CN117688222B - Implementation method and system of digital library based on Bayesian network - Google Patents
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Abstract
The application discloses a method and a system for realizing a digital library based on a Bayesian network, which can also recommend digital resources for a user under the condition of meeting the user demand by a technical means. On one hand, the digital resource management based on the Bayesian network is realized through data processing for management and supervision purposes; on the other hand, conditions are provided for further reducing the consumption of supervision and management resources.
Description
Technical Field
The application relates to the technical field of data processing suitable for management, supervision or prediction purposes, in particular to a method and a system for realizing a digital library based on a Bayesian network.
Background
The Digital Library is a Library for processing and storing various graphic and literature by Digital technology, the word is translated by English Digital Library, and is essentially a distributed information system for multimedia production, and information resources of various different carriers and different geographic positions are stored by Digital technology so as to be a large information system for inquiring and spreading across an object-oriented network of areas.
For example, publication (bulletin) number: CN101887549B, patent title: a book acquisition system of a digital library (main class number: G06Q 10/00) realizes the management of digital resources through the design of the digital library. On the one hand, the data processing technology capable of explaining the purpose of supervision or prediction is widely available in the technical field of digital libraries; on the other hand, it can be stated that the technology mining in this field has a wide range of expansion prospects.
Disclosure of Invention
The embodiment of the application provides a digital library implementation method and system based on a Bayesian network, which aim to at least partially solve the technical problems.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a method for implementing a digital library based on a bayesian network, where the method includes:
Determining a distribution period of the query frequency of the digital resources based on the historical query condition information of each digital resource managed by the digital library;
clustering the digital resources based on the distribution period to obtain a plurality of resource classes;
determining the average query frequency of the resource class in the distribution period to which the resource class belongs for each resource class;
Determining, for each of the resource classes, an edge probability of the resource class, the edge probability being positively correlated with the average query frequency;
Monitoring the inquiry condition of the resource class, if the inquiry frequency of the resource class is higher than the highest inquiry frequency of the resource class which the resource class belongs to in the last subsection period in a history time period which is the first time from the current moment, determining the standardized constant of the resource class, wherein the standardized constant is positively related to the degree that the inquiry frequency is higher than the average inquiry frequency of the resource class which the resource class belongs to;
When a query request is received, determining the resource class aimed by the query request as a target class;
Determining a conditional probability based on the query frequency of the digital resource contained in the target class in a historical time period of a second time from the current moment, wherein the conditional probability is positively correlated with the query frequency in the historical time period of the second time;
Determining a posterior probability of the target class based on the edge probability, the normalization constant, and the conditional probability using a bayesian network, the bayesian network being determined based on bayesian rules;
Taking the digital resource matched with the query request in the target class as a target resource;
and sequencing the target resources based on the posterior probability of the target class to which the target resources belong to, so as to obtain the displayable resources.
In an alternative embodiment of the present specification, the method further comprises:
The second time period is longer than the first time period; and/or, after obtaining the presentable resource, presenting the presentable resource.
In an alternative embodiment of the present specification, the method further comprises:
The clustering adopts k-means clustering.
In an optional embodiment of the present disclosure, when a query request is received, determining, as a target class, the resource class for which the query request is directed, includes:
carrying out semantic analysis on the query request to obtain an analysis result;
Taking the appointed number with the highest matching degree with the analysis result in the digital resources as alternative resources;
and taking the resource class to which the alternative resource belongs as the target class.
In an alternative embodiment of the present specification, the method further comprises:
the specified number is positively correlated with the semantic definition of the parsing result.
In an alternative embodiment of the present specification, the method further comprises:
and when the to-be-stored resource is received, determining the resource class to which the to-be-stored resource belongs based on the similarity of the to-be-stored resource and each digital resource managed by the digital library.
