CN108830759B - Subject knowledge description method - Google Patents

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CN108830759B
CN108830759B CN201810620819.6A CN201810620819A CN108830759B CN 108830759 B CN108830759 B CN 108830759B CN 201810620819 A CN201810620819 A CN 201810620819A CN 108830759 B CN108830759 B CN 108830759B
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李志军
徐继宁
姜力
王力
孙德辉
胡敦利
吴力普
李中贺
张黎明
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Beijing Yunjing Zhitong Education Technology Co ltd
North China University of Technology
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Abstract

The invention relates to a disciplinary knowledge description method, which is used for carrying out hierarchical arrangement on knowledge objects, carrying out ID assignment on the knowledge objects according to different hierarchical relations and determining a directional association chain between knowledge units by adopting a supporting relation and a supported relation. The method comprises the steps of setting a description unit and a test unit for knowledge objects, connecting knowledge objects of the same level and different levels by using each description unit and each test unit, assigning values to the importance, knowledge quantity and knowledge types of related knowledge objects, and finally generating a multi-level and multi-dimensional directed knowledge map according to a knowledge object attribute file, so that the attributes of relevance, hierarchy, importance and the like of the knowledge objects are clearly and completely described, guidance can be provided for a user to select a learning path in the learning process, and the learning efficiency and the learning achievement degree are improved.

Description

Subject knowledge description method
Technical Field
The invention belongs to the technical field of knowledge expression and knowledge navigation, and particularly relates to a subject knowledge description method.
Background
The subject knowledge object is a knowledge network composed of a large number of interrelated knowledge points. In the existing learning mode, online learning of the network occupies an increasingly important position, and attributes such as hierarchy, relevance and importance of knowledge objects are expressed by using a computer technology and a knowledge map mode, so that on one hand, system construction and visual presentation of learning resources can be performed, on the other hand, a system or a user can select a proper learning path, and further learning efficiency is improved.
Chinese patent CN105426967 discloses a discipline knowledge expression and description method, aiming at discipline knowledge objects, comprehensively considering factors such as hierarchical relationship, incidence relationship, knowledge types and knowledge information quantity, combining four-element expression, and innovatively establishing a description and expression method similar to a map. However, the discipline knowledge description method proposed by the patent tends to describe the integrity of the knowledge object, and the representation of the hierarchy, relevance, importance and other attributes of the knowledge object is not clear, so that guidance is difficult to provide for the learning path selection of the user.
Disclosure of Invention
In order to solve the problems, the invention provides a disciplinary knowledge description method, which comprises the following specific schemes:
a discipline knowledge description method is characterized by comprising the following steps:
and S1, dividing the knowledge objects into X levels according to the membership relation among the knowledge objects contained in the subject knowledge to be described, wherein X is 1,2 and 3 … N. Each X level comprises one or a plurality of knowledge objects, and each knowledge object comprises a description unit, a plurality of X-level knowledge units and a test unit. The knowledge objects with the level X being 1 are the whole subject knowledge and are called parent knowledge objects, and the knowledge objects with the level X being larger than the level 1 are all certain X-1 level knowledge units in certain X-1 level knowledge objects and are called the inherited knowledge of the X-1 level knowledge objects. Unique ID assignment is carried out on the description unit, the knowledge unit and the test unit of each level, and a knowledge object level relation storage file is formed, wherein the ID value comprises an attribute value, a level value and a sequence value;
s2, calling a knowledge object hierarchical relationship storage file, confirming the mutual supporting relationship of X-level knowledge units contained in the same knowledge object, marking the supporting relationship and the supported relationship of each knowledge unit, and storing the supporting relationship and the supported relationship into the knowledge object to form a knowledge unit supporting relationship storage file; the supported relation comprises a knowledge unit ID value and a supported relation assignment which play a supporting role for the current knowledge unit, and the supported relation comprises a knowledge unit ID value and a supporting relation assignment which are supported by the current knowledge unit;
