CN114969460A - Resource recommendation method, device and equipment based on knowledge graph and storage medium - Google Patents
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Abstract
The application discloses a resource recommendation method, a resource recommendation device, resource recommendation equipment and a storage medium based on a knowledge graph, and belongs to the technical field of teaching resource recommendation. The method comprises the following steps: acquiring a target knowledge point to be queried by a user and portrait data of the user; acquiring teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data; and sending the teaching resources to the terminal of the user. In other words, in the method and the device, the matched teaching resources are recommended for the user according to the target knowledge points to be inquired by the user and the portrait data of the user, the personalized requirements of the user are met, and the learning efficiency of the user is improved.
Description
Technical Field
The present application relates to the technical field of teaching resource recommendation, and in particular, to a resource recommendation method, apparatus, device, and storage medium based on a knowledge graph.
Background
With the development of internet technology, the way for people to acquire knowledge is not limited to schools and books, and online learning becomes an important way for people to acquire knowledge. However, the existing online learning platform cannot meet the individual learning requirements of different users, and after the users spend a lot of time watching the teaching resources on the platform, the users find that the teaching resources are not matched with the learning habits and receptivity of the users, so that the learning efficiency of the users is poor.
Disclosure of Invention
The application mainly aims to provide a resource recommendation method, a resource recommendation device and a resource recommendation storage medium based on a knowledge graph, and aims to solve the technical problem that the existing online learning mode cannot meet the personalized learning requirements of different users, so that the learning efficiency of the users is poor.
In order to achieve the above object, the present application provides a resource recommendation method based on a knowledge graph, including the following steps:
acquiring a target knowledge point to be inquired by a user and portrait data of the user;
acquiring teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data;
and sending the teaching resources to the terminal of the user.
Optionally, the step of obtaining teaching resources matched with the user according to the target knowledge point to be queried and the portrait data includes:
acquiring a knowledge graph matched with the target knowledge point to be inquired according to the target knowledge point to be inquired;
determining knowledge points to be learned of the user from a knowledge map matched with the target knowledge points to be queried according to functional data and cognitive data in the portrait data;
and acquiring the teaching resources matched with the user from a teaching resource library based on the knowledge points to be learned of the user.
Optionally, the step of determining the knowledge points to be learned by the user from the knowledge graph matched with the target knowledge points to be queried according to the functional data and the cognitive data in the portrait data includes:
acquiring knowledge nodes associated with the function data from the knowledge graph matched with the target knowledge points to be inquired according to the function data;
inquiring knowledge points corresponding to knowledge nodes associated with the functional data in the cognitive data;
if the knowledge point corresponding to the knowledge node associated with the functional data is stored in the cognitive data and the mastery degree of the knowledge point corresponding to the knowledge node associated with the functional data does not meet a preset condition, determining the knowledge point corresponding to the knowledge node associated with the functional data as the knowledge point to be learned by the user;
and if the knowledge points corresponding to the knowledge nodes associated with the functional data do not exist in the cognitive data, determining the knowledge points corresponding to the knowledge nodes associated with the functional data as the knowledge points to be learned by the user.
Optionally, the step of obtaining teaching resources matched with the user from a teaching resource library based on the knowledge points to be learned by the user includes:
and acquiring a plurality of teaching resources corresponding to the knowledge point to be learned from a teaching resource library, and selecting the teaching resource matched with the user from the plurality of teaching resources.
Optionally, the teaching resources include teaching videos, and the step of selecting teaching resources matching the user from the plurality of teaching resources includes:
acquiring teaching habit data corresponding to the plurality of teaching videos;
calculating the similarity between learning habit data in the portrait data and teaching habit data corresponding to the plurality of teaching videos;
and determining the teaching video corresponding to the teaching habit data with the maximum similarity as the teaching video matched with the user.
Optionally, after the step of sending the teaching resource to the terminal of the user, the method further includes:
and receiving a teaching result of the teaching resource sent by the user terminal, and updating portrait data of the user according to the teaching result.
Optionally, before the step of obtaining the target knowledge point to be queried by the user and the portrait data of the user, the method includes:
and constructing a knowledge graph and a teaching resource library, wherein nodes in the knowledge graph are associated with teaching resources in the teaching resource library, and the nodes in the knowledge graph are also associated with function data.