In a second aspect, an embodiment of the present application further provides a system for implementing a digital library based on a bayesian network, where the system includes:
a distribution period determination module configured to: determining a distribution period of the query frequency of the digital resources based on the historical query condition information of each digital resource managed by the digital library;
A resource class determination module configured to: clustering the digital resources based on the distribution period to obtain a plurality of resource classes;
An average query frequency determination module configured to: determining the average query frequency of the resource class in the distribution period to which the resource class belongs for each resource class;
An edge probability determination module configured to: determining, for each of the resource classes, an edge probability of the resource class, the edge probability being positively correlated with the average query frequency;
A normalization constant determination module configured to: monitoring the inquiry condition of the resource class, if the inquiry frequency of the resource class is higher than the highest inquiry frequency of the resource class which the resource class belongs to in the last subsection period in a history time period which is the first time from the current moment, determining the standardized constant of the resource class, wherein the standardized constant is positively related to the degree that the inquiry frequency is higher than the average inquiry frequency of the resource class which the resource class belongs to;
the target class determination module is configured to: when a query request is received, determining the resource class aimed by the query request as a target class;
A conditional probability determination module configured to: determining a conditional probability based on the query frequency of the digital resource contained in the target class in a historical time period of a second time from the current moment, wherein the conditional probability is positively correlated with the query frequency in the historical time period of the second time;
A posterior probability determination module configured to: determining a posterior probability of the target class based on the edge probability, the normalization constant, and the conditional probability using a bayesian network, the bayesian network being determined based on bayesian rules;
A target resource determination module configured to: taking the digital resource matched with the query request in the target class as a target resource;
a resource determination module may be presented configured to: and sequencing the target resources based on the posterior probability of the target class to which the target resources belong to, so as to obtain the displayable resources.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
A processor; and
A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method steps of the first aspect.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method steps of the first aspect.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects:
The digital library is used for managing digital resources, and is specifically expressed as follows: and feeding corresponding digital resources back to the user according to the query request of the user. With the increasing demands of users for use, merely providing users with their targeted digital resources has not been able to guarantee the user's experience. In addition, in some cases, the user does not necessarily know the digital resource of the target explicitly, and the digital library is required to have a recommendation function. The method and the system in the specification can also recommend digital resources for the user under the condition of meeting the user requirement by technical means. On one hand, the digital resource management based on the Bayesian network is realized through data processing for management and supervision purposes; on the other hand, conditions are provided for further reducing the consumption of supervision and management resources.
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 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 process schematic diagram of a digital library implementation method based on a bayesian network according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
The application will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present application. However, one skilled in the art will readily recognize that some of the features may be omitted, or replaced by other elements, materials, or methods in different situations. In some instances, related operations of the present application have not been shown or described in the specification in order to avoid obscuring the core portions of the present application, and may be unnecessary to persons skilled in the art from a detailed description of the related operations, which may be presented in the description and general knowledge of one skilled in the art.
Furthermore, the described features, operations, or characteristics of the description may be combined in any suitable manner in various embodiments. Also, various steps or acts in the method descriptions may be interchanged or modified in a manner apparent to those of ordinary skill in the art. Thus, the various orders in the description and drawings are for clarity of description of only certain embodiments, and are not meant to be required orders unless otherwise indicated.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning. The term "coupled" as used herein includes both direct and indirect coupling (coupling), unless otherwise indicated.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, the implementation method of the digital library based on the bayesian network in the present specification includes the following steps:
S100: based on historical query condition information of each digital resource managed by the digital library, a distribution period of the query frequency of the digital resource is determined.
The digital resource in the specification is characterized by a digital form, and the functions of the digital library are not only the input, the deletion and the like of the digital resource, but also the recommendation of the digital resource to the user based on the requirement of the user.
The query frequency may be characterized by a time node, e.g., days may be used as the time node. The user's demand for digital resources is typically periodic. For example, for examination materials, it is common that the frequency of queries at the end of the school period is higher, while the frequency of queries at other time nodes is lower. While some have stronger periodicity, the peaks and troughs of the query frequency are more obvious, while some have weaker periodicity, the peaks and troughs are less obvious, but always have periodicity. For periods of weaker periodicity, it can be seen that one period has not yet ended.
In addition, the period is long or short, and the period is one year or 3 months.
S102: and clustering the digital resources based on the distribution period to obtain a plurality of resource classes.
The clustering means in the related art can be applied to the present specification as conditions allow. In an alternative embodiment of the present description, the clustering uses k-means clustering.
For example, digital resources with a distribution period of one year and 13 months are grouped into one class. While digital resources with a distribution period of 3 months are clustered in another class.