s3, calling a supporting relation storage file, acquiring supporting relation and supported relation data of each knowledge unit, and forming a directed association chain between the knowledge units according to a supporting direction; acquiring all knowledge units positioned at the beginning of the directed association chain and marking the knowledge units as head-end knowledge units; acquiring all knowledge units positioned at the tail end of the directed association chain and marking the knowledge units as tail end knowledge units; each description unit is marked as a knowledge object learning inlet, each test unit is marked as a knowledge object learning outlet, and a directed association relation storage file is formed;
s4, a directed correlation storage file is called, a description unit in the same knowledge object is connected with a head-end knowledge unit, a tail-end knowledge unit in the same knowledge object is connected with a test unit, and the test units of all the next-level knowledge objects with a supporting function in the knowledge object are connected with the description units of all the next-level knowledge objects supported by the test units to form an in-layer directed correlation storage file;
s5, calling an intra-layer directed association relation storage file, acquiring an inherited knowledge X-level knowledge object belonging to the same X-1 level knowledge object, judging whether an X-level knowledge unit contained in the X-level knowledge object has an inherited knowledge X +1 level knowledge object, and acquiring an X-level description unit, an X-level test unit, all X-level head-end knowledge units and all X-level tail-end knowledge units of the X-level knowledge object when the judgment result is 'yes'; acquiring an X +1 level head end description unit of each X level head end knowledge unit (namely an X +1 level head end knowledge object) and an X +1 level tail end test unit of each X level tail end knowledge unit (namely an X +1 level tail end knowledge object); connecting an X-level description unit in the X-level knowledge object with the X + 1-level head end description unit; and connecting the X-level test unit in the X-level knowledge object with the X + 1-level tail end test unit to form an interlayer directed incidence relation, and forming and updating a knowledge object attribute file.
The disciplinary knowledge description method provided by the invention has the advantages that the knowledge objects are arranged in a layered mode, the meaning of the X-level knowledge unit is the same as that of the corresponding X + 1-level knowledge object, and the names can be exchanged; the method comprises the steps of carrying out ID assignment on knowledge objects according to inheritance relations, determining a directional association chain of each knowledge unit by adopting a support relation and a supported relation, setting a description unit and a test unit for each knowledge object, connecting the knowledge objects of the same level or different levels by using each description unit and each test unit, and finally generating a multi-level directional knowledge map according to knowledge object attribute files, wherein the hierarchy, the directionality and the association among different knowledge units are completely described, so that reasonable learning path guidance can be provided for users.
Further, confirming the mutual supporting relationship of the X-level knowledge units contained in the same knowledge object, and marking the supporting relationship and the supported relationship of each knowledge unit, specifically: when X is equal to N, the support relationship is a necessary condition for learning that the current knowledge unit is the knowledge unit supported by the current knowledge unit; when X ≠ N, the support relationship is a necessary condition for learning that the X + 1-level knowledge unit included in the inheritance knowledge of β current knowledge units is the knowledge unit supported by the current knowledge unit.
Further, the support relationship further comprises a support coefficient η, when X is equal to N, and when the X-level knowledge units have a mutual support relationship, the support coefficient η of the knowledge unit playing a supporting role is equal to 1; when X ≠ N, η ≠ β/α, where α is the number of X + 1-level knowledge units included in the inherited knowledge of the knowledge unit serving as a support.
The support relation comprises a support coefficient, so that the support condition among the knowledge units is more clearly quantized, and a user or a system can conveniently select a learning path.
Further, the method comprises the following steps:
and S6, calling the knowledge unit attribute file, assigning values to the importance degree of each X-level knowledge unit for calculation, and updating the knowledge object attribute file. And importance degree assignment is added to the description attributes of subject knowledge, so that the convenience of path selection for the user to learn is further improved, and the learning efficiency is improved.
