CN116594658A

CN116594658A - Version upgrading method and device for metadata, electronic equipment and medium

Info

Publication number: CN116594658A
Application number: CN202310806710.2A
Authority: CN
Inventors: 李翔宇; 王祺; 杜思良
Original assignee: Beijing Volcano Engine Technology Co Ltd
Current assignee: Beijing Volcano Engine Technology Co Ltd
Priority date: 2023-07-03
Filing date: 2023-07-03
Publication date: 2023-08-15
Anticipated expiration: 2043-07-03
Also published as: CN116594658B

Abstract

Embodiments of the present disclosure relate to a version upgrade method, apparatus, electronic device, and medium for metadata. The method includes obtaining a first set of classifications as a first version of metadata of the content and a second set of classifications for upgrading a second version of the metadata. The method further includes verifying whether the category labels in the first set of categories all have corresponding category labels in the second set of categories. The method also includes further comparing the first set of content acquired through the first set of classifications with the second set of content acquired through the second set of classifications. In addition, the method includes updating the metadata by replacing the first set of classifications with the second set of classifications in response to the comparison of the first set of content to the second set of content meeting the predetermined condition. According to the embodiment of the disclosure, when the metadata of the content is upgraded, not only the mapping relation of the new version and the old version is verified, but also the content call of the new version and the old version is verified, so that the accuracy and the stability of the metadata upgrade are ensured.

Description

Version upgrading method and device for metadata, electronic equipment and medium

Technical Field

The present disclosure relates generally to the field of computers, and more particularly, to a version upgrade method, apparatus, electronic device, and medium for metadata.

Background

With the continuous growth of platform content, metadata management systems based on platform content are also becoming more and more important. Wherein the metadata is intermediate data for describing attribute information of the platform content. And a classification label obtained by classifying the platform content in the service field can be used as a representation form of the metadata in the service field.

Based on the rapid growth of platform content, content suites often encounter application scenarios of classification label selection, wherein classification labels relate to multi-topic content classification, vertical content classification, vehicle model and train classification, local living article classification, administrative division classification, author classification, thematic classification, and the like. With the enrichment of content, the classification labels have a further expanding trend, and the metadata management system needs to be continuously optimized to match the expansion efficiency of the classification labels.

Disclosure of Invention

The embodiment of the disclosure provides a version upgrading method, device, electronic equipment and medium for metadata.

According to a first aspect of the disclosure, a version upgrade method for metadata is provided. The method includes obtaining a first set of classifications as a first version of metadata of the content and a second set of classifications for upgrading a second version of the metadata. The method further includes verifying whether the category labels in the first set of categories all have corresponding category labels in the second set of categories. The method further includes comparing the first set of content acquired through the first set of classifications with the second set of content acquired through the second set of classifications in response to verifying that the classification tags in the first set of classifications all have corresponding classification tags in the second set of classifications. In addition, the method includes updating the metadata by replacing the first set of classifications with the second set of classifications in response to the comparison of the first set of content to the second set of content meeting the predetermined condition.

In a second aspect of the disclosure, a version upgrade apparatus for metadata is provided. The apparatus includes a metadata acquisition module configured to acquire a first set of classifications as a first version of metadata of content and a second set of classifications for upgrading a second version of metadata. The apparatus further includes a category verification module configured to verify whether category labels in the first set of categories all exist in the second set of categories with corresponding category labels. The apparatus also includes a content comparison module configured to compare the first set of content acquired by the first set of classifications with the second set of content acquired by the second set of classifications in response to verifying that the classification tags in the first set of classifications all have the corresponding classification tags in the second set of classifications. The apparatus further includes a metadata upgrade module configured to upgrade metadata by replacing the first set of classifications with the second set of classifications in response to a comparison of the first set of content and the second set of content meeting a predetermined condition.

In a third aspect of the present disclosure, an electronic device is provided. The electronic device comprises a processor and a memory coupled to the processor, the memory having instructions stored therein, which when executed by the processor, cause the electronic device to perform the method according to the first aspect.

In a fourth aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium has stored thereon computer-executable instructions, wherein the computer-executable instructions are executed by a processor to implement the method according to the first aspect.

The summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Drawings

The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:

FIG. 1 illustrates a schematic diagram of an example environment in which various embodiments of the present disclosure may be implemented;

FIG. 2 illustrates a flow chart of a version upgrade method for metadata in accordance with certain embodiments of the present disclosure;

FIG. 3 illustrates a schematic diagram of a process of verifying a first set of classifications and a second set of classifications by encoding according to some embodiments of the present disclosure;

FIG. 4 illustrates a schematic diagram of a process of comparing a first set of content and a second set of content by statistical information, according to some embodiments of the present disclosure;

FIG. 5 illustrates a schematic diagram of a process of verifying a first set of classifications and a second set of classifications by combinatorial screening of classification tags, according to some embodiments of the present disclosure;

FIG. 6 illustrates a schematic diagram of a process of adapting metadata in accordance with certain embodiments of the present disclosure;

FIG. 7 illustrates a block diagram of a version-up device for metadata in accordance with certain embodiments of the present disclosure; and

fig. 8 illustrates a block diagram of an electronic device, according to some embodiments of the present disclosure.