In actual operation, a unique identifier may be allocated to a resource class and added to the digital resource of the resource class.
S104: and determining the average query frequency of the resource class in the distribution period to which the resource class belongs for each resource class.
The average query frequency is the average value of the query frequency, and indicates the demand level of the user on the macroscopic level for the digital resources in the resource class.
S106: for each of the resource classes, determining an edge probability of the resource class.
The edge probability in this specification is positively correlated with the average query frequency. The edge probability is the degree of demand of users on the digital resources of the type in a macroscopic level, and is a representation of the situation in a longer historical time period. That is, the edge probability does not take into account the impact of recent and even real-time incidents on the user's needs.
Illustratively, in the long term, the probability of a certain qualification test in one year requiring the teaching aid digital resources is the edge probability. However, when an emergency occurs in a certain year, the current examination is cancelled, and the examination is prolonged to the next year, and the reference personnel in the next year are increased, the corresponding inquiry amount of the digital resource is increased in the next year, and the emergency and irregular event cannot be represented by the edge probability.
S108: and monitoring the query condition of the resource class, and if the query frequency of the resource class is higher than the highest query frequency of the resource class to which the resource class belongs in the last subsection period in the historical time period of the first time from the current time, determining the standardized constant of the resource class.
In general, emergency events are difficult to predict accurately. If massive webpage information is monitored to detect an emergency, a large amount of resources are consumed, which is not preferable.
The method in the specification does not need to monitor public opinion. If the query frequency of the resource class is higher than the highest query frequency of the resource class to which the resource class belongs in the last subsection period in the history time period which is the first time from the current moment, the emergency corresponding to the resource class is indicated. The normalization constant characterizes the conditional probability that occurs under the condition of the query frequency corresponding to the distribution period. The normalization constant may be obtained by a bayesian algorithm. The normalization constant in this specification is positively correlated to the extent that the query frequency is higher than the average query frequency of the resource class to which it belongs.
In an alternative embodiment of the present description, the first time period is a preset time period.
S110: and when a query request is received, determining the resource class aimed by the query request as a target class.
The resource class is obtained based on the distribution period, and then the determination of the target class can characterize whether the behavior of the user is based on the emergency.
In an optional embodiment of the present disclosure, the query request is semantically parsed to obtain a parsing result. In the related art, the technical means that can be used to realize semantic analysis are applicable to the present specification when conditions allow. And taking the appointed number with the highest matching degree with the analysis result in the digital resources as an alternative resource. In the related art, the technical means capable of determining the matching degree are applicable to the present specification, if the conditions allow. And taking the resource class to which the alternative resource belongs as the target class.
At least some of the digital resources contained in the target class are query-compliant, that is, the target class is an extension of the user's needs as compared to the alternative resources, possibly containing digital resources of interest to the user.
In an alternative embodiment of the present specification, the specified number is positively correlated with the semantic clarity of the parsing result. Semantic clarity is used to indicate whether a user knows explicitly what he wants. The higher the semantic clarity, the more explicit the user. Taking the patent literature as an example, when the user explicitly knows the application number, it is indicated that the user explicitly knows what he wants. If keywords such as "digital library" and "system" are input by the search, it is indicated that what is desired by the user is known with ambiguity. In the related art, the technical means capable of quantifying whether the user is clear or not is applicable to the present specification when the price adjustment is permitted.
S112: and determining the conditional probability based on the query frequency of the digital resource contained in the target class in a historical time period of a second duration from the current moment.
The conditional probability is positively correlated with the query frequency over the historical time period of the second duration.
The historical time period of the second duration is also a time period which is closer to the current moment, and the inquiry behavior in the time period combines the combination of the emergency and the historical rule.
In an alternative embodiment of the present disclosure, the second time period is longer than the first time period. So that the conditional probability covers the whole process of the incident as much as possible.
S114: a Bayesian network is employed to determine posterior probabilities of the target classes based on the edge probabilities, the normalization constants, and the conditional probabilities.
The bayesian network in the present specification is determined based on the bayesian rule, and the bayesian network may be regarded as an image of the bayesian rule, and may be a device capable of performing data processing based on the bayesian rule.
S116: and taking the digital resource matched with the query request in the target class as a target resource.
