CN110866848A - Learning method and device based on knowledge graph, electronic equipment and storage medium - Google Patents
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Abstract
The present application relates to the field of communications technologies, and in particular, to a learning method and apparatus based on a knowledge graph, an electronic device, and a storage medium. The learning method comprises the following steps: acquiring user characteristics uploaded by a terminal; determining the identity of a user according to the characteristics of the user and matching a knowledge graph corresponding to the user; the learning information corresponding to each node is sent to the terminal according to the weight value of each node of the knowledge graph; acquiring feedback information based on learning information uploaded by a terminal; and matching the feedback information with target feedback information corresponding to the learning information, and adjusting the weight value of the knowledge graph node corresponding to the learning information according to the matching result. The learning method and the learning device based on the knowledge graph dynamically set the weight values in the nodes corresponding to the knowledge graph through interaction with users, so that the knowledge graph corresponding to the users is continuously updated and perfected, the weight conditions of the users at the nodes are inferred through the weight values, and the nodes with insufficient weight values of the users are strengthened through the weight values.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a learning method and apparatus based on a knowledge graph, an electronic device, and a storage medium.
Background
In the rapidly evolving world today, the frequency of people's lives is beginning to accelerate. For families with children, education for children is insufficient because both parties are busy working. Or the learning of children is unbalanced and the knowledge is lost. Therefore, the educational problem for such children is concerned by many parents.
In view of the technical problems in the related art, no effective solution is provided at present.
Disclosure of Invention
In order to solve the technical problem or at least partially solve the technical problem, the present application provides a voice control method and apparatus.
In a first aspect, the present application provides a knowledge-graph-based learning method, comprising:
acquiring user characteristics uploaded by a terminal;
determining the identity of the user according to the user characteristics and matching a knowledge graph corresponding to the user;
sending learning information corresponding to each node to a terminal according to the weight value of each node of the knowledge graph;
acquiring feedback information uploaded by a terminal and based on the learning information;
and matching the feedback information with target feedback information corresponding to the learning information, and adjusting the weight value of the knowledge graph node corresponding to the learning information according to a matching result.
Further, before obtaining the user characteristics uploaded by the terminal, the method further includes:
acquiring voice data uploaded by a terminal;
semantic analysis is carried out on the voice data to obtain user intention information;
and when the user intention information is the target intention information, acquiring the user characteristics uploaded by the terminal.
Further, in the step of sending learning information corresponding to each node to the control terminal according to the weight value of each node of the knowledge graph: the quantity of the learning information corresponding to each node is positively correlated with the percentage of the weight value of the node in the total weight value of all the nodes.
Further, the step of adjusting the weight value of the knowledge graph node corresponding to the learning information according to the matching result includes:
if the feedback information is successfully matched with the target feedback information corresponding to the learning information, increasing the weight value of the knowledge graph node corresponding to the learning information;
and if the feedback information fails to be matched with the target feedback information corresponding to the learning information, reducing the weight value of the knowledge graph node corresponding to the learning information.
Furthermore, the weight values corresponding to the nodes of the pre-constructed knowledge graph are equal.
In a second aspect, the present application provides a knowledge-graph based learning device, comprising:
the acquisition module is used for acquiring voice data, user characteristics and feedback information uploaded by the terminal;
the matching module is used for determining the identity of the user according to the user characteristics and matching the knowledge graph corresponding to the user;
the first sending module is used for sending learning information corresponding to each node to a terminal according to the weight value of each node of the knowledge graph;
and the adjusting module is used for matching the feedback information with the target feedback information corresponding to the learning information and adjusting the weight value of the knowledge graph node corresponding to the learning information according to a matching result.
In a third aspect, the present application provides an electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor is configured to implement the learning method according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium storing computer instructions for causing a computer to perform the learning method of the first aspect.
In a fifth aspect, the present application provides a knowledge-graph-based learning method, comprising:
collecting user characteristics of a user and uploading the user characteristics to a server;
receiving learning information sent by a server and sending the learning information to a client;
and acquiring feedback information of the user based on the learning information and uploading the feedback information to a server.