The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements.

Detailed Description

It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

In describing embodiments of the present disclosure, the term "comprising" and its like should be understood to be open-ended, i.e., including, but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like, may refer to different or the same object unless explicitly stated otherwise. Other explicit and implicit definitions are also possible below.

Because of the huge amount of content carried by the content platform, in order to provide access content for users quickly, a set of metadata-based classification system needs to be established for the platform content, and the access intention of the users is determined quickly through the selection and setting of classification labels under the classification system by the users. The classification system is a system for organizing metadata, which is formed after establishing a logical relationship between corresponding metadata based on the logical relationship between platform contents, and one classification system corresponds to one version of the metadata. In order to match the taxonomy to the ever-increasing platform content, it is often necessary to upgrade the taxonomy. In the traditional upgrading scheme, a new classification system is directly established and is associated with platform content to realize upgrading.

However, the data volume of the platform content is too large and various, which easily results in that part of the content is not associated with the upgraded classification system, and the upgraded classification system is easy to have a problem of incompatibility with the platform content. Thus, upgrades to the taxonomy of platform content in conventional upgrade schemes are not accurate enough and are not stable. For example, for a platform facing an enterprise user, if a classification system of the platform content cannot be stably upgraded, the enterprise user cannot normally access the platform content, so that the management of the enterprise user is seriously affected.

In the embodiment of the disclosure, in the metadata upgrading process, the classification label of the current classification system is obtained as a first group of classification, the classification label of the upgraded classification system is obtained as a second group of classification, and each classification label in the first group of classification is determined to find the corresponding classification label in the second group of classification, so that the situation that the corresponding part of platform content is not associated with the upgraded classification system due to the omission of part of classification labels in the first group of classification in the upgrading process is avoided. Further, platform contents under the first group of classification and platform contents under the second group of classification are grabbed in the platform, and a new classification system is determined to be matched with the platform contents through comparative analysis of the platform contents, so that the problem of compatibility of the upgraded classification system is avoided. By the embodiment of the disclosure, the classification system of the platform content can be accurately and stably upgraded by checking the classification labels of the two groups of classifications and the platform content.

FIG. 1 illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure may be implemented. As shown in fig. 1, the example environment 100 may include a computing device 102, which may be a user terminal, a mobile device, a computer, etc., which may also be a computing system, a single server, a distributed server, or a cloud-based server. Referring to FIG. 1, at block 104, metadata is obtained resulting in a first set of classifications of the current taxonomy and a second set of classifications of the upgraded taxonomy. The metadata is intermediate data for describing attribute information of the platform content, for example, the metadata may be used for describing storage location, type, style, size, source, etc. of the platform content. The taxonomy of metadata is used to describe the management and organization architecture of metadata. The first group of classifications and the second group of classifications describe the plurality of classification labels under the current classification system and the plurality of classification labels under the upgraded classification system in a set form respectively.

At block 106, the class labels in the first set of classifications and the class labels in the second set of classifications are validated to determine whether the class labels in the first set of classifications are all mapped to the class labels in the second set of classifications. It will be appreciated that the upgraded classification system is established based on the classification tags in the second set of classifications, and if a portion of the classification tags in the first set of classifications cannot find a corresponding classification tag in the second set of classifications, the upgraded classification system cannot be associated with the platform content under the portion of the classification tags in the first set of classifications. Thus, if one or more category labels in the first set of categories are not mapped into the second set of categories, then the category verification in block 106 cannot be passed.

If all the classification labels in the first group of classifications are mapped to the classification labels in the second group of classifications, then the first group of platform contents corresponding to the first group of classifications and the second group of platform contents corresponding to the second group of classifications are further captured in the platform, and the first group of platform contents are compared with the second group of platform contents, so that a comparison result of the first group of platform contents and the second group of platform contents is obtained.

In some embodiments, the first set of platform content may be compared to the second set of platform content based on a direct comparison, such as comparing all of the first set of platform content to all of the second set of platform content, or screening the first set of platform content to the second set of platform content according to the same screening rules. Alternatively, the first set of platform content and the second set of platform content may also be compared based on an indirect comparison, e.g., extracting content features of the first set of platform content and the second set of platform content and comparing.

In block 110, if the first set of platform contents and the second set of platform contents meet a certain condition, for example, the coincidence degree of the first set of platform contents and the second set of platform contents is high (or even the coincidence is complete) or most of the contents (or even all of the contents) of the first set of platform contents are included in the second set of platform contents, the first set of classifications in the current classification system are replaced by the second set of classifications based on the mapping relationship between the first set of classifications and the second set of classifications, so that the metadata is upgraded.

The method and the device upgrade the metadata based on the verification results of the classification labels and the platform contents under two groups of classifications corresponding to the metadata before and after upgrading. It should be understood that the architecture and functionality in the example environment 100 are described for illustrative purposes only and are not meant to suggest any limitation as to the scope of the disclosure. Embodiments of the present disclosure may also be applied to other environments having different structures and/or functions.