In the related art, the technical means for determining the matching degree is applicable to the present specification, as the conditions allow. The target resource in this specification may be more than one.
S118: and sequencing the target resources based on the posterior probability of the target class to which the target resources belong to, so as to obtain the displayable resources.
Based on the sorting performed in the step, the influence of the emergency on the inquiry can be reflected and appears, and the interest of the user can be more met under the condition that the user inquires based on the emergency.
The digital library is used for managing digital resources, and is specifically expressed as follows: and feeding corresponding digital resources back to the user according to the query request of the user. With the increasing demands of users for use, merely providing users with their targeted digital resources has not been able to guarantee the user's experience. In addition, in some cases, the user does not necessarily know the digital resource of the target explicitly, and the digital library is required to have a recommendation function. The method and the system in the specification can also recommend digital resources for the user under the condition of meeting the requirement of the user.
Further, the present disclosure also provides a system for implementing a digital library based on a bayesian network, the system comprising:
a distribution period determination module configured to: determining a distribution period of the query frequency of the digital resources based on the historical query condition information of each digital resource managed by the digital library;
A resource class determination module configured to: clustering the digital resources based on the distribution period to obtain a plurality of resource classes;
An average query frequency determination module configured to: determining the average query frequency of the resource class in the distribution period to which the resource class belongs for each resource class;
An edge probability determination module configured to: determining, for each of the resource classes, an edge probability of the resource class, the edge probability being positively correlated with the average query frequency;
A normalization constant determination module configured to: monitoring the inquiry condition of the resource class, if the inquiry frequency of the resource class is higher than the highest inquiry frequency of the resource class which the resource class belongs to in the last subsection period in a history time period which is the first time from the current moment, determining the standardized constant of the resource class, wherein the standardized constant is positively related to the degree that the inquiry frequency is higher than the average inquiry frequency of the resource class which the resource class belongs to;
the target class determination module is configured to: when a query request is received, determining the resource class aimed by the query request as a target class;
A conditional probability determination module configured to: determining a conditional probability based on the query frequency of the digital resource contained in the target class in a historical time period of a second time from the current moment, wherein the conditional probability is positively correlated with the query frequency in the historical time period of the second time;
A posterior probability determination module configured to: determining a posterior probability of the target class based on the edge probability, the normalization constant, and the conditional probability using a bayesian network, the bayesian network being determined based on bayesian rules;
A target resource determination module configured to: taking the digital resource matched with the query request in the target class as a target resource;
a resource determination module may be presented configured to: and sequencing the target resources based on the posterior probability of the target class to which the target resources belong to, so as to obtain the displayable resources.
The system can execute the method in any of the foregoing embodiments and achieve the same or similar technical effects, and will not be described herein.
Fig. 2 is a schematic structural view of an electronic device according to an embodiment of the present application. Referring to fig. 2, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 2, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form a digital library implementation system based on the Bayesian network on a logic level. The processor executes the program stored in the memory and is specifically used for executing any one of the implementation methods of the digital library based on the Bayesian network.
The implementation method of the digital library based on the Bayesian network disclosed in the embodiment of the application shown in fig. 1 can be applied to a processor or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but may also be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may also execute a method for implementing a digital library based on a bayesian network in fig. 1, and implement the functions of the embodiment shown in fig. 1, which is not described herein.
The embodiments of the present application also provide a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device comprising a plurality of application programs, perform any one of the foregoing bayesian network based digital library implementation methods.
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 a system 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 the element.
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 foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (9)
1. A method for implementing a digital library based on a bayesian network, the method comprising:
Determining a distribution period of the query frequency of the digital resources based on the historical query condition information of each digital resource managed by the digital library;
clustering the digital resources based on the distribution period to obtain a plurality of resource classes;
determining the average query frequency of the resource class in the distribution period to which the resource class belongs for each resource class;
Determining, for each of the resource classes, an edge probability of the resource class, the edge probability being positively correlated with the average query frequency;
Monitoring the inquiry condition of the resource class, if the inquiry frequency of the resource class is higher than the highest inquiry frequency of the resource class which the resource class belongs to in the latest distribution period in a history time period which is a first time period from the current moment, determining the standardized constant of the resource class, wherein the standardized constant is positively related to the degree that the inquiry frequency is higher than the average inquiry frequency of the resource class which the resource class belongs to;
When a query request is received, determining the resource class aimed by the query request as a target class;
Determining a conditional probability based on the query frequency of the digital resource contained in the target class in a historical time period of a second time from the current moment, wherein the conditional probability is positively correlated with the query frequency in the historical time period of the second time;
Determining a posterior probability of the target class based on the edge probability, the normalization constant, and the conditional probability using a bayesian network, the bayesian network being determined based on bayesian rules;
Taking the digital resource matched with the query request in the target class as a target resource;
and sequencing the target resources based on the posterior probability of the target class to which the target resources belong to, so as to obtain the displayable resources.