In addition, to achieve the above object, the present application further provides a resource recommendation device based on a knowledge graph, including:
the first acquisition module is used for acquiring a target knowledge point to be queried by a user and portrait data of the user;
the second acquisition module is used for acquiring teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data;
and the sending module is used for sending the teaching resources to the terminal of the user.
In addition, to achieve the above object, the present application further provides a resource recommendation apparatus based on a knowledge graph, the apparatus including: the resource recommendation system comprises a memory, a processor and a knowledge-graph based resource recommendation program stored on the memory and operable on the processor, wherein the knowledge-graph based resource recommendation program is configured to implement the steps of the knowledge-graph based resource recommendation method as described above.
In addition, to achieve the above object, the present application further provides a storage medium having a resource recommendation program based on a knowledge graph stored thereon, wherein the resource recommendation program based on a knowledge graph realizes the steps of the resource recommendation method based on a knowledge graph as described above when being executed by a processor.
Compared with the prior art that the on-line learning mode cannot meet the personalized learning requirements of different users and the learning efficiency of the users is poor, the resource recommendation method, the device, the equipment and the storage medium based on the knowledge graph have the advantages that target knowledge points to be inquired by the users and portrait data of the users are obtained; acquiring teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data; and sending the teaching resources to the terminal of the user. In other words, in the method and the device, the matched teaching resources are recommended for the user according to the target knowledge points to be inquired by the user and the portrait data of the user, the personalized requirements of the user are met, and the learning efficiency of the user is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic diagram of a knowledge-graph-based resource recommendation device for a hardware operating environment according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a first embodiment of a knowledge-graph-based resource recommendation method according to the present application;
FIG. 3 is a flowchart illustrating a knowledge-graph-based resource recommendation apparatus according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a resource recommendation device based on a knowledge graph for a hardware operating environment according to an embodiment of the present application.
As shown in fig. 1, the resource recommendation device based on knowledge-graph may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the architecture shown in FIG. 1 does not constitute a limitation of a knowledge-graph based resource recommendation device, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and a knowledge-graph-based resource recommendation program.
In the knowledge-graph based resource device shown in fig. 1, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the resource recommendation device based on the knowledge graph may be disposed in the resource recommendation device based on the knowledge graph, and the resource recommendation device based on the knowledge graph calls the resource recommendation program based on the knowledge graph stored in the memory 1005 through the processor 1001 and executes the resource recommendation method based on the knowledge graph provided by the embodiment of the present application.
An embodiment of the present application provides a resource recommendation method based on a knowledge graph, and referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of the resource recommendation method based on a knowledge graph according to the present application.
In this embodiment, the resource recommendation method based on a knowledge graph includes:
step S10, acquiring target knowledge points to be inquired by a user and portrait data of the user;
step S20, obtaining teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data;
and step S30, sending the teaching resources to the terminal of the user.
Compared with the prior art that the on-line learning mode cannot meet the personalized learning requirements of different users, so that the learning efficiency of the users is poor, the embodiment obtains the target knowledge points to be queried of the users and the portrait data of the users; acquiring teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data; and sending the teaching resources to the terminal of the user. That is to say, in this embodiment, according to the target knowledge point to be queried by the user and the portrait data of the user, a matched teaching resource is recommended for the user, so as to meet the personalized requirements of the user and improve the learning efficiency of the user.
The method comprises the following specific steps:
and step S10, acquiring the target knowledge point to be inquired by the user and the portrait data of the user.
The user's portrait data is recorded and stored in the memory of the online learning platform, and when the online learning platform detects that the user logs in, the server of the online learning platform can read the portrait data of the user from the memory. The portrait data includes functional data, cognitive data, and learning habit data. Wherein the functional data is used for characterizing the occupation of the user, for example, the occupation of the user is a student, a mechanical engineer or a construction engineer; the cognitive data are used for representing the mastery degree of the user on the knowledge points, the lowest mastery degree is 0 (the knowledge points are not learned by the user), and the highest mastery degree is 100 (the knowledge points are completely mastered by the user); the learning habit data is used for representing some habits in the aspect of learning of the user, such as the time length of single learning, the speed of speech of a speaker when the teaching resources are teaching videos, and the like.
