, said method for managing multiple system knowledge entities further comprises:
acquiring user identity information sent by the browser;
Determining a historical access record of the user identity information according to the user identity information;
and updating the target knowledge entity selection mode according to the history access record.
7. The method of claim 6, wherein the step of updating the target knowledge entity selection mode according to the history access record comprises:
constructing a user portrait according to the history access record;
Determining a retrievable set corresponding to the user portrait, wherein the retrievable set is used for retrieving a knowledge entity corresponding to the user portrait in the knowledge entity library;
And updating the target knowledge entity selection mode according to the search type set.
8. A multi-system knowledge entity management apparatus, comprising:
The first processing module is used for receiving a link request sent by a browser, wherein the link request carries target identification information of a target system accessed by the browser;
the second processing module is used for determining a target knowledge entity selection mode corresponding to the target identification information;
The third processing module is used for determining a target knowledge entity corresponding to the knowledge entity selection mode in a knowledge entity base according to the target knowledge entity selection mode;
And the fourth processing module is used for sending the target knowledge entity to the browser, wherein the browser is used for displaying the target knowledge entity.
9. A non-volatile storage medium, wherein a program is stored in the non-volatile storage medium, and wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform the multi-system knowledge entity management method of any one of claims 1 to 7.
10. An electronic device, comprising: a memory and a processor for executing a program stored in the memory, wherein the program is run to perform the multi-system knowledge entity management method of any one of claims 1 to 7.
CN202410026588.1A 2024-01-08 2024-01-08 Multi-system knowledge entity management method, device and nonvolatile storage medium Pending CN118014061A (en)

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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410026588.1A CN118014061A (en) 2024-01-08 2024-01-08 Multi-system knowledge entity management method, device and nonvolatile storage medium

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Publication Number Publication Date
CN118014061A true CN118014061A (en) 2024-05-10

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