2. The method of claim 1, wherein the method further comprises:
The second time period is longer than the first time period; and/or, after obtaining the presentable resource, presenting the presentable resource.
3. The method of claim 1, wherein the method further comprises:
The clustering adopts k-means clustering.
4. The method of claim 1, wherein upon receiving a query request, determining the resource class for which the query request is directed as a target class comprises:
carrying out semantic analysis on the query request to obtain an analysis result;
Taking the appointed number with the highest matching degree with the analysis result in the digital resources as alternative resources;
and taking the resource class to which the alternative resource belongs as the target class.
5. The method of claim 4, wherein the method further comprises:
the specified number is positively correlated with the semantic definition of the parsing result.
6. The method of claim 1, wherein the method further comprises:
and when the to-be-stored resource is received, determining the resource class to which the to-be-stored resource belongs based on the similarity of the to-be-stored resource and each digital resource managed by the digital library.
7. A system for implementing a digital library based on a bayesian network, the system comprising:
a distribution period determination module configured to: determining a distribution period of the query frequency of the digital resources based on the historical query condition information of each digital resource managed by the digital library;
A resource class determination module configured to: clustering the digital resources based on the distribution period to obtain a plurality of resource classes;
An average query frequency determination module configured to: determining the average query frequency of the resource class in the distribution period to which the resource class belongs for each resource class;
An edge probability determination module configured to: determining, for each of the resource classes, an edge probability of the resource class, the edge probability being positively correlated with the average query frequency;
A normalization constant determination module configured to: monitoring the inquiry condition of the resource class, if the inquiry frequency of the resource class is higher than the highest inquiry frequency of the resource class which the resource class belongs to in the latest distribution period in a history time period which is a first time period from the current moment, determining the standardized constant of the resource class, wherein the standardized constant is positively related to the degree that the inquiry frequency is higher than the average inquiry frequency of the resource class which the resource class belongs to;
the target class determination module is configured to: when a query request is received, determining the resource class aimed by the query request as a target class;
A conditional probability determination module configured to: determining a conditional probability based on the query frequency of the digital resource contained in the target class in a historical time period of a second time from the current moment, wherein the conditional probability is positively correlated with the query frequency in the historical time period of the second time;
A posterior probability determination module configured to: determining a posterior probability of the target class based on the edge probability, the normalization constant, and the conditional probability using a bayesian network, the bayesian network being determined based on bayesian rules;
A target resource determination module configured to: taking the digital resource matched with the query request in the target class as a target resource;
a resource determination module may be presented configured to: and sequencing the target resources based on the posterior probability of the target class to which the target resources belong to, so as to obtain the displayable resources.
8. An electronic device, comprising:
A processor; and
A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 6.
9. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-6.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604322A (en) * | 2009-06-24 | 2009-12-16 | 北京理工大学 | A kind of decision level text automatic classified fusion method |
| CN103186558A (en) * | 2011-12-28 | 2013-07-03 | 中国航空工业集团公司第六三一研究所 | Intelligent management method based on library management information system |
| CN117093653A (en) * | 2023-10-19 | 2023-11-21 | 中国标准化研究院 | Informationized resource sharing method and system |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101604322A (en) * | 2009-06-24 | 2009-12-16 | 北京理工大学 | A kind of decision level text automatic classified fusion method |
| CN103186558A (en) * | 2011-12-28 | 2013-07-03 | 中国航空工业集团公司第六三一研究所 | Intelligent management method based on library management information system |
| CN117093653A (en) * | 2023-10-19 | 2023-11-21 | 中国标准化研究院 | Informationized resource sharing method and system |
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