A process according to an embodiment of the present disclosure will be described in detail below in conjunction with fig. 2 to 8. For ease of understanding, specific data set forth in the following description are intended to be exemplary and are not intended to limit the scope of the disclosure. It will be appreciated that the embodiments described below may also include additional actions not shown and/or may omit shown actions, the scope of the present disclosure being not limited in this respect.

Fig. 2 illustrates a flow chart of a version upgrade method 200 for metadata in accordance with certain embodiments of the present disclosure. The computing device 102, which may be a user terminal, mobile device, computer, etc., may be a computing system, a single server, a distributed server, or a cloud-based server, etc., as the subject of the execution of the method 200. At block 202, a first set of classifications is obtained as a first version of metadata for content and a second set of classifications is obtained for upgrading a second version of metadata. Wherein the first version may be determined as the current taxonomy of metadata and the second version may be determined as the taxonomy of upgraded metadata. The first group of classifications and the second group of classifications are used for storing a plurality of classification labels under the classification system of the current metadata and a plurality of classification labels under the classification system of the updated metadata respectively.

In some embodiments, each class label affiliated under the hierarchy is read by the class hierarchy of metadata. For example, the classification system is stored in the memory in a unified modeling form, a field having a specific tag format or stored in a specific tag position in the memory is read, and then a classification tag is obtained based on the field.

In some embodiments, platform content is processed based on a content processing model to obtain a classification result of the platform content, and a classification label of the platform content is determined based on the classification result. Among them, the content processing model includes, but is not limited to, a natural language processing (Natural Language Processing, abbreviated as NLP) model, a neural network model, and the like. Before classifying the platform content by the neural network model, training the neural network model, specifically taking the platform content as input of the neural network model, extracting a classification result of the platform content by the neural network model, and adjusting parameters based on loss between the extracted classification result and a pre-labeled classification result, so that a loss function meets a convergence condition, thereby obtaining the trained neural network model. Alternatively, the classification tag may be obtained after setting the platform content manually.

In some embodiments, the first set of classifications of the current classification system is typically stored in a database, and the second set of classifications of the upgraded classification system may be stored in the database in advance, or may be obtained in real time by a neural network model or manually before upgrading. Further, classification labels of platform content generated by a user in the using process can be collected, and the first group of classifications and the second group of classifications stored in the database can be expanded based on a full synchronization or incremental synchronization mode.

In some embodiments, the category labels in the first set of categories and the category labels in the second set of categories may be organized based on a tree structure. The tree structure comprises nodes and edges used for connecting the nodes, the nodes are used for describing classification labels, and the edges arranged between the nodes are used for describing logical relations between the classification labels. Based on the position of the node in the path, the node may be classified into a primary node, a secondary node, a tertiary node, and the like. For example, the "video" is taken as the trunk of the tree structure, the first-level nodes comprise "movies", "television dramas", "cartoons", short videos "and the like, the first-level nodes comprise second-level nodes such as" comedy "," action "and" suspense "and the second-level nodes comprise third-level nodes such as" domestic comedy "," japanese and korean comedy "," european and american comedy "and the like.

In some embodiments, on-line previewing is implemented by corresponding operations on the tree structure stored in the database, for example, by fast accessing the tree structure by way of high-performance reads, by hierarchical operations and leaf operations in the tree structure by way of tree memory operations, and by converting the encoding of classification labels corresponding to the nodes into names, etc. In addition, for the online preview function, a user gray scale may also be set, where the user gray scale may be set for the entire version, or personalized for a particular channel, such as configuration removal or configuration specified location insertion.

In some embodiments, the platform content often has some metadata associated with the extension information in addition to the category labels. Because the data size of the expansion information is larger and has uncertainty compared with the classification label, the nodes of the tree structure are only used for describing the classification label in general, but not used for describing the expansion information, and the expansion information associated with the nodes can be acquired through the corresponding relation between the prestored nodes and the expansion information, so that more comprehensive context information is provided for the nodes. In addition, when the extension information is stored, the description data of the tree structure and the description data of the extension information can be stored separately, so that the tree structure and the extension information can be managed conveniently. Further, for the data volume and diversity of the classification labels, the classification labels can be managed based on static tree operation logic, so that nodes and paths in the tree structure can be directly extracted.

With continued reference to FIG. 2, at block 204, it is verified whether the class labels in the first set of classifications all have a corresponding class label in the second set of classifications. All the classification labels in the first group of classification need to be mapped to the classification labels in the second group of classification, so that platform contents associated with all the classification labels in the first group of classification can be captured through the classification labels in the second group of classification under the upgraded classification system. In order to avoid the situation that part of platform contents cannot be captured after upgrading, the corresponding relation between the first group of classifications and the second group of classifications needs to be verified. An example implementation of verifying the first set of classifications and the second set of classifications is described below in connection with FIG. 3.