Preferably, the importance degree assignment comprises importance degree I of the corresponding X-level knowledge unit in the knowledge object to which the corresponding X-level knowledge unit belongsi。IiValue I is assigned by an expert according to a predetermined formulai,1And the supporting relation I of the other X-level knowledge units in the related knowledge objecti,2And (4) obtaining the product through calculation.
Preferably, Ii＝max(Ii,1,Ii,2),
Figure BDA0001697993220000041
Ii,2＝max(Ii,21,Ii,22,L,Ii,2m) Wherein, Ii,1qAssigning the importance degree of the X-level knowledge unit in the knowledge object to which the X-level knowledge unit belongs to the expert, wherein n is the number of the experts, Ii,1,maxMaximum value assigned to expert, Ii,1,minMinimum value assigned to expert; i isi,2mAnd supporting coefficients of the current knowledge unit to other m X-level knowledge units in the knowledge object.
The importance of the knowledge unit is reasonably considered through the two angles of the expert assignment and the support relation, so that the assignment of the importance degree of the knowledge is more reasonable, and the accuracy of knowledge description is improved.
Further preferably, when X ≠ 1, the importance degree assignment further includes the importance value I of the corresponding X-level knowledge unit within the whole subject knowledgei,u。
Still further, the method further comprises the steps of:
s7, the knowledge object attribute file is retrieved, and for each knowledge unit, the time T required for all users who know the knowledge unit to learn the knowledge unit is acquired. And generating a knowledge quantity value of the knowledge unit according to a preset formula, storing the knowledge quantity value in a knowledge object, and updating the attribute file of the knowledge object.
Further, when the user's achievement degree gamma of the corresponding knowledge unit is larger than the pre-thresholdWhen setting the threshold value mu, marking the corresponding user as the user grasping the knowledge unit, wherein the achievement degree gamma is Ax/AmWherein A ismIs the total test score of the test unit in the knowledge object in which the corresponding knowledge unit is located, AxAnd testing scores of the test units in the knowledge object where the corresponding knowledge units are located for the current user.
The knowledge quantity attribute is added to each knowledge unit, so that a user can conveniently and reasonably distribute learning time and energy in the learning process, and the subject knowledge obtained by the description method has strong practicability.
Still further, the method further comprises the steps of:
and S8, acquiring the attribute file of the knowledge object, calling N-level knowledge units with X-level as N-level, assigning values to the knowledge types of all the N-level knowledge units, wherein the knowledge types comprise a concept class, a principle class, a method class, a fact class, a viewpoint class and an instance class, and storing and updating the attribute file of the knowledge object.
The discipline knowledge description method provided by the invention has the advantages that the hierarchical relationship of knowledge objects is reasonably set, the description unit is set as a learning inlet of the knowledge objects, the test unit is set as a learning outlet of the knowledge objects, the directional connection of knowledge objects at the same level is established by adopting the support relationship, the directional connection of knowledge objects at adjacent levels is established by utilizing the description unit of a head-end knowledge object and the test unit of a tail-end knowledge object, finally, a multi-dimensional and multi-level directional knowledge network is formed, and the relevance, the hierarchy and the importance among different knowledge units are clearly described. The subject knowledge map obtained by the description method provided by the invention can help a user to reasonably select a learning path, and the learning efficiency and the achievement degree are improved.
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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. It is obvious that the drawings described below are only embodiments of the invention, and that other drawings can be derived from the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a subject knowledge description method of example 1;
FIG. 2. Z of example 111The connection schematic diagram of the three-level supporting relationship of (1);
FIG. 3. Z of example 111The directed association chain diagram among all knowledge units in the system (1);
FIG. 4. Z of example 11The connection schematic diagram of the secondary support relationship of (1);
FIG. 5. Z of example 11A directed association chain diagram among all knowledge units in the system;
FIG. 6 is a connection diagram of the primary support relationship of the subject knowledge U of example 1;
FIG. 7 is a schematic diagram of a directed chain of associations between knowledge units in the subject knowledge U of example 1.
Detailed Description
The technical solution in the embodiments of the present invention is clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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 invention.