In a sixth aspect, the present application provides a knowledge-graph based learning device, comprising:
the acquisition module is used for acquiring user characteristics of the user and feedback information based on the learning information and uploading the feedback information to the server;
the receiving module is used for receiving the learning information sent by the server;
and the second sending module is used for sending the learning information to the client.
In a seventh aspect, the present application provides an electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor is configured to, when executing the computer program, implement the knowledge-graph-based learning method of the fifth aspect.
In an eighth aspect, the present application provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method for knowledge-graph based learning of the fifth aspect.
The learning method and the learning device based on the knowledge graph dynamically set the weight values in the nodes corresponding to the knowledge graph through interaction with users, so that the knowledge graph corresponding to the users is continuously updated and perfected, the weight conditions of the users at the nodes are inferred through the weight values, and the nodes with insufficient weight values of the users are strengthened through the weight values.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings 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 that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a learning method based on a knowledge graph applied to a server according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method of some steps before step S1 shown in FIG. 1;
fig. 3 is a schematic structural diagram of functional modules of a learning apparatus applied to a server according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device applied to a server according to an embodiment of the present disclosure;
fig. 5 is a flowchart illustrating a knowledge graph-based learning method applied to a terminal according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a functional module of a learning apparatus applied to a terminal according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device applied to a terminal according to an embodiment of the present disclosure; and
fig. 8 is a schematic diagram of a method for implementing child learning by the cooperation of a server and a terminal.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a learning method based on a knowledge graph, applied to a server side, according to an embodiment of the present application, and includes steps S110 to S510 as follows:
s110: acquiring user characteristics uploaded by a terminal;
the acquisition of the user characteristics is performed by the terminal, wherein the user characteristics are used for representing the matching characteristics of the current user, the user characteristics include but are not limited to voiceprint characteristics or facial characteristics, and generally the acquisition is an analog signal, and the analog signal is converted into a digital signal and then uploaded to the cloud server, for example: the terminal can be selected as an air conditioner, when the user interacts with the air conditioner, the air conditioner can collect voiceprint features or facial features of the user within the collection range of the air conditioner, and the voiceprint features or the facial features are uploaded to a cloud server through a network;
s210: determining the identity of the user according to the user characteristics and matching a knowledge graph corresponding to the user;
specifically, after obtaining the user characteristics, the cloud server can compare the user characteristics with the prestored user characteristics of different users in the database, and can confirm the specific identity of the user through identification processing, a knowledge graph corresponding to the user corresponds to each user in the database, and the server can automatically match the knowledge graph of the user according to the identified user identity; for example, still taking the air conditioner as an example of the terminal, the air conditioner acquires voiceprint features of zhang san of the user and uploads the voiceprint features to the cloud server, the cloud server compares the voiceprint features of zhang san, lie si, wang wu and other users in the database with voiceprint features acquired from the air conditioner, and when the voiceprint features acquired from the air conditioner are successfully compared with the voiceprint features of zhang san of the database, the server automatically matches a knowledge graph corresponding to zhang san of the database.
S310: sending learning information corresponding to each node to a terminal according to the weight value of each node of the knowledge graph;
specifically, each node of a knowledge graph in the server has a corresponding weight value, the knowledge graph stores relationship information between entities by applying a graph theory, and the weight value of each node reflects the weight relationship between the entities corresponding to each node; each node corresponds to a large amount of learning information, wherein the learning information includes but is not limited to question and answer questions, hearing identification, recitation requirements and the like, the server sends the learning information corresponding to each node to the terminal according to the weight value of each node of the knowledge graph, the specific logic for sending the learning information may be to preferentially send the learning information corresponding to the node with a large weight value, or to preferentially send the learning information corresponding to the node with a small weight value, or may be configured such that the number of the learning information corresponding to each node sent by the server to the terminal is positively correlated with the percentage of the weight value of the node in the total weight values of all the nodes, for example, if there are four nodes in the knowledge graph and the weight ratio of the four nodes is 3:1:5:2, the server may also send the number of learning information corresponding to each node to the terminal according to the ratio of 3:1:5: 2.