Fig. 3 illustrates a schematic diagram of a process 300 for verifying a first set of classifications and a second set of classifications by encoding according to some embodiments of the present disclosure. In some embodiments, as shown in FIG. 3, the first set of classifications 302 includes classification tags 302-1, classification tags 302-2, … …, classification tag 302-N, and the second set of classifications 304 includes classification tag 304-1, classification tags 304-2, … …, classification tag 304-M, where M, N is a positive integer. The class labels 302-1, class labels 302-2, … …, class label 302-N in the first set of classes 302 are assigned with the codes 306-1, 306-2, … … of the first set of codes 306, 306-N in order, and the class labels 304-1, class labels 304-2, … …, class label 304-M in the second set of classes 304 are assigned with the codes 308-1, 308-2, … …, 308-M in the second set of codes 308 in order. By performing the code verification in block 210 on the correspondence between the first set of codes 306 and the second set of codes 308, the correspondence between the first set of classifications 302 and the second set of classifications 304 may be determined. Wherein the correspondence between the first set of codes 306 and the second set of codes 308 is validated in block 210, specifically by determining whether all codes in the first set of codes 306 have been mapped to codes in the second set of codes 308. The classification is checked through the codes, so that the mixing of the classification label for the front end and the codes for the rear end can be avoided in the process of managing the metadata, and the rear end can conveniently check the classification label. And compared with the classification labels, the codes have uniqueness, so that classification labels can be better distinguished, confusion of classification labels with the same expression but different associated contents is avoided, and the accurate verification of the classification labels is realized.

It will be appreciated that the class label is used for presentation at the front end and the unique code is used for identification information as a class label at the back end. The class labels in the first set of classes may be the same as the class labels in the second set of classes, but the codes in the first set of codes are not the same as the codes in the second set of codes. Further, a unique code of the class label may be generated by a unique Identification (ID) generator to ensure the uniqueness of the code.

In some embodiments, the correspondence between the classification labels of the first set of classifications and the classification labels of the second set of classifications may be described based on a mapping between the tree structure nodes of the first set of classifications and the tree structure nodes of the second set of classifications. Thus, a verification may be made as to whether all of the tree structure nodes of the first set of classifications map to tree structure nodes of the second set of classifications, thereby determining whether the classification labels in the first set of classifications all exist in the second set of classifications with corresponding classification labels.

Referring back to fig. 2, at block 206, in response to verifying that the category labels in the first set of categories all have corresponding category labels in the second set of categories, the first set of content acquired through the first set of categories is compared to the second set of content acquired through the second set of categories. If the first group classification and the second group classification pass verification, the first group content of the first group classification and the second group content of the second group classification are further captured in the platform content. An example implementation of comparing the first set of content to the second set of content is described below in connection with fig. 4, and an example implementation of comparing the first set of screened content to the second set of screened content based on a combination of classification tags of the screening is described in connection with fig. 5, wherein the first set of content and the second set of content in fig. 4 are derived based on a single classification tag, and the first set of screened content and the second set of screened content in fig. 5 are derived based on a combination of a plurality of classification tags.

Fig. 4 illustrates a schematic diagram of a process 400 for comparing a first set of content and a second set of content by statistical information, according to some embodiments of the present disclosure. In some embodiments, as shown in FIG. 4, a first set of classifications 402 includes classification tags 402-1, classification tags 402-2, … …, classification tag 402-N, a second set of classifications 404 includes classification tag 404-1, classification tags 404-2, … …, classification tag 404-M, a first set of content 406 includes content 406-1, content 406-2, … …, content 406-E, and a second set of content 408 includes content 408-1, content 408-2, … …, content 408-F, where N, M, E and F are positive integers. After capturing the first set of content 406 corresponding to the first set of classifications 402 and the second set of content 408 corresponding to the second set of classifications 404, extracting and counting information of the first set of content 406 and the second set of content 408 respectively to obtain statistical information 410 and statistical information 412, and finally determining the similarity 414 of the statistical information 410 and the statistical information 412. By extracting the statistical information of the first group of contents and the second group of contents and performing similarity calculation, all contents are prevented from being directly compared, so that the data volume of the similarity calculation is reduced, and the efficiency of content verification is improved.

In some embodiments, samples of the captured content may be extracted according to a predetermined sampling rule and the extracted samples compared. Alternatively, feature extraction and analysis may be performed on the captured content, so as to obtain indirect description information such as an image style (corresponding to the image content), a video style (corresponding to the video content), a content change curve, and the like, as statistical information.

Fig. 5 illustrates a schematic diagram of a process 500 for verifying a first set of classifications and a second set of classifications by combinatorial screening of classification tags according to some embodiments of the present disclosure. In some embodiments, as shown in FIG. 5, the first set of classifications 502 includes classification tags 502-1, classification tags 502-2, … …, classification tag 502-N, and the second set of classifications 504 includes classification tag 504-1, classification tags 504-2, … …, classification tag 504-M, where N, M is a positive integer. The first set of classifications 502 and the second set of classifications 504 are screened at block 506 and block 508, respectively, to obtain a first set of screening classifications 510 and a second set of screening classifications 512, the first set of screening classifications 510 including screening tags 510-1, screening tags 510-2, … …, screening tags 510-P, the second set of screening classifications 512 including screening tags 512-1, screening tags 512-2, … …, screening tags 512-U, where P is a positive integer less than or equal to N and U is a positive integer less than or equal to M. A first set of screening content 514 that corresponds to all screening tags of the first set of screening categories 510 at the same time is grabbed, and a second set of screening content 516 that corresponds to all screening tags of the second set of screening categories 512 at the same time, wherein the first set of screening content 514 includes content 514-1, content 514-2, … …, content 514-Q, and the second set of screening content 516 includes content 516-1, content 516-2, … …, content 516-V, wherein Q, V is a positive integer. The first set of filter content 514 and the second set of filter content 516 are compared at block 518 to obtain a comparison.