Example 1
A discipline knowledge description method, as shown in fig. 1, comprising the steps of:
s1, dividing the knowledge objects into X levels according to the membership relationship contained in the subject knowledge to be described, determining the level relationship and the ID value of the knowledge objects, and forming a knowledge object level relationship storage file, wherein the ID value contains an attribute value, a level value and a sequence value;
as an example, the present embodiment establishes three hierarchical relationships, where X is 1, which is the first hierarchical relationship, and includes a parent knowledge object U, and U includes a description unit SuN primary knowledge units ZiAnd a test unit Fu(ii) a X is 2, the second hierarchical level, contains N secondary knowledge objects, i.e. eachThe secondary knowledge object corresponds to a primary knowledge unit ZiEach secondary knowledge object ZiEach comprising a pair ZiDescription unit S for carrying out the descriptionZiA test unit FZiAnd M secondary knowledge units Zij(ii) a When X is 3, the third level contains M three-level knowledge objects, that is, each three-level knowledge object corresponds to one secondary knowledge unit ZijEach tertiary knowledge unit ZijEach comprising a pair ZijDescription unit for carrying out the description
Figure BDA0001697993220000071
A test unit
Figure BDA0001697993220000072
And P three-level knowledge units
Figure BDA0001697993220000073
And setting the hierarchy value and the sequence value according to the attribute values of different knowledge, namely the description attribute, the knowledge unit attribute and the test attribute, so as to obtain the knowledge object hierarchy relation storage file.
As an example, the three-level knowledge unit in this embodiment is a minimum unit of discipline knowledge, and does not include a description unit and a test unit.
Further by way of example, a description attribute of 001, a knowledge unit attribute of 002, and a test attribute of 003 may be set; the father knowledge object U can be a subject overall profile, the N primary knowledge units are N chapters, each level is expressed by a two-bit integer, and the primary knowledge unit is Z01、Z02And so on; each primary knowledge unit is a secondary knowledge object from the perspective of the second level, Z02The secondary description unit is Sz02(ii) a Test cell is Fz02The second-level knowledge unit is Z0201、Z0202And so on; each secondary knowledge unit is a three-level knowledge object from the perspective of the third level, Z020204Is Z02The 04 th inherited knowledge contained in the 02 th secondary knowledge unit below the primary knowledge unitAnd (4) a three-level knowledge unit.
And S2, calling a knowledge object hierarchical relationship storage file, confirming the mutual supporting relationship of the X-level knowledge units contained in the same knowledge object, marking the supporting relationship and the supported relationship of each knowledge unit, and storing the supporting relationship and the supported relationship into the knowledge object to form a knowledge unit supporting relationship storage file. The supported relation comprises a knowledge unit ID value and a supported relation assignment which play a supporting role for the current knowledge unit, and the supported relation comprises a knowledge unit ID value and a supporting relation assignment which are supported by the current knowledge unit;
as an example, the support relationship of one knowledge unit to other knowledge units belonging to the same knowledge object is analyzed respectively; when the knowledge unit A supports the knowledge unit B, the knowledge unit B supports the knowledge unit C and the knowledge unit D, and the knowledge unit D supports the knowledge unit E, the support relation of the mark A is B01; b are supported and supported in relation to C01, D01 and A02; the supported relation of C is B02, the supported relation of D is B02, E01; e is D02; where 01 represents the supporting relationship assignment and 02 represents the supported relationship assignment.