It should be noted that after the knowledge graph is constructed for the first time, the weight values corresponding to the nodes are equal, and when the knowledge graph is used for the first time, the learning information corresponding to the nodes may be randomly sent to the terminal in step S310.
S410: acquiring feedback information uploaded by a terminal and based on the learning information;
specifically, the feedback information is collected by the terminal, the terminal receives the learning information sent by the server and then sends the learning information to the client, and the client feeds back the learning information based on the obtained learning information, for example, if the learning information received by the client is a question, the feedback information of the client is an answer to the question considered by the client, a correct answer to the question is certainly stored in the server, and the correct answer is target feedback information; if the learning information received by the client is the hearing recognition, the feedback information of the client is the repeatedly heard content of the client, and the server stores the corresponding content of the hearing recognition, and the corresponding content is the target feedback information.
S510: and matching the feedback information with target feedback information corresponding to the learning information, and adjusting the weight value of the knowledge graph node corresponding to the learning information according to a matching result.
As an optional implementation manner, in step S510, if the feedback information is successfully matched with the target feedback information corresponding to the learning information, increasing the weight value of the knowledge graph node corresponding to the learning information; and if the feedback information fails to be matched with the target feedback information corresponding to the learning information, reducing the weight value of the knowledge graph node corresponding to the learning information. Through the interaction between the terminal and the users, the weighted values are dynamically set in the corresponding nodes, so that the knowledge graph of the corresponding users is continuously updated and perfected. The weight conditions of the user at the nodes are inferred through the weight values, and the nodes with insufficient weight values of the user are strengthened through the weight values.
In some embodiments, as shown in fig. 2, before acquiring the user feature uploaded by the terminal, the method further includes:
s101: acquiring voice data uploaded by a terminal;
s102: semantic analysis is carried out on the voice data to obtain user intention information;
s103: and when the user intention information is the target intention information, acquiring the user characteristics uploaded by the terminal.
The embodiment has the effect that the user intention information is obtained according to the voice data uploaded by the terminal, and the server can obtain the user characteristics through the terminal only when the client intention information is the target intention information. For example: when a user says that 'I want to play a game' to the terminal, the terminal uploads the voice information of the user to the cloud server, the cloud server obtains user intention information by using NLP semantic analysis, judges whether the intention is target intention information, and if yes, the cloud server obtains user characteristics through the terminal.
As shown in fig. 3, according to another embodiment of the present application, there is also provided a knowledge-graph-based learning apparatus including:
the acquisition module 11 is used for acquiring voice data, user characteristics and feedback information uploaded by a terminal;
the matching module 12 is used for determining the user identity according to the user characteristics and matching the knowledge graph corresponding to the user;
a first sending module 13, configured to send learning information corresponding to each node to a terminal according to a weight value of each node of the knowledge graph;
and the adjusting module 14 is configured to match the feedback information with target feedback information corresponding to the learning information, and adjust a weight value of a knowledge graph node corresponding to the learning information according to a matching result.
Specifically, the specific process of implementing the functions of each module in the apparatus according to the embodiment of the present invention may refer to the related description in the method embodiment, and is not described herein again.
As shown in fig. 4, according to another embodiment of the present application, there is provided an electronic device, provided on a server side, including: the system comprises a processor 1501, a communication interface 1502, a memory 1503 and a communication bus 1504, wherein the processor 1501, the communication interface 1502 and the memory 1503 complete communication with each other through the communication bus 1504.
A memory 1503 for storing a computer program;
the processor 1501 is configured to implement the steps of the method embodiment applied to the server side when executing the program stored in the memory 1503.
The embodiment of the present application further provides a computer-readable storage medium, which is disposed at the server side and stores computer instructions, and the computer instructions enable the computer to execute the steps of the method embodiment applied to the server side.
