CN108255962A - Knowledge Relation method, apparatus, storage medium and electronic equipment - Google Patents
Knowledge Relation method, apparatus, storage medium and electronic equipment Download PDFInfo
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- CN108255962A CN108255962A CN201711407886.1A CN201711407886A CN108255962A CN 108255962 A CN108255962 A CN 108255962A CN 201711407886 A CN201711407886 A CN 201711407886A CN 108255962 A CN108255962 A CN 108255962A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Abstract
This disclosure relates to which a kind of Knowledge Relation method, apparatus, storage medium and electronic equipment, are related to information technology field, this method includes：Obtain the degree of correlation about specified knowledge point information between the first knowledge point and the second knowledge point, first knowledge point and the second knowledge point are any two knowledge points in default knowledge point range, when the degree of correlation is greater than or equal to preset relevance threshold, it determines the first knowledge point and the second knowledge point correlated knowledge point each other, can be associated knowledge point according to the degree of correlation.
Description
Technical field
This disclosure relates to information technology field, and in particular, to a kind of Knowledge Relation method, apparatus, storage medium and
Electronic equipment.
Background technology
With the continuous development of information technology, educational mode is also more diversified, and many schools or student employ
CAI means of the interactive educational system of line as existing educational mode, because including a large amount of religion in educational system
Resource, such as Exercise Library, online course, knowledge point collection of illustrative plates etc. are learned, teaching range can be extended, consolidates the content of courses, improves religion
Learn quality.But be independent from each other between the knowledge point in current tutoring system, lack to subject entirety knowledge train of thought
It holds, therefore when user is when imparting knowledge to students or learning a certain knowledge point, can not obtain and the relevant teaching resource in the knowledge point, influence
The using effect of educational system.
Invention content
The purpose of the disclosure is to provide a kind of Knowledge Relation method, apparatus, storage medium and electronic equipment, to solve
Knowledge point is mutual indepedent in educational system, lacks the problem of being associated with.
To achieve these goals, according to the embodiment of the present disclosure in a first aspect, providing a kind of Knowledge Relation method, institute
The method of stating includes：
Obtain the degree of correlation about specified knowledge point information between the first knowledge point and the second knowledge point, first knowledge
Point and second knowledge point are any two knowledge points in default knowledge point range；
When the degree of correlation is greater than or equal to preset relevance threshold, first knowledge point and described second are determined
Knowledge point correlated knowledge point each other.
Optionally, the specified knowledge point information includes wrong topic rate and/or knowledge point contents, the first knowledge point of the acquisition
The degree of correlation between the second knowledge point about specified knowledge point information, including：
Obtain the degree of correlation about wrong topic rate between first knowledge point and second knowledge point；Alternatively,
Obtain the degree of correlation about knowledge point contents between first knowledge point and second knowledge point；Alternatively,
It obtains between first knowledge point and second knowledge point about the degree of correlation of wrong topic rate and about knowledge point
The degree of correlation of content；
Determine that described first knows according to the degree of correlation about wrong topic rate and the degree of correlation about knowledge point contents
Know the degree of correlation of the point with second knowledge point.
Optionally, when the specified knowledge point information is wrong topic rate, the first knowledge point of the acquisition and the second knowledge point it
Between the degree of correlation about wrong topic rate, including：
Obtain the first wrong topic rate sequence of first knowledge point and the second wrong topic rate sequence of second knowledge point, institute
It states the first wrong topic rate sequence and includes each wrong topic rate of the student on first knowledge point in the first student set, described second
Wrong topic rate sequence includes each wrong topic rate of the student on second knowledge point in first student set；
According to the described first wrong topic rate sequence and the second wrong topic rate sequence, the wrong topic rate of first knowledge point is obtained
Standard deviation, the mistake between the wrong topic rate standard deviation of second knowledge point and first knowledge point and second knowledge point
Topic rate covariance；
According to the wrong topic rate standard deviation, the wrong topic rate standard deviation of second knowledge point, Yi Jisuo of first knowledge point
Wrong topic rate covariance is stated, obtains the degree of correlation about the wrong topic rate between first knowledge point and second knowledge point.
Optionally, the wrong topic rate standard deviation according to first knowledge point, second knowledge point wrong topic rate mark
Accurate poor and described wrong topic rate covariance is obtained between first knowledge point and second knowledge point about the wrong topic
The degree of correlation of rate, including：
According to the wrong topic rate standard deviation, the wrong topic rate standard deviation of second knowledge point, Yi Jisuo of first knowledge point
Wrong topic rate covariance is stated, is obtained using the first relatedness computation formula and is closed between first knowledge point and second knowledge point
In the degree of correlation of the wrong topic rate；
Wherein, the first relatedness computation formula includes：
Wherein, ρXYRepresent the similarity of knowledge point X and knowledge point Y, Cov (X, Y) represents the knowledge point X and the knowledge
Wrong topic rate covariance between point Y, σXRepresent the wrong topic rate standard deviation of the knowledge point X, σYRepresent the wrong topic of the knowledge point Y
Rate standard deviation.
Optionally, when the specified knowledge point information is knowledge point contents, the first knowledge point of the acquisition and the second knowledge
The degree of correlation between point about the knowledge point contents, including：
The content of content and second knowledge point to first knowledge point segments, and obtains first knowledge
The keyword of point and the keyword of second knowledge point, the keyword include one or more words；
According to the keyword of first knowledge point and the keyword of second knowledge point, first knowledge point is obtained
The degree of correlation between second knowledge point about the knowledge point contents.
Optionally, the method further includes：
Recommendation information is exported, the recommendation information is used to recommend institute of second knowledge point as first knowledge point
State correlated knowledge point；
Obtain the associated instructions for second knowledge point, the associated instructions include confirming second knowledge point with
The affirmative instruction of first Knowledge Relation confirms second knowledge point and the not associated negative in first knowledge point
Instruction；
When the associated instructions is the instructions certainly, recommend topic corresponding with second knowledge point.