S3, calling a supporting relation storage file, acquiring supporting relation and supported relation data of each knowledge unit, and forming a directed association chain between the knowledge units according to a supporting direction; acquiring all knowledge units positioned at the beginning of the directed association chain and marking the knowledge units as head-end knowledge units; acquiring all knowledge units positioned at the tail end of the directed association chain and marking the knowledge units as tail end knowledge units; each description unit is marked as a knowledge object learning inlet, each test unit is marked as a knowledge object learning outlet, and a directed association relation storage file is formed;
s4, a directed correlation storage file is called, a description unit in the same knowledge object is connected with a head-end knowledge unit, a tail-end knowledge unit in the same knowledge object is connected with a test unit, and the test units of all the next-level knowledge objects with a supporting function in the knowledge object are connected with the description units of all the next-level knowledge objects supported by the test units to form an in-layer directed correlation storage file;
it is worth to be noted that the description unit, the knowledge unit and the test unit in the same knowledge object are connected to ensure that all knowledge contents in the same layer are in the same association chain, thereby realizing the continuity of knowledge in the same level. Because of the directionality of the supporting relationship, directional association chains are formed among knowledge units in the same knowledge object belonging to the X +1 level, the first-end knowledge units of the association chains only have the supporting relationship but not the supported relationship in the same level, and the supported relationship is embodied in the supported relationship of the X level; similarly, the end knowledge units of the association chain only have a supported relationship and an unsupported relationship in the same level, and the supported relationship is embodied in the supported relationship of the X-level knowledge object.
As an example, the present embodiment establishes three hierarchical relationships, which respectively define the supporting relationship:
(1) three-level support relationship for a certain second-level knowledge unit ZijAll three levels of knowledge units
Figure BDA0001697993220000091
Defining a three-level support relationship: preferably, the support relationship of the three-level knowledge unit a to the three-level knowledge unit B means that generally, the learning a is a necessary condition for the learning B, for example, the learning numbers 1,2,3, and 10 are necessary conditions for learning the addition within 10, that is, the former constitutes a support relationship to the latter, and the support relationship can be obtained by expert experience. According to the supporting relation, each three-level knowledge unit can be formed into a directed structure diagram, for example, a second-level knowledge unit Z is assumed11There are 5 three-level knowledge units
Figure BDA0001697993220000092
The supporting relationship is as follows:
Figure BDA0001697993220000093
to pair
Figure BDA0001697993220000094
The support relationship is formed, and the support structure,
Figure BDA0001697993220000095
is a schooling party
Figure BDA0001697993220000096
The requirements of the process are as follows,
Figure BDA0001697993220000097
to pair
Figure BDA0001697993220000098
In supporting relation, as shown in FIG. 2, wherein
Figure BDA0001697993220000099
Is a head end knowledge unit, and S11Are connected to each other, S11As a knowledge entry, pointing to a head-end knowledge unit;
Figure BDA0001697993220000101
is the end knowledge unit, with F11Are connected to each other, F11As a knowledge outlet, the description units pointing to other knowledge objects, Z of the subject knowledge U11The directed association chain of the three-level support relationship of (2) is shown in fig. 3.
(2) Secondary support relationship to a certain primary knowledge unit ZiSecond level knowledge unit Zi1，Zi2，Zi3∧ZijDefining a secondary support relationship: preferably, the support relationship of the second-level knowledge unit a to the second-level knowledge unit B means that, in general, it is a necessary condition for learning the second-level knowledge unit B to learn several third-level knowledge units in the society a. According to the supporting relation, the test unit of the secondary knowledge unit A and the description unit of the secondary knowledge unit B can be connected to form a directed structure diagram of the secondary knowledge unit, for example, the primary knowledge unit is assumed to have Z1There are 5 second-level knowledge units Z11、Z12、Z13、Z14、Z15The supporting relationship is as follows: z11To Z12Form a supporting relationship, learn Z12Is to learn Z13A requirement of (A), Z13To Z14、Z15Form a supporting relationship, Z14To Z15Form a supporting relationship, Z of subject knowledge U1The secondary support relationship of (2) is shown in fig. 4 and 5.