The method for acquiring the target knowledge point to be inquired by the user comprises the following steps:
and receiving the natural language input by the user, and analyzing the natural language input by the user to obtain a target knowledge point to be queried by the user.
The natural language input mode of the user may be one of a voice mode, a click mode and a touch mode.
The steps of analyzing the natural language input by the user to obtain the target knowledge point to be queried by the user specifically comprise:
cutting the natural language input by the user according to a preset cutting rule to obtain an information segment set consisting of a plurality of information segments;
matching each information segment in the information segment set with a noun in a standard word list to obtain a matching degree, wherein the noun in the standard word list is a noun of a name corresponding to each knowledge graph;
and determining a target knowledge point to be queried by the user according to the matching degree.
For example, in one embodiment, the input information is text information:
and a, truncating the text information according to a preset truncating rule to obtain a plurality of character strings, wherein the character strings form a character string set.
Wherein the truncation rule includes: counting the number of characters of the text information, and setting a truncated character number set, wherein the truncated character number set is { the number of characters of the text information, the number of characters of the text information-1, … …,2 }; selecting elements in the truncated character number set as truncated character numbers, and truncating the text information according to the truncated character numbers; and repeating the step of selecting the elements in the truncated character number set as the truncated character number and truncating the text information according to the truncated character number until all the elements in the truncated character number set are traversed.
For example, when the text message is "vehicle engine operating principle", that is, the truncated character number set is {9, 8,7,6,5,4,3,2 }. Firstly, 9 is selected as the number of truncation characters, the text information is truncated to obtain a character string of 'vehicle engine working principle', secondly, 8 is selected as the number of truncation characters, the file information is truncated to obtain the character string of 'vehicle engine working principle' and 'principle', secondly, 7 is selected as the number of truncation characters, the text information is truncated to obtain the character string of 'vehicle engine working' and 'principle', … …, and finally, 2 is selected as the number of truncation characters, the text information is truncated to obtain the character strings of 'vehicle', 'engine', 'machine work', 'source' and 'principle'. Finally, the character strings obtained in the above process are combined in the order of the truncation to generate a character string set { vehicle engine operation principle, vehicle engine, … …, operation principle, vehicle, … … }. It should be noted that, when there is duplication in the individual character strings acquired in the process of truncation by the truncation rule, only one of the duplicated character strings needs to be reserved when the character string set is finally generated.
And b, matching each information segment in the information segment set with a noun in a standard word list to obtain a matching degree, wherein the noun in the standard word list is the noun of the name corresponding to each knowledge graph.
In this embodiment, step b may specifically be:
and matching each character string in the character string set with the nouns in the standard word list one by one according to the sequence, wherein the matching degree is the ratio of the number of characters which are consistent with the character strings in the nouns in the standard word list to the total number of characters of the nouns in the standard word list.
And c, determining a target knowledge point to be queried by the user according to the matching degree.
In the embodiment, considering that the input information is input manually, and errors are inevitably generated in the input process, the character string with the highest matching degree is used as the target knowledge point to be queried by the user, so that the problem that the resource recommendation method based on the knowledge graph cannot be implemented due to the fact that the character string with the highest matching degree cannot be obtained is solved.
In another embodiment, when the input information is voice information, a step of converting the voice information into text information is added as compared with when the input information is text information, and the remaining embodiments are the same as those in which the input information is text information.
It should be noted that, in this embodiment, converting the speech information into the text information may be implemented by using any existing speech recognition method, and therefore, details are not described in this embodiment again.
Further, in this embodiment, before the step of acquiring the target knowledge point to be queried by the user and the portrait data of the user, the method includes:
and constructing a knowledge graph and a teaching resource library, wherein nodes in the knowledge graph are associated with teaching resources in the teaching resource library, and the nodes in the knowledge graph are also associated with the function data.
And step S20, obtaining teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data.
Specifically, acquiring a teaching resource matched with the user according to the target knowledge point to be queried and the portrait data, including:
and step S21, acquiring a knowledge graph matched with the target knowledge point to be inquired according to the target knowledge point to be inquired.