Optionally, the method further includes：
Whenever determining that any topic of any user in first knowledge point is lost points, according to the degree of correlation to described
Any user pushes the topic and/or course about second knowledge point；
Multiple users are counted for push about the topic of second knowledge point and/or the use rate of course, are obtained
Using rate sequence, the association includes that the multiple user is corresponding described to use rate using rate sequence for association；
Obtain variance of the association using rate sequence；
When the association is greater than or equal to preset variance threshold values using the variance of rate sequence, the degree of correlation is adjusted.
Optionally, the method further includes：
According to the association using rate sequence, the relevance threshold is updated.
Optionally, it is described that rate sequence is used according to the association, the relevance threshold is updated, including：
When the association is greater than or equal to preset expectation threshold value using the expectation of rate sequence, the degree of correlation threshold is reduced
Value；
When described be associated with using rate sequence desirably less than preset expectation threshold value, the relevance threshold is increased.
According to the second aspect of the embodiment of the present disclosure, a kind of Knowledge Relation device is provided, described device includes：
Acquisition module, for obtaining between the first knowledge point and the second knowledge point about the related of specified knowledge point information
Degree, first knowledge point and second knowledge point are any two knowledge points in default knowledge point range；
Relating module, for when the degree of correlation is greater than or equal to preset relevance threshold, determining that described first knows
Know point and second knowledge point correlated knowledge point each other.
Optionally, the specified knowledge point information includes wrong topic rate and/or knowledge point contents, the acquisition module include：
First acquisition submodule, for obtaining between first knowledge point and second knowledge point about wrong topic rate
The degree of correlation；
Second acquisition submodule, for obtaining between first knowledge point and second knowledge point about in knowledge point
The degree of correlation of appearance；
Third acquisition submodule, for obtaining between first knowledge point and second knowledge point about wrong topic rate
The degree of correlation and the degree of correlation about knowledge point contents；
The third acquisition submodule is additionally operable to according to the degree of correlation about wrong topic rate and described about in knowledge point
The degree of correlation of appearance determines the degree of correlation of first knowledge point and second knowledge point.
Optionally, when the specified knowledge point information is wrong topic rate, first acquisition submodule is used for：
Obtain the first wrong topic rate sequence of first knowledge point and the second wrong topic rate sequence of second knowledge point, institute
It states the first wrong topic rate sequence and includes each wrong topic rate of the student on first knowledge point in the first student set, described second
Wrong topic rate sequence includes each wrong topic rate of the student on second knowledge point in first student set；
According to the described first wrong topic rate sequence and the second wrong topic rate sequence, the wrong topic rate of first knowledge point is obtained
Standard deviation, the mistake between the wrong topic rate standard deviation of second knowledge point and first knowledge point and second knowledge point
Topic rate covariance；
According to the wrong topic rate standard deviation, the wrong topic rate standard deviation of second knowledge point, Yi Jisuo of first knowledge point
Wrong topic rate covariance is stated, obtains the degree of correlation about the wrong topic rate between first knowledge point and second knowledge point.
Optionally, the wrong topic rate standard deviation according to first knowledge point, second knowledge point wrong topic rate mark
Accurate poor and described wrong topic rate covariance is obtained between first knowledge point and second knowledge point about the wrong topic
The degree of correlation of rate, including：
According to the wrong topic rate standard deviation, the wrong topic rate standard deviation of second knowledge point, Yi Jisuo of first knowledge point
Wrong topic rate covariance is stated, is obtained using the first relatedness computation formula and is closed between first knowledge point and second knowledge point
In the degree of correlation of the wrong topic rate；
Wherein, the first relatedness computation formula includes：
Wherein, ρXYRepresent the similarity of knowledge point X and knowledge point Y, Cov (X, Y) represents the knowledge point X and the knowledge
Wrong topic rate covariance between point Y, σXRepresent the wrong topic rate standard deviation of the knowledge point X, σYRepresent the wrong topic of the knowledge point Y
Rate standard deviation.
Optionally, when the specified knowledge point information is knowledge point contents, second acquisition submodule is used for：
The content of content and second knowledge point to first knowledge point segments, and obtains first knowledge
The keyword of point and the keyword of second knowledge point, the keyword include one or more words；
According to the keyword of first knowledge point and the keyword of second knowledge point, first knowledge point is obtained
The degree of correlation between second knowledge point about the knowledge point contents.
Optionally, described device further includes：
Output module, for exporting recommendation information, the recommendation information is used to recommend described in the second knowledge point conduct
The correlated knowledge point of first knowledge point；
Instruction acquisition module, for obtaining the associated instructions for second knowledge point, the associated instructions include true
Recognize the affirmative instruction of second knowledge point and first Knowledge Relation or confirm second knowledge point and described first
Knowledge point is not associated to ignore instruction；
Recommending module, for when the associated instructions is the instructions certainly, recommending corresponding with second knowledge point
Topic.
Optionally, described device further includes：
Pushing module, for whenever determining that any topic of any user in first knowledge point is lost points, according to institute
State topic and/or course of the degree of correlation to any user push about second knowledge point；
Statistical module, for counting topic and/or course about second knowledge point of multiple users for push
Use rate, obtain association using rate sequence, the association using rate sequence include the multiple user it is corresponding described in adopt
With rate；
Variance acquisition module, for obtaining variance of the association using rate sequence；
Module is adjusted, for when the association is greater than or equal to preset variance threshold values using the variance of rate sequence, adjusting
The whole degree of correlation.
Optionally, described device further includes：
Update module, for, using rate sequence, updating the relevance threshold according to the association.
Optionally, the update module includes：
First update submodule is greater than or equal to preset expectation threshold value for working as the association using the expectation of rate sequence
When, reduce the relevance threshold；
Second update submodule, for when the association using rate sequence desirably less than preset expectation threshold value when, rise
The high relevance threshold.