(3) Primary support relationship to all primary knowledge units Z of subject knowledge U1，Z2，Z3∧ZnDefining a primary support relationship: preferably, the support relationship of the primary knowledge unit a to the primary knowledge unit B means that, in general, a plurality of secondary knowledge units of the academic conference a are necessary conditions for the academic conference B, and the test unit of the primary knowledge unit a and the description unit of the B can be connected according to the support relationship to form a directed structure diagram of the primary knowledge unit. For example, assume that there are subject knowledge U with 5 primary knowledge units Z1、Z2、Z3、Z4、Z5The supporting relationship is as follows: z1To Z2Form a supporting relationship, Z2To Z3、Z4In a supporting relationship, Z3To Z4Form a supporting relationship, learn Z4Is to learn Z5The primary support relationship of the subject knowledge U is shown in fig. 6.
S5, calling an intra-layer directed association relation storage file, acquiring an inherited knowledge X-level knowledge object belonging to the same X-1 level knowledge object, judging whether an X-level knowledge unit contained in the X-level knowledge object has an inherited knowledge X +1 level knowledge object, and acquiring an X-level description unit, an X-level test unit, all X-level head-end knowledge units and all X-level tail-end knowledge units of the X-level knowledge object when the judgment result is 'yes'; acquiring an X +1 level head end description unit of each X level head end knowledge unit (namely an X +1 level head end knowledge object) and an X +1 level tail end test unit of each X level tail end knowledge unit (namely an X +1 level tail end knowledge object); connecting an X-level description unit in the X-level knowledge object with the X + 1-level head end description unit; and connecting the X-level test unit in the X-level knowledge object with the X + 1-level tail end test unit to form an interlayer directed incidence relation, and forming and updating a knowledge object attribute file.
It should be noted that after knowledge has continuity in the same level, specific knowledge across levels needs to be further associated, for example, fig. 7 forms a network structure containing all knowledge in the entire subject knowledge, and all knowledge units can be connected through a description unit and a test unit according to ID values and support relationship values of different knowledge units to form a multi-level directed knowledge map, which facilitates effective learning of users.
Example 2
The discipline knowledge description method provided by this embodiment is different from embodiment 1 in that, by further definition, the support relationship further includes a support coefficient η, and when X is equal to N, and when X-level knowledge units have a mutual support relationship, the support coefficient η of the knowledge units serving as supports is equal to 1; when X ≠ N, η ≠ β/α, where α is the number of X + 1-level knowledge units included in the inherited knowledge of the knowledge unit serving as a support.
As an example, the present embodiment establishes three hierarchical relationships, defining support relationship coefficients:
(1) third-order support coefficient: and if the supporting relationship exists between the two three-level knowledge units, defining the supporting coefficient to be 1, and if the supporting relationship does not exist, defining the supporting coefficient to be 0.
(2) Second order support factor, assuming that the second order knowledge unit A has alpha2A three-level knowledge unit, wherein in A there is beta2And if the three-level knowledge unit is a necessary condition for learning B, defining the support coefficients from A to B as follows:
Figure BDA0001697993220000121
(3) first-order support coefficient: assume that the primary knowledge unit A has α1A second level knowledge unit, wherein there is beta in A1And if the second-level knowledge unit is a necessary condition for learning B, defining the support coefficients from A to B as follows:
Figure BDA0001697993220000122
example 3
The subject knowledge description method provided in this embodiment is different from embodiment 2 in that, by way of further limitation, the description of the subject knowledge also relates to an importance index, and the subject knowledge description method further includes the following steps:
and S6, calling the knowledge unit attribute file, performing importance degree assignment calculation on the importance of each X-level knowledge unit, and updating the knowledge object attribute file.
Preferably, in this embodiment, the importance degree assignment includes importance degree I of the corresponding X-level knowledge unit in the knowledge object to which the corresponding X-level knowledge unit belongsi，IiValue I is assigned by an expert according to a predetermined formulai,1And the support coefficient I of other X-level knowledge units in the related knowledge objecti,2And (4) obtaining the product through calculation.
It is worth mentioning thatiThe IDs of knowledge units according to different levels have different representation variants, where i is only illustrative of the identity of the different knowledge units.