As shown in fig. 5, according to another embodiment of the present application, there is also provided a knowledge-graph-based learning method applied to a terminal, including the following steps S100 to S300:
s100: collecting user characteristics of a user and uploading the user characteristics to a server;
s200: receiving learning information sent by a server and sending the learning information to a client;
s300: and acquiring feedback information of the user based on the learning information and uploading the feedback information to a server.
Specifically, the specific implementation process of each step in the method according to the embodiment of the present invention may refer to the related description applied to the method embodiment of the server side, and is not described herein again.
As shown in fig. 6, according to another embodiment of the present application, there is provided a learning apparatus based on knowledge-graph, applied to a terminal, including:
the acquisition module 21 is used for acquiring user characteristics of the user and feedback information based on the learning information and uploading the feedback information to the server;
a receiving module 22, configured to receive learning information sent by a server;
and a second sending module 23, configured to send the learning information to the client.
Specifically, the specific implementation process of each step in the method of the embodiment of the present invention is consistent with the content in the method embodiment applied to the terminal, and is not described herein again.
According to another embodiment of the present application, there is also provided an electronic device applied to a terminal, including: as shown in fig. 7, the electronic device may include: the system comprises a processor 2401, a communication interface 2402, a memory 2403 and a communication bus 2404, wherein the processor 2401, the communication interface 2402 and the memory 2403 are communicated with each other through the communication bus 2404.
A memory 2403 for storing a computer program;
the processor 2401 is configured to implement the steps of the above-described method embodiments when executing the program stored in the memory 2403.
The embodiment of the present application further provides a computer-readable storage medium, which is applied to a terminal, and the computer-readable storage medium stores computer instructions, and the computer instructions enable a computer to execute the steps of the method embodiment.
As shown in fig. 8, a specific example is given in which the server side and the terminal cooperate with each other to implement a child learning method. Through classifying knowledge disciplines related to children education and refining the knowledge disciplines, a corresponding knowledge map is established by combining the knowledge or learning conditions of children in the knowledge disciplines. For example: the subjects of children education include languages such as Chinese, mathematics and English. Secondly, subdividing the categories, such as Chinese into poems and modern texts; mathematical principles are subdivided into additions, subtractions, multiplications, divisions, and the like. Further, the division may be divided into addition, subtraction, multiplication and division within 10 or within and outside 100. The lowest level of subdivision hierarchy is the nodes of the knowledge graph mentioned in the application, weights are set on all the nodes, the knowledge graph corresponding to the children is established, the mastering conditions of the children in the knowledge fields are inferred according to the weight values of the corresponding nodes, and the children are educated in the knowledge fields with smaller weight ratio through the weight values. When a child says that 'i want to play a game' to an electrical device (a voice inlet), the electrical device uploads voice information of the user to a cloud server, the cloud server obtains user intention information by using NLP semantic analysis, the intention is judged to be learned through game interaction, the intention is successfully matched with target intention information, the cloud server collects facial features or voiceprint features of the user through the electrical device, the user features of the user are uploaded to the cloud server, the cloud server identifies the user features, the identification result is that the user is child 1, and the cloud server retrieves the knowledge graph corresponding to the child 1 which is established before. Through searching, it is found that the children 1 are in the mathematical field, the weight proportion of the addition within 100 in the addition is small, namely the children are not known by the addition within 100 and are not skilled, the server sends the questions with the addition within 100 to the regular equipment part, the electric equipment broadcasts the questions to the children 1 through voice equipment, the responses of the children 1 based on the questions are collected through the electric equipment and uploaded to the server, the server obtains the user responses, corresponding results are compared, if the responses are wrong for multiple times (such as more than 3 times), the cloud server reduces the weight value of the addition within 100 in the knowledge graph corresponding to the children, and if the responses are correct, the weight value is increased correspondingly. So reciprocal, the weight in different knowledge fields accounts for than to have dynamic change, and when children returned the restart recreation interdynamic down, electrical equipment combined cloud server can play the purpose of educating children through the knowledge map.