According to the third aspect of the embodiment of the present disclosure, a kind of computer readable storage medium is provided, is stored thereon with calculating
The step of machine program, the Knowledge Relation method that realization first aspect provides when which is executed by processor.
According to the fourth aspect of the embodiment of the present disclosure, a kind of electronic equipment is provided, including：The computer that the third aspect provides
Readable storage medium storing program for executing；And one or more processors, for performing the computer journey in the computer readable storage medium
Sequence.
Through the above technical solutions, the specified knowledge of any two knowledge point of the disclosure in default knowledge point range
Point information, determines the degrees of correlation of two knowledge points, further, according to the degree of correlation of two knowledge points and preset degree of correlation threshold
Whether each other value, judge two knowledge points correlated knowledge point, when the degree of correlation of two knowledge points is greater than or equal to relevance threshold
When, it determines two knowledge points correlated knowledge point each other, can be associated knowledge point according to the degree of correlation.
Other feature and advantage of the disclosure will be described in detail in subsequent specific embodiment part.
Description of the drawings
Attached drawing is for providing further understanding of the disclosure, and a part for constitution instruction, with following tool
Body embodiment is used to explain the disclosure, but do not form the limitation to the disclosure together.In the accompanying drawings：
Fig. 1 is the flow chart according to a kind of Knowledge Relation method shown in an exemplary embodiment；
Fig. 2 is the flow chart according to another Knowledge Relation method shown in an exemplary embodiment；
Fig. 3 is the flow chart according to another Knowledge Relation method shown in an exemplary embodiment；
Fig. 4 is the flow chart according to another Knowledge Relation method shown in an exemplary embodiment；
Fig. 5 is the flow chart according to another Knowledge Relation method shown in an exemplary embodiment；
Fig. 6 is the flow chart according to another Knowledge Relation method shown in an exemplary embodiment；
Fig. 7 is the block diagram according to a kind of Knowledge Relation device shown in an exemplary embodiment；
Fig. 8 is the block diagram according to another Knowledge Relation device shown in an exemplary embodiment；
Fig. 9 is the block diagram according to another Knowledge Relation device shown in an exemplary embodiment；
Figure 10 is the block diagram according to another Knowledge Relation device shown in an exemplary embodiment；
Figure 11 is the block diagram according to another Knowledge Relation device shown in an exemplary embodiment；
Figure 12 is the block diagram according to another Knowledge Relation device shown in an exemplary embodiment；
Figure 13 is the block diagram according to a kind of electronic equipment shown in an exemplary embodiment.
Specific embodiment
Here exemplary embodiment will be illustrated in detail, example is illustrated in the accompanying drawings.Following description is related to
During attached drawing, unless otherwise indicated, the same numbers in different attached drawings represent the same or similar element.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistent with the disclosure.On the contrary, they be only with it is such as appended
The example of the consistent device and method of some aspects be described in detail in claims, the disclosure.
Before Knowledge Relation method, apparatus, storage medium and the electronic equipment of disclosure offer is introduced, first to this
Application scenarios involved by disclosing each embodiment are introduced.The application scenarios can give financial aid to students raw or teacher study or religion supplemented by
The educational system of Line interaction, the educational system can be arranged in terminal, and user needs to use by terminal selection
Knowledge point range, so as to correspond to the teaching resource of the knowledge point of range, such as topic, course etc. in the system of acquiring an education.It should
Educational system also is able to acquisition user in use in the case of feedack, such as the answer of topic, to course whether
It is interested, if it is satisfied etc., so as to push more suitably teaching resource to user.Wherein, which for example can be intelligence
Mobile phone, tablet computer, smart television, smartwatch, PDA (Personal Digital Assistant, personal digital assistant),
The fixed terminals such as the mobile terminals such as pocket computer or desktop computer.
Fig. 1 is according to a kind of flow chart of Knowledge Relation method shown in an exemplary embodiment, as shown in Figure 1, should
Method includes：
Step 101, the degree of correlation between the first knowledge point of acquisition and the second knowledge point about specified knowledge point information, first
Knowledge point and the second knowledge point are any two knowledge points in default knowledge point range.
For example, user can select knowledge point range when using educational system according to the actual demand of oneself,
Such as the mathematics class of five grades, the history of the Ming Dynasty or a high physical mechanics, the first knowledge point and the second knowledge point are default knowledge
Any two knowledge point in point range, by taking a high physical mechanics as an example, the first knowledge point can be the three elements of power, second know
It can be the forst law of motion to know point.According to the specified knowledge point information of the first knowledge point and the second knowledge point, obtain first and know
Know the degree of correlation between point and the second knowledge point.Wherein, specify knowledge point information that can include attribute, the correspondence of each knowledge point
Topic information, significance level etc., local can be stored in by way of advance typing or is stored in far-end server, also
Study and the education informations of user's reality can be acquired, specified knowledge point information is updated and adjusted.
Step 102, when the degree of correlation is greater than or equal to preset relevance threshold, the first knowledge point and the second knowledge are determined
Put correlated knowledge point each other.
For example, the degree of correlation that can be according to two knowledge points and preset relevance threshold, to judge two knowledge
Whether each other point correlated knowledge point when the degree of correlation of two knowledge points is greater than or equal to relevance threshold, determines two knowledge
Correlated knowledge point each other is put, when the degree of correlation of two knowledge points is less than relevance threshold, it is not related to determine two knowledge points
Knowledge point.Wherein, relevance threshold can be according to related field expert (such as can be the senior teacher of a certain subject etc.)
Opinion (such as could be provided as 0.8) for pre-setting, can also be adjusted according to the actual demand of user, can be with root
It is updated according to the particular condition in use of user.
In conclusion the specified knowledge point information of any two knowledge point of the disclosure in default knowledge point range,
It determines the degree of correlation of two knowledge points, further, according to the degree of correlation of two knowledge points and preset relevance threshold, judges
Whether each other two knowledge points correlated knowledge point when the degree of correlation of two knowledge points is greater than or equal to relevance threshold, determines
Knowledge point, can be associated by two knowledge points correlated knowledge point each other according to the degree of correlation.