As an example, the present embodiment establishes three hierarchical relationships, and an example of assigning importance is as follows:
(1) the degree of importance of the primary knowledge unit;
for each primary knowledge unit ZiAnd assigning the importance degree of the U according to the following assignment rule: first for the first level knowledge unit ZiSetting ten importance levels of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1, and considering n (n is more than or equal to 3) experts to assign the importance levels to obtain Ii,11,Ii,12,L,Ii,1nDefining the maximum and minimum values thereof:
Ii,1,max＝max(Ii,11,Ii,12,L,Ii,1n),Ii,1,min＝min(Ii,11,Ii,12,L,Ii,1n)
then the expert is right to ZiThe importance degree of (c) is assigned as:
Figure BDA0001697993220000131
then, according to the support condition of the first-level knowledge unit to other first-level knowledge units, considering the first-level knowledge unit ZiThe other m primary knowledge units have supporting relations, and the supporting coefficients are I respectivelyi,21,Ii,22,L,Ii,2mThen, the importance degree obtained according to the support relationship is:
Ii,2＝max(Ii,21,Ii,22,L,Ii,2m)
according to the above two importance degrees, the importance degree of the primary knowledge unit in U is obtained as follows:
Ii＝max(Ii,1,Ii,2)
(2) degree of importance of secondary knowledge units
Consider a primary knowledge unit ZiAll secondary knowledge units Z ofi1，Zi2，Zi3∧ZijTo it is in ZiThe importance degree in (2) is assigned, and the assignment rule is as follows: first for the second level knowledge unit ZijTen grades 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 are set, and n (n ≧ 3) experts are considered to assign them to obtain Iij,11,Iij,12,L,Iij,1nDefining the maximum and minimum values thereof:
Iij,1,max＝max(Iij,11,Iij,12,L,Iij,1n),Iij,1,min＝min(Iij,11,Iij,12,L,Iij,1n)
then the expert assigns the importance degree as:
Figure BDA0001697993220000141
then, according to the support condition of the secondary knowledge unit to other secondary knowledge units, the secondary knowledge unit Z is consideredijThe other m secondary knowledge units have supporting relations, and the supporting coefficients are I respectivelyij,21,Iij,22,L,Iij,2mThen, the importance degree obtained according to the support relationship is:
Iij,2＝max(Iij,21,Iij,22,L,Iij,2m)
according to the above two importance degrees, the second-level knowledge unit Z is obtainedijAt ZiThe degree of importance of (c) is:
Iij＝max(Iij,1,Iij,2)
further, it is possible to obtain:
Iij,U＝Ii*Iij
all I arei1,U,Ii2,U,L,
Figure BDA0001697993220000142
Carrying out normalization treatment to obtain ZiThe degree of importance of all secondary knowledge points in U.
(3) Degree of importance of three levels of knowledge units
Considering the second level knowledge unit ZijAll three levels of knowledge unit Zij1，Zij2，Zij3∧ZijhTo it is in ZijThe importance degree in (2) is assigned, and the assignment rule is as follows: first for the three-level knowledge unit ZijhTen grades 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 are set, and n (n ≧ 3) experts are considered to assign them to obtain Iijk,11,Iijk,12,L,Iijk,1nDefining the maximum and minimum values thereof:
Iijk,1,max＝max(Iijk,11,Iijk,12,L,Iijk,1n),Iijk,1,min＝min(Iijk,11,Iijk,12,L,Iijk,1n)
then the expert assigns the importance degree as:
Figure BDA0001697993220000151
then setting the supporting condition of the three-level knowledge unit to other three-level knowledge units according to the three-level knowledge unit, and considering the three-level knowledge unit ZijhThe other m three-level knowledge units have supporting relations, and the supporting coefficients are I respectivelyijk,21,Iijk,22,L,Iijk,2mThen, the importance degree obtained according to the support relationship is:
Iijk,2＝max(Iijk,21,Iijk,22,L,Iijk,2m)
according to the two importance degrees, the second-level knowledge unit J of the third-level knowledge unit is obtainedijDegree of importance of:
Iijk＝max(Iijk,1,Iijk,2)
further, for ZiEach three-level knowledge unit in the system can obtain:
Figure BDA0001697993220000152
will be provided with
Figure BDA0001697993220000153
Carrying out normalization treatment to obtain ZiAll three levels of knowledge units in ZiOf importance in (b).