Further, a test request may be sent to the user device, after receiving an instruction that the user agrees to test, the first set of screening categories 510 and the second set of screening categories 512 sent by the electronic device of the user are obtained, based on the first set of screening categories 510 and the second set of screening categories 512, the first set of screening contents 514 and the second set of screening contents 516 are grabbed, and the first set of screening contents 514 and the second set of screening contents 516 are compared, so as to obtain a comparison result. By verifying the combination of the classified labels, unexpected situations caused by the combination of a plurality of screening factors are avoided, and the accuracy and stability of metadata upgrading are further improved.

Returning to FIG. 2, at block 208, the metadata is upgraded by replacing the first set of classifications with the second set of classifications in response to the comparison of the first set of content to the second set of content meeting the predetermined condition. In some embodiments, the predetermined condition may be set such that, for example, the first set of content is high in coincidence (or even fully coincident) with the second set of content or a substantial portion of the first set of content (or even all of the content) is contained in the second set of content.

In some embodiments, during the metadata upgrading process, the user equipment still displays an interactive interface corresponding to the first group of content, receives an interactive instruction of the user equipment on an unequalized class label in the first group of classes in the interactive interface, and sends the content corresponding to the unequalized class label to the user equipment; and receiving an interaction instruction of the user equipment on the upgraded classification label in the first group of classification in the interaction interface, determining the classification label in the second group of classification corresponding to the upgraded classification label, and sending the content corresponding to the classification label in the second group of classification to the user equipment.

In some embodiments, user grayscales are determined, and an upgrade channel is gradually opened to the user device based on the user grayscales. And in the gradual opening process, collecting feedback experience of a user opening an upgrade channel and a test result of user equipment, and adjusting a gray level period, an opening range and the like based on the feedback experience. Through the user gray level, the problems can be responded and adjusted in time in the upgrading process, and smooth upgrading of the version is ensured. Alternatively, after upgrading the version in the user equipment, the evaluation index of the user is obtained, including the overall content screening index, the application content consumption index, the subjective satisfaction degree and the like.

In some embodiments, metadata is used for isolating service scenes based on service types, each service scene is provided with a plurality of versions corresponding to a classification system, each service scene comprises an online version and a plurality of historical versions, different versions can be mapped with each other, and version switching is realized in a memory based on the mapping relation between the different versions. Before version switching, a new version can be created or extracted, mapping relations among the versions are verified, on-line data of different versions are compared and evaluated, evaluation results are fed back through a migration callback interface, and then version switching is performed based on the verification and evaluation results, so that an upgrading flow of creating the new version, mapping inspection, callback inspection, version upgrading and version switching is achieved, and version upgrading is achieved on the premise that service of a user is not affected.

Fig. 6 illustrates a schematic diagram of a process 600 for adapting metadata in accordance with certain embodiments of the present disclosure. In some embodiments, as shown in FIG. 6, metadata 602-1, metadata 602-2, … …, metadata 602-S are unified data modeled in unified metadata platform 602 and stored in a tree structure, where S is a positive integer. For access requests of different platforms, such as front end 608, data development end 610, and back end 612, for metadata, adaptation layer 604 adapts metadata based on the corresponding platform type, and sends the adapted data to the platform requesting access through transport layer 606. The transmission layer 606 is provided with a uniform caliber, which is used for acquiring different adaptation requirements and feeding back metadata in a corresponding format.

It can be appreciated that the front end 608, the data development end 610, and the back end 612 listed in this disclosure are merely examples of platforms, and in an application scenario, different platforms can be set based on multiple requirements such as online business, management state, and offline report, so that performance and form of metadata can be differentiated on the premise of being based on unified metadata. By adapting to different platforms, different business scenes and the user classification label customization requirements can be more fully considered, so that a universal metadata model which supports multiple sources, multiple versions and multiple use scenes and has uniform label semantics is obtained. By adapting the metadata model, the service platform does not need to convert after receiving metadata, and consistency of the metadata in different service platforms can be ensured, so that maintenance and synchronization cost of the metadata is reduced.

Receiving the access request for metadata sent by the front end 608, the adaptation layer 604 extracts the tree structure of the metadata from the unified metadata platform 602, tiles the tree structure, and sends the tree structure and the tiled tree structure to the front end 608 through the transmission layer 606. The tree structure and the tiled tree structure may also be serialized before being sent to the front end 608. For example, the tree structure and the tiled tree structure are serialized through a lightweight data exchange format of JavaScript object notation (JavaScript Object Notation, JSON for short).