Fig. 2 be according to the flow chart of another Knowledge Relation method shown in an exemplary embodiment, as shown in Fig. 2,
Specified knowledge point information, which can include wrong topic rate and/or knowledge point contents, corresponding step 101, can include three kinds of realization sides
Formula, corresponding execution step 1011 respectively, 1012 or 1013 to 1014, wherein：The knowledge point information performs step when being wrong topic rate
Rapid 1011, step 1012 is performed when which is knowledge point contents, which includes wrong topic rate and include again
During knowledge point contents, step 1013 or 1014 is performed.
Step 1011, the degree of correlation between the first knowledge point and the second knowledge point about wrong topic rate is obtained.
Step 1012, the degree of correlation between the first knowledge point and the second knowledge point about knowledge point contents is obtained.
Step 1013, it obtains between the first knowledge point and the second knowledge point about the degree of correlation of wrong topic rate and about knowledge point
The degree of correlation of content.
Step 1014, the first knowledge point is determined according to the degree of correlation about wrong topic rate and the degree of correlation about knowledge point contents
With the degree of correlation of the second knowledge point.
It hereafter illustrates by default knowledge point range of a high physical mechanics, the first knowledge point is the three elements of power, second knows
It is that the forst law of motion illustrates to know point：
When specified knowledge point information is wrong topic rate, step 1011 can be accomplished by the following way：
First, the first of the first knowledge point of acquisition the wrong topic rate sequence and the second wrong topic rate sequence of the second knowledge point, first
Wrong topic rate sequence includes each wrong topic rate of the student on the first knowledge point in the first student set, and the second wrong topic rate sequence includes
Each wrong topic rate of the student on the second knowledge point in first student set.
Exemplary, the first student set is a sampling set of wrong topic rate, can be whole students of a class,
Can be the student of entire grade, by taking the first student collection is combined into high whole students of class one by one as an example, correspondingly, the first wrong topic rate
Include each student of high class one by one in sequence in the three elements correlation exercise of study idea, the probability for the topic that does wrong, second
Include each student of high class one by one in wrong topic rate sequence when learning forst law of motion correlation exercise, the topic that does wrong it is general
Rate.It should be noted that the first wrong topic rate sequence and the second wrong topic rate sequence can also be defined in certain period of time each
The wrong topic rate of student, for example, can select the period of a high last term or select in certain school 3 years, each high one goes to school
The time range of phase.Shown in table 1 is the first wrong topic rate and the second wrong topic rate of high 6 students of class one by one, wherein the
Two row represent the first wrong topic rate sequence, and third row represent the second wrong topic rate sequence：
Table 1
| Student numbers | First knowledge point mistake topic rate | Second knowledge point mistake topic rate |
| 1 | 35% | 42% |
| 2 | 15% | 21% |
| 3 | 69% | 80% |
| 4 | 28% | 37% |
| 5 | 2% | 3% |
| 6 | 41% | 29% |
Secondly, according to the first wrong topic rate sequence and the second wrong topic rate sequence, the wrong topic rate standard deviation of the first knowledge point is obtained,
Wrong topic rate covariance between the wrong topic rate standard deviation of second knowledge point and the first knowledge point and the second knowledge point.
It is exemplary, by taking the first wrong topic rate sequence and the second wrong topic rate sequence in table 1 as an example, then can know in the hope of first
The wrong topic rate standard deviation for knowing point is 0.231, and the wrong topic rate standard deviation 0.258 of the second knowledge point and the first knowledge point are known with second
Know the wrong topic rate covariance 0.056 between point.
Finally, according to the wrong topic rate standard deviation of the first knowledge point, the wrong topic rate standard deviation of the second knowledge point and wrong topic rate
Covariance obtains the degree of correlation about wrong topic rate between the first knowledge point and the second knowledge point.
Further, can utilize the first relatedness computation formula obtain between the first knowledge point and the second knowledge point about
The degree of correlation of wrong topic rate：
Wherein, which can be：
Wherein, ρXYRepresent the similarity of knowledge point X and knowledge point Y, Cov (X, Y) represents the knowledge point X and the knowledge
Wrong topic rate covariance between point Y, σXRepresent the wrong topic rate standard deviation of knowledge point X, σYRepresent the wrong topic rate standard deviation of knowledge point Y.
It is exemplary, bring the data in table 1 into first relatedness computation formula, Cov (X, Y) is 0.056, σXIt is 0.231,
σYIt is 0.258, it can be in the hope of ρXYBe 0.94, by relevance threshold for for 0.8, then the first knowledge point and the second knowledge point it
Between about the degree of correlation of wrong topic rate be more than relevance threshold, it may be determined that the first knowledge point and the second knowledge point relevant knowledge each other
Point.
When specified knowledge point information is knowledge point contents, step 1012 can include：
First, the content to the first knowledge point and the content of the second knowledge point segment, and obtain the pass of the first knowledge point
Keyword and the keyword of the second knowledge point, keyword include one or more words.
For example, knowledge point contents can include definition, covering scope, formula of knowledge point etc., know respectively first
Know point and the second knowledge point is segmented, obtain corresponding keyword, wherein, keyword can be that a word can also be multiple
Word.It is illustrated with the knowledge point contents of the first knowledge point, first chooses first statement and segmented, it is suitable according to from left to right
Sequence according to pre-stored dictionary, identifies multiple alternative phrases being made of N number of word, wherein, N is the integer more than 1, then
Best alternative phrase is chosen according to preset disambiguation rule, which can for example include：A. length value and
It is maximum；B. the word length that is averaged is maximum；C. the variation of word length is minimum；D. monosyllabic word frequency of occurrences statistical value highest.Later, by best phrase
In keyword of first word as first statement, the phrase and Article 2 language in addition to first word in best phrase
Sentence is combined, and is segmented into next round, under the premise of context scene is considered, can select the key of Article 2 sentence
Word is iterated knowledge point contents, can select the keyword of knowledge point successively.