The bus mentioned in the electronic device applied to the server side or the terminal may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (12)
1. A learning method based on knowledge graph is characterized by comprising the following steps:
acquiring user characteristics uploaded by a terminal;
determining the identity of the user according to the user characteristics and matching a knowledge graph corresponding to the user;
sending learning information corresponding to each node to a terminal according to the weight value of each node of the knowledge graph;
acquiring feedback information uploaded by a terminal and based on the learning information;
and matching the feedback information with target feedback information corresponding to the learning information, and adjusting the weight value of the knowledge graph node corresponding to the learning information according to a matching result.
2. The knowledge-graph-based learning method according to claim 1, wherein before obtaining the user characteristics uploaded by the terminal, the method further comprises:
acquiring voice data uploaded by a terminal;
semantic analysis is carried out on the voice data to obtain user intention information;
and when the user intention information is the target intention information, acquiring the user characteristics uploaded by the terminal.
3. The knowledge graph-based learning method according to claim 1, wherein in the step of sending learning information corresponding to each node to a control terminal according to the weight value of each node of the knowledge graph: the quantity of the learning information corresponding to each node is positively correlated with the percentage of the weight value of the node in the total weight value of all the nodes.
4. The knowledge-graph-based learning method according to claim 1, wherein the step of adjusting the weight values of the knowledge-graph nodes corresponding to the learning information according to the matching result comprises:
if the feedback information is successfully matched with the target feedback information corresponding to the learning information, increasing the weight value of the knowledge graph node corresponding to the learning information;
and if the feedback information fails to be matched with the target feedback information corresponding to the learning information, reducing the weight value of the knowledge graph node corresponding to the learning information.
5. The knowledge graph-based learning method according to claim 1, wherein the weight values corresponding to the nodes of the pre-constructed knowledge graph are equal.
6. A knowledge-graph-based learning apparatus, comprising:
the acquisition module is used for acquiring voice data, user characteristics and feedback information uploaded by the terminal;
the matching module is used for determining the identity of the user according to the user characteristics and matching the knowledge graph corresponding to the user;
the first sending module is used for sending learning information corresponding to each node to a terminal according to the weight value of each node of the knowledge graph;
and the adjusting module is used for matching the feedback information with the target feedback information corresponding to the learning information and adjusting the weight value of the knowledge graph node corresponding to the learning information according to a matching result.
7. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor, when executing the computer program, implementing the learning method of any one of claims 1-5.
8. A computer-readable storage medium storing computer instructions for causing a computer to perform the learning method of any one of claims 1-5.
9. A learning method based on knowledge graph is characterized by comprising the following steps:
collecting user characteristics of a user and uploading the user characteristics to a server;
receiving learning information sent by a server and sending the learning information to a client;
and acquiring feedback information of the user based on the learning information and uploading the feedback information to a server.
10. A knowledge-graph-based learning apparatus, comprising:
the acquisition module is used for acquiring user characteristics of the user and feedback information based on the learning information and uploading the feedback information to the server;
the receiving module is used for receiving the learning information sent by the server;
and the second sending module is used for sending the learning information to the client.
11. An electronic device, comprising: the system comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
the memory is used for storing a computer program;
the processor, when executing the computer program, implements the knowledge-graph based learning method of claim 9.
12. A computer-readable storage medium storing computer instructions for causing a computer to perform the knowledge-graph based learning method of claim 9.
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| WO2018006469A1 (en) * | 2016-07-07 | 2018-01-11 | 深圳狗尾草智能科技有限公司 | Knowledge graph-based human-robot interaction method and system |
| CN109389870A (en) * | 2017-08-10 | 2019-02-26 | 亿度慧达教育科技（北京）有限公司 | A kind of data adaptive method of adjustment and its device applied in electronic instruction |
| CN109522420A (en) * | 2018-11-16 | 2019-03-26 | 广东小天才科技有限公司 | Method and system for acquiring learning demand |
| CN109636693A (en) * | 2018-12-20 | 2019-04-16 | 广东小天才科技有限公司 | Exercise question recommendation method and electronic device |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113066327A (en) * | 2021-04-13 | 2021-07-02 | 黑龙江中医药大学 | Online intelligent education method for college students |
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