Receiving the access request for metadata sent by the data initiator 610, the adaptation layer 604 extracts the tree structure of metadata from the unified metadata platform 602 and converts the tree structure into a table structure, such as Hive table, which is sent to the data initiator 610 via the transport layer 606. Wherein Hive is a data warehouse based on Hadoop, and a data developer can quickly query Hive tables stored in Hive for describing tree structures through query statements.

Receiving the access request for metadata sent by the back end 612, the adaptation layer 604 determines a cache interface that invokes the tree structure, and based on the cache interface, causes the transport layer 606 to send the tree structure to the back end 612. The data transmission to the tree structure is realized through a remote procedure call (Remote Procedure Call, RPC) interface, such as a thread RPC interface. In addition, the backend 612 may also directly call the program corresponding to the tree structure based on the remote procedure call interface. Through the cache interface, the efficiency with which the back-end 612 obtains and manipulates metadata is improved.

In some embodiments, after upgrading the version in the user device, a request for the user to access the metadata is received from the user device, the identity information of the user in the request is resolved, the corresponding permission information is determined based on the identity information of the user, and then the class label in the second set of classes with permission is sent to the user device. By authenticating the rights of the user, the privacy and the security of the metadata can be ensured. For example, enterprise users may have different sales policies for content, requiring rights control over the category labels of different content.

In some embodiments, a unified visibility configuration is provided based on the tree structure of the taxonomy. And loading the visibility configuration of the user when different users access the tree structure, and displaying or hiding the nodes of the tree structure based on the visibility configuration, so that the visibility control of different users is realized. It will be appreciated that the view of metadata seen by different clients may be different, and that by virtue of the visibility configuration of the tree structure, sensitive metadata may be ensured not to be accessed by unauthorized users.

Fig. 7 illustrates a block diagram of a version-up device 700 for metadata in accordance with certain embodiments of the present disclosure. As shown in fig. 7, the apparatus 700 includes a metadata acquisition module 702 configured to acquire a first set of classifications as a first version of metadata of content and a second set of classifications for upgrading a second version of metadata. The apparatus 700 further comprises a category verification module 704 configured to verify whether category labels in the first set of categories all exist in the second set of categories. The apparatus 700 further includes a content comparison module 706 configured to compare the first set of content acquired through the first set of classifications with the second set of content acquired through the second set of classifications in response to verifying that the classification tags in the first set of classifications all have the corresponding classification tags in the second set of classifications. In addition, the apparatus 700 includes a metadata upgrade module 708 configured to upgrade metadata by replacing the first set of classifications with the second set of classifications in response to the comparison of the first set of content and the second set of content meeting a predetermined condition.

Fig. 8 illustrates a block diagram of an electronic device 800, which device 800 may be a device or apparatus described in embodiments of the present disclosure, in accordance with certain embodiments of the present disclosure. As shown in fig. 8, device 800 includes a Central Processing Unit (CPU) and/or a Graphics Processing Unit (GPU) 802 that may perform various suitable actions and processes in accordance with computer program instructions stored in a read-only memory (ROM) 804 or loaded from a storage unit 816 into a Random Access Memory (RAM) 806. In the RAM 806, various programs and data required for the operation of the device 800 can also be stored. CPU/GPU 802, ROM 804, and RAM 806 are connected to each other via bus 808. An input/output (I/O) interface 810 is also connected to bus 808. Although not shown in fig. 8, device 800 may also include a coprocessor.

Various components in device 800 are connected to I/O interface 810, including: an input unit 812 such as a keyboard, a mouse, etc.; an output unit 814 such as various types of displays, speakers, and the like; a storage unit 816, such as a magnetic disk, optical disk, etc.; and communication unit 818 such as a network card, modem, wireless communication transceiver, etc. Communication unit 818 allows device 800 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.

The various methods or processes described above may be performed by the CPU/GPU 802. For example, in some embodiments, the method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 816. In some embodiments, some or all of the computer programs may be loaded and/or installed onto device 800 via ROM 804 and/or communication unit 818. When the computer program is loaded into RAM 806 and executed by CPU/GPU 802, one or more steps or actions in the methods or processes described above may be performed.

In some embodiments, the methods and processes described above may be implemented as a computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

The computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object-oriented programming language and conventional procedural programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two consecutive blocks may in fact be performed substantially in parallel, and they may sometimes be performed in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Some example implementations of the present disclosure are listed below.

Example 1. A version upgrade method for metadata, comprising:

obtaining a first set of classifications as a first version of metadata of the content and a second set of classifications for upgrading a second version of the metadata;

verifying whether the class labels in the first set of classes all have corresponding class labels in the second set of classes;

in response to said verifying that both class labels in said first set of classes have corresponding class labels in said second set of classes, comparing a first set of content acquired by said first set of classes with a second set of content acquired by said second set of classes; and

in response to the comparison of the first set of content and the second set of content meeting a predetermined condition, the metadata is upgraded by replacing the first set of classifications with the second set of classifications.