Secondly, according to the keyword of the first knowledge point and the keyword of the second knowledge point, the first knowledge point and second is obtained
About the degree of correlation of knowledge point contents between knowledge point.
It is exemplary, each keyword of the second knowledge point is compared successively with each keyword of the first knowledge point,
It determines between the first knowledge point and the second knowledge point about the degree of correlation of knowledge point contents.For example, the first knowledge point is by participle
The keyword obtained afterwards is：Movement changes, power, and the keyword that the second knowledge point obtains after participle is：It hinders, power is rubbed
It wipes, the keyword of two of which knowledge point all includes：" power ", then can determine between a knowledge point and the second knowledge point about
The degree of correlation of knowledge point contents is 33.3%, wherein, the keyword of the keyword of the first knowledge point and the second knowledge point can lead to
The mode of character match is crossed to obtain, the computational methods of particularly relevant degree can be selected according to actual demand.
It, can be with base by performing step 1013-1014 when specified knowledge point information includes wrong topic rate and knowledge point contents
The degree of correlation between knowledge point is determined in wrong topic rate and knowledge point contents the two dimensions synthesis.It should be noted that step 1013
The embodiment of step 1011 and step 1012 is combined with step 1014, will be closed between the first knowledge point and the second knowledge point
Combine in the degree of correlation of wrong topic rate and the degree of correlation about knowledge point contents, determine the first knowledge point and the second knowledge point
The degree of correlation, such as the weight of the degree of correlation about the degree of correlation of wrong topic rate and about knowledge point contents can be set respectively, then will
About the degree of correlation of wrong topic rate and the degree of correlation about knowledge point contents weight is multiplied by respectively to sum again, as the first knowledge point with
The degree of correlation of second knowledge point.By taking S=mW+nV as an example, wherein, S represents the degree of correlation of the first knowledge point and the second knowledge point, W
Represent the degree of correlation about wrong topic rate between the first knowledge point and the second knowledge point, V represents the first knowledge point and the second knowledge point
Between about the degree of correlation for knowing point content, m represents the corresponding weight of the degree of correlation about wrong topic rate, and n is represented about in knowledge point
The corresponding weight of the degree of correlation of appearance.
Fig. 3 be according to the flow chart of another Knowledge Relation method shown in an exemplary embodiment, as shown in figure 3,
This method further includes：
Step 103, recommendation information is exported, recommendation information is known for the second knowledge point of recommendation as the correlation of the first knowledge point
Know point.
Exemplary, teacher is in teaching process or student is in learning process, can show and recommend to teacher or student
Information recommends the second knowledge point related to the first knowledge point, it should be noted that can have multiple knowledge points and the first knowledge point
Relevant situation, therefore correlated knowledge point of multiple knowledge points as the first knowledge point can also be recommended, so that user is selected
It selects.
Step 104, the associated instructions for the second knowledge point are obtained, associated instructions include confirming the second knowledge point and first
The affirmative instruction of Knowledge Relation confirms the second knowledge point and the first knowledge point is not associated ignores instruction.
Step 105, when associated instructions is instruct certainly, recommend topic corresponding with the second knowledge point.
For example, it after user receives recommendation information, can be determined the need for according to the actual demand of oneself
It is associated with the first knowledge point and the second knowledge point.Such as can display whether the pop-up of the first knowledge point of association and the second knowledge point,
Wherein comprising "Yes" button and "No" button, user performs corresponding operation to issue associated instructions, which can divide
To instruct and ignoring instruction certainly, the "Yes" button and "No" button can be corresponded to respectively.When associated instructions is instruct certainly,
Recommend the corresponding topic in the second knowledge point to user, student can be assisted to be extended study, can also assisted teacher carry out entirely
The teaching of aspect.
Fig. 4 be according to the flow chart of another Knowledge Relation method shown in an exemplary embodiment, as shown in figure 4,
This method further includes：
Step 106, whenever determining any user when any topic of the first knowledge point is lost points, according to the degree of correlation to any
User pushes the topic and/or course about the second knowledge point.
Step 107, multiple users are counted for push about the topic of the second knowledge point and/or the use rate of course,
Association is obtained using rate sequence, association includes that multiple users are corresponding to use rate using rate sequence.
Step 108, variance of the association using rate sequence is obtained.
Step 109, when association is greater than or equal to preset variance threshold values using the variance of rate sequence, the degree of correlation is adjusted.
For example, it when user is in learning process, loses points on one of topic, when both having answered wrong one of topic, determining should
The first knowledge point belonging to topic, when the degree of correlation about specified knowledge point information between the first knowledge point and the second knowledge point is big
When relevance threshold, the topic and/or course about the second knowledge point are pushed to the user, at this point, this may be used in user
A push can also be refused.In the learning process for counting multiple users, for topic of the push about the second knowledge point and/
Or the use rate of course, association is obtained using rate sequence, wherein, association includes the corresponding use of multiple users using rate sequence
Rate for example, party A-subscriber is pushed the topic totally 10 times about the second knowledge point, has selected to use, then party A-subscriber's wherein having 8 times
Rate is used as 80%, party B-subscriber is pushed the topic totally 6 times about the second knowledge point, has selected to use wherein having 5 times, then B's
Rate is used as 83.3%, and so on, it is corresponding using rate that multiple users can be obtained.Further, association is calculated using rate
The variance of sequence, thus to obtain user group between the first knowledge point and the second knowledge point associated feedback, then corresponding adjust
The whole degree of correlation, to adapt to different user groups.It will finally be associated with and carried out using the variance of rate sequence and preset variance threshold values
Compare, when association is greater than or equal to preset variance threshold values using the variance of rate sequence, adjust the degree of correlation.For example, it can rise
The degree of correlation between high first knowledge point and the second knowledge point about specified knowledge point information, when association is using the variance of rate sequence
During less than preset variance threshold values, the degree of correlation can also be reduced, it further, can be with when the degree of correlation is less than preset threshold value
First knowledge point and the second knowledge point are updated to uncorrelated knowledge point.