In this embodiment, the target knowledge points to be queried are knowledge points that have a large range and can individually correspond to the knowledge graph. For example, a knowledge point of a vehicle engine, which includes a diesel engine, a gasoline engine, an electric vehicle motor, a hybrid power machine, etc., i.e., a vehicle transmitter, may correspond to a knowledge map.
And step S22, determining knowledge points to be learned by the user from the knowledge map matched with the target knowledge points to be queried according to the functional data and the cognitive data in the portrait data.
Specifically, the step of determining the knowledge points to be learned by the user from the knowledge map matched with the target knowledge points to be queried according to the functional data and the cognitive data in the portrait data includes:
and step S221, acquiring knowledge nodes related to the functional data from the knowledge graph matched with the target knowledge points to be inquired according to the functional data.
It should be noted that, in the present embodiment, the knowledge nodes in the knowledge graph are associated with the functional data. For example, taking screws as an example, there are screws that are dedicated to the mechanical domain and screws that are dedicated to the architectural domain, i.e., there are knowledge nodes of two screws in the knowledge graph, where the knowledge node of one screw is associated with the machine and the knowledge node of the other screw is associated with the building.
Step S222, inquiring knowledge points corresponding to the knowledge nodes associated with the functional data in the cognitive data;
step S223, if the knowledge point corresponding to the knowledge node associated with the functional data already exists in the cognitive data and the mastery degree of the knowledge point corresponding to the knowledge node associated with the functional data does not satisfy a preset condition, determining the knowledge point corresponding to the knowledge node associated with the functional data as the knowledge point to be learned by the user;
step S224, if the knowledge points corresponding to the knowledge nodes associated with the functional data do not exist in the cognitive data, determining the knowledge points corresponding to the knowledge nodes associated with the functional data as the knowledge points to be learned by the user.
According to the method and the system, the knowledge nodes in the knowledge map are associated with the function data, and then the knowledge associated with the functions of the user can be recommended to the user in a targeted manner according to the mastering degree of the knowledge nodes of the user, so that the learning efficiency of the user is further improved.
And step S23, acquiring teaching resources matched with the user from a teaching resource library based on the knowledge points to be learned of the user.
Specifically, the step of obtaining teaching resources matched with the user from a teaching resource library based on the knowledge points to be learned of the user includes:
and acquiring a plurality of teaching resources corresponding to the knowledge point to be learned from a teaching resource library, and selecting the teaching resources matched with the user from the plurality of teaching resources.
After the knowledge points to be learned of the user are determined, all teaching resources corresponding to the knowledge points to be learned and resource description information corresponding to each teaching resource are obtained from a teaching resource library, the resource description information corresponding to each teaching resource is traversed, the teaching resources which simultaneously comprise the knowledge points to be learned of the user are screened out from all the teaching resources corresponding to the knowledge points to be learned of the user, and one or more teaching resources are randomly selected from the screened teaching resources to serve as the final teaching resources matched with the user.
In this embodiment, the instructional resources may exist in the form of instructional videos.
When the teaching resources are teaching videos, the step of selecting teaching resources matched with the user from the plurality of teaching resources comprises:
acquiring teaching habit data corresponding to the plurality of teaching videos;
calculating the similarity between learning habit data in the portrait data and teaching habit data corresponding to the plurality of teaching videos;
and determining the teaching video corresponding to the teaching habit data with the maximum similarity as the teaching video matched with the user.
In this embodiment, the teaching habit data is the teaching video time and the speed of speech of the interpreter, and the learning habit data is the user single learning time and the speed of speech of the teaching video interpreter that the user is suitable for. Respectively calculating the ratio of the teaching video time to the user single learning time and the ratio of the speed of speech of the interpreter to the speed of speech of the teaching video interpreter suitable for the user, carrying out weighted calculation on the ratio of the teaching video time to the user single learning time and the ratio of the speed of speech of the interpreter to the speed of speech of the teaching video interpreter suitable for the user to obtain the similarity between the learning habit data in the portrait data and the teaching habit data corresponding to the teaching video, and determining the teaching video corresponding to the teaching habit data with the maximum similarity as the teaching video matched with the user.