Example 2 the method of example 1, wherein each category label in the first set of categories and each category label in the second set of categories is assigned a unique code, and verifying whether the category labels in the first set of categories all have a corresponding category label in the second set of categories comprises:

Verifying whether each code of a first set of codes assigned to the first set of classifications has a corresponding code of a second set of codes assigned to the second set of classifications.

Example 3 the method of any of examples 1-2, wherein comparing the first set of content acquired through the first set of classifications with the second set of content acquired through the second set of classifications comprises:

extracting the first group of content based on a preset rule to obtain first extraction information;

extracting the second group of content based on the preset rule to obtain second extraction information; and

and determining the similarity between the first extraction information and the second extraction information.

Example 4. The method of any one of examples 1-3, further comprising:

comparing a third set of content acquired simultaneously by at least two category labels in the first set of categories with a fourth set of content acquired simultaneously by at least two category labels in the second set of categories,

wherein updating the metadata by replacing the first set of classifications with the second set of classifications includes: the metadata is upgraded by replacing the first set of classifications with the second set of classifications in response to a comparison of the first set of content to the second set of content meeting a first predetermined condition and in response to a comparison of the third set of content to the fourth set of content meeting a second predetermined condition.

Example 5 the method of any one of examples 1-4, wherein the first set of classifications is organized into a first tree structure and the second set of classifications is organized into a second tree structure.

Example 6 the method of any one of examples 1-5, wherein a mapping relationship exists between nodes of the first tree structure and nodes of the second tree structure.

Example 7 the method of any one of examples 1-6, further comprising:

receiving a first request from a front end for obtaining the metadata;

converting the second tree structure into a tiled classification tree structure in response to receiving the first request; and

and sending the tiled classification tree structure to the front end.

Example 8 the method of any one of examples 1-7, further comprising:

receiving a second request for acquiring the metadata from a data development terminal;

converting the second tree structure into a table structure in response to receiving the second request; and

and sending the table structure to the data development terminal.

Example 9 the method of any one of examples 1-8, further comprising:

receiving a third request for acquiring the metadata from the rear end;

responsive to receiving the third request, determining to invoke a cache interface of the second tree structure; and

And sending the metadata to the back end through the cache interface.

Example 10 the method of any one of examples 1-9, wherein the second set of classifications is associated with a set of attribute information, the method further comprising:

storing the second tree structure in a first database; and

the set of attribute information corresponding to the second set of categories is stored in association in a second database.

Example 11. The method of any one of examples 1-10, further comprising:

receiving an access request for the metadata from a user device;

determining rights information of the user equipment in response to receiving the access request; and

and determining a classification label corresponding to the authority information in the second group of classifications based on the authority information.

Example 12. A version upgrade apparatus for metadata, comprising:

a metadata acquisition module configured to acquire a first set of classifications as a first version of metadata of content and a second set of classifications for upgrading a second version of metadata;

a category verification module configured to verify whether category labels in the first set of categories all have corresponding category labels in the second set of categories;

A content comparison module configured to compare a first set of content acquired by the first set of classifications with a second set of content acquired by the second set of classifications in response to the verifying that the classification tags in the first set of classifications all have corresponding classification tags in the second set of classifications; and

a metadata upgrade module configured to upgrade the metadata by replacing the first set of classifications with the second set of classifications in response to a comparison of the first set of content and the second set of content meeting a predetermined condition.

Example 13 the apparatus of example 12, wherein each category label in the first set of categories and each category label in the second set of categories is assigned a unique code, and the category verification module comprises:

a code verification module configured to verify whether each code of a first set of codes assigned to the first set of classifications exists in a second set of codes assigned to the second set of classifications.

Example 14 the apparatus of any one of examples 12-13, wherein the content comparison module comprises:

a first extraction module configured to extract the first set of content based on a predetermined rule, obtaining first extraction information;

A second extraction module configured to extract the second set of content based on the predetermined rule, obtaining second extraction information; and

and a similarity determination module configured to determine a similarity between the first extraction information and the second extraction information.

Example 15 the apparatus of any one of examples 12-14, further comprising:

a combined content comparison module configured to compare a third set of content acquired simultaneously by at least two category labels in the first set of categories and a fourth set of content acquired simultaneously by at least two category labels in the second set of categories,

wherein, metadata upgrade module includes:

a combination condition judgment module configured to upgrade the metadata by replacing the first group of classifications with the second group of classifications in response to a comparison result of the first group of contents and the second group of contents satisfying a first predetermined condition, and in response to a comparison result of the third group of contents and the fourth group of contents satisfying a second predetermined condition.

Example 16 the apparatus of any of examples 12-15, wherein the first set of classifications is organized into a first tree structure and the second set of classifications is organized into a second tree structure.

Example 17 the apparatus of any one of examples 12-16, wherein a mapping relationship exists between nodes of the first tree structure and nodes of the second tree structure.

Example 18 the apparatus of any one of examples 12-17, further comprising:

a front-end request receiving module configured to receive a first request for acquiring the metadata from a front end;

a front-end request response module configured to convert the second tree structure into a tiled classification tree structure in response to receiving the first request; and

and the front-end metadata sending module is configured to send the tiled classification tree structure to the front end.