Fig. 5 be according to the flow chart of another Knowledge Relation method shown in an exemplary embodiment, as shown in figure 5,
This method includes：
Step 110, relevance threshold is updated using rate sequence according to association.
For example, association can react user group using rate sequence and be closed between the first knowledge point and the second knowledge point
The degree of adoption of connection, if the recommendation of educational system is not always used, then illustrate the correlated knowledge point determined in step 102
The degree of correlation may be insufficient, i.e., relevance threshold is too low.Likewise, the recommendation of educational system is always used, then illustrates to walk
The degree of correlation of the correlated knowledge point determined in rapid 102 may be excessively high, i.e., relevance threshold is excessively high, therefore can more cenotype accordingly
Pass degree threshold value, to adapt to the specific requirements of user.
Fig. 6 be according to the flow chart of another Knowledge Relation method shown in an exemplary embodiment, as shown in fig. 6,
Step 110 includes：
Step 1101, when association is greater than or equal to preset expectation threshold value using the expectation of rate sequence, the degree of correlation is reduced
Threshold value.
Step 1102, when being associated with using rate sequence desirably less than preset expectation threshold value, relevance threshold is increased.
For example, expectation of the association using rate sequence is obtained, when it is expected to be greater than or equal to expectation threshold value, represents user
It is excessively high to the use rate of push, then can accordingly to reduce relevance threshold, make more knowledge points interrelated, extending user
Study or the range of teaching.When desirably less than preset expectation threshold value, represent that user is too low to the use rate of push, then can
Accordingly to improve relevance threshold, improve knowledge point and be mutually related efficiency, more effectively assist user's study or teaching.
In conclusion the specified knowledge point information of any two knowledge point of the disclosure in default knowledge point range,
It determines the degree of correlation of two knowledge points, further, according to the degree of correlation of two knowledge points and preset relevance threshold, judges
Whether each other two knowledge points correlated knowledge point when the degree of correlation of two knowledge points is greater than or equal to relevance threshold, determines
Knowledge point, can be associated by two knowledge points correlated knowledge point each other according to the degree of correlation.
Fig. 7 is according to a kind of block diagram of Knowledge Relation device shown in an exemplary embodiment, as shown in fig. 7, the dress
200 are put to include：
Acquisition module 201, for obtaining the phase between the first knowledge point and the second knowledge point about specified knowledge point information
Guan Du, the first knowledge point and the second knowledge point are any two knowledge points in default knowledge point range.
Relating module 202, for when the degree of correlation be greater than or equal to preset relevance threshold when, determine the first knowledge point and
Second knowledge point correlated knowledge point each other.
Fig. 8 is according to the block diagram of another Knowledge Relation device shown in an exemplary embodiment, as shown in figure 8, referring to
Determine knowledge point information including wrong topic rate and/or knowledge point contents, acquisition module 201 to include：
First acquisition submodule 2011, for obtaining between the first knowledge point and the second knowledge point about the related of wrong topic rate
Degree.
Second acquisition submodule 2012, for obtaining between the first knowledge point and the second knowledge point about knowledge point contents
The degree of correlation.
Third acquisition submodule 2013, for obtaining between the first knowledge point and the second knowledge point about the related of wrong topic rate
Degree and the degree of correlation about knowledge point contents.
Third acquisition submodule 2013, be additionally operable to according to about wrong topic rate the degree of correlation to about the related of knowledge point contents
Degree determines the degree of correlation of the first knowledge point and the second knowledge point.
Optionally, when specifying knowledge point information as wrong topic rate, the first acquisition submodule 2011 is used for：
First, the first of the first knowledge point of acquisition the wrong topic rate sequence and the second wrong topic rate sequence of the second knowledge point, first
Wrong topic rate sequence includes each wrong topic rate of the student on the first knowledge point in the first student set, and the second wrong topic rate sequence includes
Each wrong topic rate of the student on the second knowledge point in first student set.
Secondly, according to the first wrong topic rate sequence and the second wrong topic rate sequence, the wrong topic rate standard deviation of the first knowledge point is obtained,
Wrong topic rate covariance between the wrong topic rate standard deviation of second knowledge point and the first knowledge point and the second knowledge point.
Finally, according to the wrong topic rate standard deviation of the first knowledge point, the wrong topic rate standard deviation of the second knowledge point and wrong topic rate
Covariance obtains the degree of correlation about wrong topic rate between the first knowledge point and the second knowledge point.
Optionally, according to the wrong topic rate standard deviation of the first knowledge point, the wrong topic rate standard deviation of the second knowledge point and wrong topic
Rate covariance obtains the degree of correlation about wrong topic rate between the first knowledge point and the second knowledge point, including：
According to the wrong topic rate standard deviation of the first knowledge point, the wrong topic rate standard deviation of the second knowledge point and wrong topic rate association side
Difference obtains the degree of correlation between the first knowledge point and the second knowledge point about wrong topic rate using the first relatedness computation formula.
Wherein, the first relatedness computation formula includes：
Wherein, ρXYRepresent the similarity of knowledge point X and knowledge point Y, Cov (X, Y) is represented between knowledge point X and knowledge point Y
Wrong topic rate covariance, σXRepresent the wrong topic rate standard deviation of knowledge point X, σYRepresent the wrong topic rate standard deviation of knowledge point Y.
Optionally, when specifying knowledge point information as knowledge point contents, the second acquisition submodule 2012 is used for：
First, the content to the first knowledge point and the content of the second knowledge point segment, and obtain the pass of the first knowledge point
Keyword and the keyword of the second knowledge point, keyword include one or more words.
Secondly, according to the keyword of the first knowledge point and the keyword of the second knowledge point, the first knowledge point and second is obtained
About the degree of correlation of knowledge point contents between knowledge point.