It should be noted that if there are a plurality of teaching habit data with the largest similarity, a teaching video with the best teaching quality is selected from the plurality of teaching habit data, and the selected teaching video is determined as a teaching video matched with the user. In this embodiment, the teaching quality of the teaching video can be measured by the number of praise of the video points, and the greater the number of praise of the video points, the better the teaching quality of the teaching video is.
And step S30, sending the teaching resources to the terminal of the user.
And after the teaching resources are matched for the user, the teaching resources are sent to the terminal of the user. The terminal of the user displays the teaching resources, and the user learns by browsing the teaching resources. And after the user learns the teaching resources, the terminal of the user also sends the teaching result of the user to a server of the online learning platform. And the server of the online learning platform receives the teaching result of the teaching resource sent by the user terminal and updates the portrait data of the user according to the teaching result.
The embodiment of the application also provides a resource recommendation device based on the knowledge graph, and referring to fig. 3, fig. 3 is a schematic flow diagram of a first embodiment of the resource recommendation device based on the knowledge graph.
In this embodiment, the resource recommendation device based on knowledge-graph includes:
the first acquisition module 10 is used for acquiring a target knowledge point to be queried by a user and portrait data of the user;
a second obtaining module 20, configured to obtain, according to the target knowledge point to be queried and the portrait data, teaching resources matched with the user;
a sending module 30, configured to send the teaching resource to the terminal of the user.
Optionally, the second obtaining module includes:
the first matching acquisition unit is used for acquiring a knowledge graph matched with the target knowledge point to be inquired according to the target knowledge point to be inquired;
the determining unit is used for determining the knowledge points to be learned of the user from the knowledge map matched with the target knowledge points to be inquired according to the functional data and the cognitive data in the portrait data;
and the second matching acquisition unit is used for acquiring the teaching resources matched with the user from a teaching resource library based on the knowledge points to be learned of the user.
Optionally, the determining unit is configured to:
acquiring knowledge nodes associated with the function data from the knowledge graph matched with the target knowledge points to be inquired according to the function data;
inquiring knowledge points corresponding to knowledge nodes associated with the functional data in the cognitive data;
if the knowledge point corresponding to the knowledge node associated with the functional data is stored in the cognitive data and the mastery degree of the knowledge point corresponding to the knowledge node associated with the functional data does not meet a preset condition, determining the knowledge point corresponding to the knowledge node associated with the functional data as the knowledge point to be learned by the user;
and if the knowledge points corresponding to the knowledge nodes associated with the functional data do not exist in the cognitive data, determining the knowledge points corresponding to the knowledge nodes associated with the functional data as the knowledge points to be learned by the user.
Optionally, the second matching obtaining unit is configured to implement:
and acquiring a plurality of teaching resources corresponding to the knowledge point to be learned from a teaching resource library, and selecting the teaching resource matched with the user from the plurality of teaching resources.
Optionally, the tutorial resource comprises a tutorial video, and the selecting a tutorial resource from the plurality of tutorial resources that matches the user comprises:
acquiring teaching habit data corresponding to the plurality of teaching videos;
calculating the similarity between learning habit data in the portrait data and teaching habit data corresponding to the plurality of teaching videos;
and determining the teaching video corresponding to the teaching habit data with the maximum similarity as the teaching video matched with the user.
Optionally, the apparatus for resource recommendation based on knowledge-graph further includes:
and the feedback updating module is used for receiving the teaching result of the teaching resource sent by the user terminal and updating the portrait data of the user according to the teaching result.
Optionally, the apparatus for resource recommendation based on knowledge-graph further includes:
the building module is used for building a knowledge graph and a teaching resource library, wherein nodes in the knowledge graph are associated with teaching resources in the teaching resource library, and the nodes in the knowledge graph are also associated with function data.
The specific implementation manner of the resource recommendation device based on the knowledge graph is basically the same as that of each embodiment of the resource recommendation method based on the knowledge graph, and is not described herein again.
The embodiment of the present application also provides a storage medium, where a resource recommendation program based on a knowledge graph is stored, and when executed by a processor, the resource recommendation program based on a knowledge graph implements the steps of the resource recommendation method based on a knowledge graph as described above.