Example 19 the apparatus of any one of examples 12-18, further comprising:

a data development end request receiving module configured to receive a second request for acquiring the metadata from a data development end;

a data development end request response module configured to convert the second tree structure into a table structure in response to receiving the second request; and

and the data development end metadata sending module is configured to send the table structure to the data development end.

Example 20 the apparatus of any one of examples 12-19, further comprising:

A back-end request receiving module configured to receive a third request for acquiring the metadata from the back-end;

a back-end request response module configured to determine to invoke a cache interface of the second tree structure in response to receiving the third request; and

and the back-end metadata sending module is configured to send the metadata to the back-end through the cache interface.

Example 21 the apparatus of any one of examples 12-20, wherein the second set of classifications is associated with a set of attribute information, the apparatus further comprising:

a first storage module configured to store the second tree structure in a first database; and

a second storage module configured to store the set of attribute information corresponding to the second set of categories in association in a second database.

Example 22 the apparatus of any one of examples 12-21, further comprising:

a user request receiving module configured to receive an access request for the metadata from a user device;

a user request response module configured to determine rights information of the user device in response to receiving the access request; and

and the tag permission determination module is configured to determine a classification tag corresponding to the permission information in the second group of classifications based on the permission information.

Example 23 an electronic device, comprising:

a processor; and

a memory coupled with the processor, the memory having instructions stored therein, which when executed by the processor, cause the electronic device to perform actions comprising:

Example 24 the electronic device of example 23, wherein each category label in the first set of categories and each category label in the second set of categories is assigned a unique code, and verifying whether the category labels in the first set of categories all have a corresponding category label in the second set of categories comprises:

Example 25 the electronic device of any of examples 23-24, wherein comparing the first set of content acquired through the first set of classifications with the second set of content acquired through the second set of classifications comprises:

Example 26 the electronic device of any of examples 23-25, the acts further comprising:

Example 27 the electronic device of any of examples 23-26, wherein the first set of classifications is organized into a first tree structure and the second set of classifications is organized into a second tree structure.

Example 28 the electronic device of any of examples 23-27, wherein a mapping relationship exists between nodes of the first tree structure and nodes of the second tree structure.

Example 29 the electronic device of any of examples 23-28, the acts further comprising:

receiving a first request from a front end for obtaining the metadata;

and sending the tiled classification tree structure to the front end.

Example 30 the electronic device of any one of examples 23-29, the acts further comprising:

and sending the table structure to the data development terminal.

Example 31 the electronic device of any of examples 23-30, the acts further comprising:

receiving a third request for acquiring the metadata from the rear end;

and sending the metadata to the back end through the cache interface.

Example 32 the electronic device of any of examples 23-31, wherein the second set of classifications is associated with a set of attribute information, the acts further comprising:

storing the second tree structure in a first database; and

Example 33 the electronic device of any of examples 23-32, the acts further comprising:

receiving an access request for the metadata from a user device;

Example 34. A computer-readable storage medium having stored thereon computer-executable instructions, wherein the computer-executable instructions are executed by a processor to implement the method according to any of examples 1 to 11.

Example 35. A computer program product tangibly stored on a computer-readable medium and comprising computer-executable instructions that, when executed by an apparatus, cause the apparatus to perform the method of any one of examples 1 to 11.

Although the disclosure has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims

1. A version upgrade method for metadata, comprising:

2. The method of claim 1, wherein each class label in the first set of classes and each class label in the second set of classes is assigned a unique code, and verifying whether the class labels in the first set of classes all have a corresponding class label in the second set of classes comprises:

3. The method of claim 1, wherein comparing a first set of content acquired through the first set of classifications with a second set of content acquired through the second set of classifications comprises:

4. A method according to any one of claims 1-3, further comprising:

5. The method of claim 1, wherein the first set of classifications is organized into a first tree structure and the second set of classifications is organized into a second tree structure.

6. The method of claim 5, wherein there is a mapping relationship between nodes of the first tree structure and nodes of the second tree structure.

7. The method of claim 5, further comprising:

receiving a first request from a front end for obtaining the metadata;

and sending the tiled classification tree structure to the front end.

8. The method of claim 5, further comprising:

and sending the table structure to the data development terminal.

9. The method of claim 5, further comprising:

receiving a third request for acquiring the metadata from the rear end;

And sending the metadata to the back end through the cache interface.

10. The method of claim 5, wherein the second set of classifications is associated with a set of attribute information, the method further comprising:

storing the second tree structure in a first database; and

11. The method of claim 1, further comprising:

receiving an access request for the metadata from a user device;

12. A version upgrade apparatus for metadata, comprising:

13. An electronic device, comprising:

a processor; and

a memory coupled with the processor, the memory having instructions stored therein, which when executed by the processor, cause the electronic device to perform the method of any of claims 1-11.

14. A computer readable storage medium having stored thereon computer executable instructions, wherein the computer executable instructions are executed by a processor to implement the method of any of claims 1 to 11.