Fig. 9 is according to the block diagram of another Knowledge Relation device shown in an exemplary embodiment, as shown in figure 9, should
Device 200 further includes：
Output module 203, for exporting recommendation information, recommendation information is for the second knowledge point of recommendation as the first knowledge point
Correlated knowledge point.
Instruction acquisition module 204, for obtaining the associated instructions for the second knowledge point, associated instructions include confirming second
Affirmative instruction or confirmation second knowledge point of the knowledge point with the first Knowledge Relation refer to the not associated negative in the first knowledge point
It enables.
Recommending module 205, for when associated instructions is instruct certainly, recommending topic corresponding with the second knowledge point.
Figure 10 be according to the block diagram of another Knowledge Relation device shown in an exemplary embodiment, as shown in Figure 10,
The device 200 further includes：
Pushing module 206, for whenever determining any user when any topic of the first knowledge point is lost points, according to correlation
Spend the topic and/or course pushed to any user about the second knowledge point.
Statistical module 207, for counting multiple users for topic and/or course of the push about second knowledge point
It using rate, obtains using rate sequence, it is corresponding using rate to include multiple users using rate sequence.
Variance acquisition module 208, for obtaining variance of the association using rate sequence.
Module 209 is adjusted, for when association is greater than or equal to preset variance threshold values using the variance of rate sequence, adjusting
The degree of correlation.
Figure 11 be according to the block diagram of another Knowledge Relation device shown in an exemplary embodiment, as shown in figure 11,
The device 200 further includes：
Update module 210, for, using rate sequence, updating relevance threshold according to association.
Figure 12 be according to the block diagram of another Knowledge Relation device shown in an exemplary embodiment, as shown in figure 12,
Update module 210 includes：
First update submodule 2101, for being greater than or equal to preset expectation threshold value using the expectation of rate sequence when association
When, reduce relevance threshold.
Second update submodule 2102, for when being associated with using rate sequence desirably less than preset expectation threshold value, rising
High relevance threshold.
About the device in above-described embodiment, wherein modules perform the concrete mode of operation in related this method
Embodiment in be described in detail, explanation will be not set forth in detail herein.
In conclusion the specified knowledge point information of any two knowledge point of the disclosure in default knowledge point range,
It determines the degree of correlation of two knowledge points, further, according to the degree of correlation of two knowledge points and preset relevance threshold, judges
Whether each other two knowledge points correlated knowledge point when the degree of correlation of two knowledge points is greater than or equal to relevance threshold, determines
Knowledge point, can be associated by two knowledge points correlated knowledge point each other according to the degree of correlation.
Figure 13 is the block diagram according to a kind of electronic equipment 700 shown in an exemplary embodiment.As shown in figure 13, the electronics
Equipment 700 can include：Processor 701, memory 702, multimedia component 703, input/output (I/O) interface 704 and
Communication component 705.
Wherein, processor 701 is used to control the integrated operation of the electronic equipment 700, to complete above-mentioned Knowledge Relation
All or part of step in method.Memory 702 is used to store various types of data to support in the electronic equipment 700
Operation, these data can for example include the instruction of any application program or method for being operated on the electronic equipment 700,
And the relevant data of application program, such as contact data, the message of transmitting-receiving, picture, audio, video etc..The memory
702 can be realized, such as static random is deposited by any kind of volatibility or non-volatile memory device or combination thereof
Access to memory (Static Random Access Memory, abbreviation SRAM), electrically erasable programmable read-only memory
(Electrically Erasable Programmable Read-Only Memory, abbreviation EEPROM), erasable programmable
Read-only memory (Erasable Programmable Read-Only Memory, abbreviation EPROM), programmable read only memory
(Programmable Read-Only Memory, abbreviation PROM), and read-only memory (Read-Only Memory, referred to as
ROM), magnetic memory, flash memory, disk or CD.Multimedia component 703 can include screen and audio component.Wherein
Screen for example can be touch screen, and audio component is for output and/or input audio signal.For example, audio component can include
One microphone, microphone are used to receive external audio signal.The received audio signal can be further stored in storage
Device 702 is sent by communication component 705.Audio component further includes at least one loud speaker, for exports audio signal.I/O
Interface 704 provides interface between processor 701 and other interface modules, other above-mentioned interface modules can be keyboard, mouse,
Button etc..These buttons can be virtual push button or entity button.Communication component 705 is for the electronic equipment 700 and other
Wired or wireless communication is carried out between equipment.Wireless communication, such as Wi-Fi, bluetooth, near-field communication (Near Field
Communication, abbreviation NFC), 2G, 3G or 4G or they one or more of combination, therefore corresponding communication
Component 705 can include：Wi-Fi module, bluetooth module, NFC module.
In one exemplary embodiment, electronic equipment 700 can be by one or more application application-specific integrated circuit
(Application Specific Integrated Circuit, abbreviation ASIC), digital signal processor (Digital
Signal Processor, abbreviation DSP), digital signal processing appts (Digital Signal Processing Device,
Abbreviation DSPD), programmable logic device (Programmable Logic Device, abbreviation PLD), field programmable gate array
(Field Programmable Gate Array, abbreviation FPGA), controller, microcontroller, microprocessor or other electronics member
Part is realized, for performing above-mentioned Knowledge Relation method.
In a further exemplary embodiment, a kind of computer readable storage medium including program instruction, example are additionally provided
Such as include the memory 702 of program instruction, above procedure instruction can be performed by the processor 701 of electronic equipment 700 in completion
The Knowledge Relation method stated.
In conclusion the specified knowledge point information of any two knowledge point of the disclosure in default knowledge point range,
It determines the degree of correlation of two knowledge points, further, according to the degree of correlation of two knowledge points and preset relevance threshold, judges
Whether each other two knowledge points correlated knowledge point when the degree of correlation of two knowledge points is greater than or equal to relevance threshold, determines
Knowledge point, can be associated by two knowledge points correlated knowledge point each other according to the degree of correlation.