The specific implementation of the storage medium of the present application is substantially the same as that of the above-mentioned resource recommendation method based on a knowledge graph, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present application may be substantially or partially embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A resource recommendation method based on a knowledge graph is characterized by comprising the following steps:
acquiring a target knowledge point to be queried by a user and portrait data of the user;
acquiring teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data;
and sending the teaching resources to the terminal of the user.
2. The method of claim 1, wherein the step of obtaining teaching resources matched with the user according to the target knowledge point to be queried and the representation data comprises:
acquiring a knowledge graph matched with the target knowledge point to be inquired according to the target knowledge point to be inquired;
determining knowledge points to be learned of the user from a knowledge map matched with the target knowledge points to be queried according to functional data and cognitive data in the portrait data;
and acquiring the teaching resources matched with the user from a teaching resource library based on the knowledge points to be learned of the user.
3. The method of claim 2, wherein the step of determining the knowledge points to be learned by the user from the knowledge map matching the target knowledge points to be queried according to the functional data and the cognitive data in the representation data comprises:
acquiring knowledge nodes related to the functional data from the knowledge graph matched with the target knowledge points to be inquired according to the functional data;
inquiring knowledge points corresponding to knowledge nodes associated with the functional data in the cognitive data;
if the knowledge point corresponding to the knowledge node associated with the functional data is stored in the cognitive data and the mastery degree of the knowledge point corresponding to the knowledge node associated with the functional data does not meet a preset condition, determining the knowledge point corresponding to the knowledge node associated with the functional data as the knowledge point to be learned by the user;
and if the knowledge points corresponding to the knowledge nodes associated with the functional data do not exist in the cognitive data, determining the knowledge points corresponding to the knowledge nodes associated with the functional data as the knowledge points to be learned by the user.
4. The knowledge-graph-based resource recommendation method of claim 2, wherein the step of obtaining teaching resources matching the user from a teaching resource library based on knowledge points to be learned by the user comprises:
and acquiring a plurality of teaching resources corresponding to the knowledge point to be learned from a teaching resource library, and selecting the teaching resource matched with the user from the plurality of teaching resources.
5. The knowledge-graph-based resource recommendation method of claim 4 wherein said pedagogical resource comprises a pedagogical video, and wherein said step of selecting from said plurality of pedagogical resources a pedagogical resource matching said user comprises:
acquiring teaching habit data corresponding to the plurality of teaching videos;
calculating the similarity between learning habit data in the portrait data and teaching habit data corresponding to the plurality of teaching videos;
and determining the teaching video corresponding to the teaching habit data with the maximum similarity as the teaching video matched with the user.
6. The method of resource recommendation based on knowledge-graph as claimed in claim 1, wherein after the step of sending the tutorial resource to the user's terminal, further comprising:
and receiving a teaching result of the teaching resource sent by the user terminal, and updating portrait data of the user according to the teaching result.
7. The method for resource recommendation based on knowledge-graph as claimed in claim 1, wherein the step of obtaining the target knowledge point to be queried by the user and the portrait data of the user is preceded by:
and constructing a knowledge graph and a teaching resource library, wherein nodes in the knowledge graph are associated with teaching resources in the teaching resource library, and the nodes in the knowledge graph are also associated with function data.
8. A resource recommendation device based on knowledge graph, characterized in that the resource recommendation device based on knowledge graph comprises:
the first acquisition module is used for acquiring a target knowledge point to be queried by a user and portrait data of the user;
the second acquisition module is used for acquiring teaching resources matched with the user according to the target knowledge point to be inquired and the portrait data;
and the sending module is used for sending the teaching resources to the terminal of the user.
9. A resource recommendation device based on a knowledge graph, the device comprising: a memory, a processor, and a knowledge-graph based resource recommendation program stored on the memory and executable on the processor, the knowledge-graph based resource recommendation program configured to implement the steps of the knowledge-graph based resource recommendation method of any one of claims 1 to 7.
10. A storage medium having a resource recommendation program based on a knowledge-graph stored thereon, wherein the resource recommendation program based on a knowledge-graph is executed by a processor to implement the steps of the resource recommendation method based on a knowledge-graph according to any one of claims 1 to 7.
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