The preferred embodiment of the disclosure is described in detail above in association with attached drawing, still, the disclosure is not limited to above-mentioned reality
The detail in mode is applied, in the range of the technology design of the disclosure, those skilled in the art are considering specification and practice
After the disclosure, it is readily apparent that other embodiments of the disclosure, belongs to the protection domain of the disclosure.
It is further to note that specific technical features described in the above specific embodiments, in not lance
In the case of shield, it can be combined by any suitable means.Simultaneously between a variety of different embodiments of the disclosure
Arbitrary combination can also be carried out, as long as its thought without prejudice to the disclosure, should equally be considered as disclosure disclosure of that.
The disclosure is not limited to the precision architecture being described above out, and the scope of the present disclosure is only limited by appended claim
System.
Claims (10)
- A kind of 1. Knowledge Relation method, which is characterized in that the method includes：Obtain the degree of correlation about specified knowledge point information between the first knowledge point and the second knowledge point, first knowledge point and Second knowledge point is any two knowledge point in default knowledge point range；When the degree of correlation is greater than or equal to preset relevance threshold, first knowledge point and second knowledge are determined Put correlated knowledge point each other.
- 2. according to the method described in claim 1, it is characterized in that, the specified knowledge point information includes wrong topic rate and/or knows A knowledge point content, the degree of correlation obtained between the first knowledge point and the second knowledge point about specified knowledge point information, including：Obtain the degree of correlation about wrong topic rate between first knowledge point and second knowledge point；Alternatively,Obtain the degree of correlation about knowledge point contents between first knowledge point and second knowledge point；Alternatively,It obtains between first knowledge point and second knowledge point about the degree of correlation of wrong topic rate and about knowledge point contents The degree of correlation；First knowledge point is determined according to the degree of correlation about wrong topic rate and the degree of correlation about knowledge point contents With the degree of correlation of second knowledge point.
- It is 3. described to obtain according to the method described in claim 2, it is characterized in that, when the specified knowledge point information is wrong topic rate The degree of correlation about wrong topic rate between the first knowledge point and the second knowledge point is taken, including：Obtain the first wrong topic rate sequence of first knowledge point and the second wrong topic rate sequence of second knowledge point, described the One wrong topic rate sequence includes each wrong topic rate of the student on first knowledge point in the first student set, the described second wrong topic Rate sequence includes each wrong topic rate of the student on second knowledge point in first student set；According to the described first wrong topic rate sequence and the second wrong topic rate sequence, the wrong topic rate standard of first knowledge point is obtained Difference, the wrong topic rate between the wrong topic rate standard deviation of second knowledge point and first knowledge point and second knowledge point Covariance；According to the wrong topic rate standard deviation, the wrong topic rate standard deviation of second knowledge point and the mistake of first knowledge point Topic rate covariance obtains the degree of correlation about the wrong topic rate between first knowledge point and second knowledge point.
- 4. the according to the method described in claim 3, it is characterized in that, wrong topic rate standard according to first knowledge point The wrong topic rate standard deviation of poor, described second knowledge point and the wrong topic rate covariance, obtain first knowledge point with it is described The degree of correlation between second knowledge point about the wrong topic rate, including：According to the wrong topic rate standard deviation, the wrong topic rate standard deviation of second knowledge point and the mistake of first knowledge point Topic rate covariance is obtained between first knowledge point and second knowledge point using the first relatedness computation formula about institute State the degree of correlation of wrong topic rate；Wherein, the first relatedness computation formula includes：Wherein, ρXYRepresent the similarity of knowledge point X and knowledge point Y, Cov (X, Y) represents the knowledge point X and knowledge point Y Between wrong topic rate covariance, σXRepresent the wrong topic rate standard deviation of the knowledge point X, σYRepresent the wrong topic rate mark of the knowledge point Y It is accurate poor.
- 5. according to the method described in claim 2, it is characterized in that, the specified knowledge point information be knowledge point contents when, institute The degree of correlation obtained between the first knowledge point and the second knowledge point about the knowledge point contents is stated, including：The content of content and second knowledge point to first knowledge point segments, and obtains first knowledge point Keyword and the keyword of second knowledge point, the keyword include one or more words；According to the keyword of first knowledge point and the keyword of second knowledge point, first knowledge point and institute are obtained State the degree of correlation about the knowledge point contents between the second knowledge point.
- 6. method according to any one of claims 1-5, which is characterized in that the method further includes：Recommendation information is exported, the recommendation information is used to recommend the phase of second knowledge point as first knowledge point Close knowledge point；Obtain the associated instructions for second knowledge point, the associated instructions include confirming second knowledge point with it is described The affirmative instruction of first Knowledge Relation confirms that second knowledge point refers to the not associated negative in first knowledge point It enables；When the associated instructions is the instructions certainly, recommend topic corresponding with second knowledge point.
- 7. method according to any one of claims 1-5, which is characterized in that the method further includes：Whenever determining that any topic of any user in first knowledge point is lost points, according to the degree of correlation to described any User pushes the topic and/or course about second knowledge point；Multiple users are counted for push about the topic of second knowledge point and/or the use rate of course, are associated with Using rate sequence, it is described association using rate sequence include the multiple user it is corresponding it is described use rate；Obtain variance of the association using rate sequence；When the association is greater than or equal to preset variance threshold values using the variance of rate sequence, the degree of correlation is adjusted.
- 8. a kind of Knowledge Relation device, which is characterized in that described device includes：Acquisition module, for obtaining the degree of correlation between the first knowledge point and the second knowledge point about specified knowledge point information, institute It is any two knowledge point preset in knowledge point range to state the first knowledge point and second knowledge point；Relating module, for when the degree of correlation is greater than or equal to preset relevance threshold, determining first knowledge point With second knowledge point correlated knowledge point each other.
- 9. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor The step of any one of claim 1-7 the methods are realized during row.
- 10. a kind of electronic equipment, which is characterized in that including：Computer readable storage medium described in claim 9；AndOne or more processor, for performing the program in the computer readable storage medium.
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