In the same way, by
Figure BDA0001697993220000154
Will Iijk,U,i＝1,L,N,j＝1,L,Mj,k＝1,L,
Figure BDA0001697993220000155
And normalization processing is carried out, so that the importance degree of all three-level knowledge units in the U can be obtained.
Example 4
The subject knowledge description method provided in this embodiment is different from embodiment 1 in that this embodiment further defines that the subject knowledge description further includes a knowledge quantity factor, and specifically, the subject knowledge description method further includes the following steps:
s7, calling the attribute file of the knowledge object, acquiring the time T required by learning the corresponding knowledge unit of all users who master the knowledge unit aiming at each knowledge unit, generating the knowledge quantity value of the knowledge unit according to a preset formula, storing the knowledge quantity value in the knowledge object, and updating the attribute file of the knowledge object.
To explain further, the definition of the user who grasps the knowledge unit is: when the achievement degree gamma of the user to the corresponding knowledge unit is larger than a preset threshold value mu, marking the corresponding user as the user grasping the knowledge unit, wherein the achievement degree gamma is Ax/AmWherein A ismIs the total test score of the test unit in the knowledge object in which the corresponding knowledge unit is located, AxAnd testing scores of the test units in the knowledge object where the corresponding knowledge units are located for the current user.
When a user learns knowledge of a certain subject, each knowledge unit is tested and then enters the learning of the next knowledge unit, the test result can influence the mark of the user, when the test score of the user reaches a certain threshold value, the user grasps the knowledge unit, different knowledge units have different passing tests, the same user has different test scores for different knowledge units, and therefore the marks are different for different knowledge units.
The knowledge amount of the present embodiment is described by the learning time of the user, and if the learning time of the user is longer, the knowledge amount of the knowledge unit is higher.
By way of example, the amount of knowledge is calculated as follows:
(1) knowledge quantity of secondary knowledge unit
Achievement degree of the secondary knowledge unit: by means of a secondary knowledge unit ZijTest unit F in (1)zijTesting the student, wherein the ratio r of the score to the total score of the test is the student's pair ZijThe achievement degree of learning.
Knowledge quantity of the secondary knowledge unit: suppose there are n student pairs two-level knowledge units ZijLearning is carried out, tests are carried out, and the learning time and the test results of the n students are recorded. Set and master theThe achievement threshold of the second level knowledge unit is muij(0.6≤μijLess than or equal to 1), that is, the achievement degree of a student is not less than muijThis indicates that the student mastered the knowledge unit. If m students have mastered the knowledge unit, the learning time corresponding to the knowledge unit is Tij,1,Tij,2,L Tij,mThen the secondary knowledge unit ZijIs defined as:
Figure BDA0001697993220000171
Tij,min＝min(Tij,1,Tij,2,L,Tij,m),Tij,max＝max(Tij,1,Tij,2,L,Tij,m)
(2) knowledge quantity of primary knowledge unit
Achievement degree of the primary knowledge unit: by a primary knowledge unit ZiTest unit F in (1)ZiTesting the students, wherein the ratio of the scores to the total score of the tests is the student pair ZiAchievement degree of learning.
Knowledge quantity of the primary knowledge unit: consider a primary knowledge unit ZiAll MiA second level knowledge unit Zi1，Zi2，Zi3∧ZijThe corresponding knowledge amounts are used respectively
Figure BDA0001697993220000172
And (4) showing. On the other hand, assume that there are n students paired with primary knowledge unit ZiLearning is carried out, tests are carried out, and the learning time and the test results of the n students are recorded. Setting the threshold value of achievement degree for grasping the primary knowledge unit as mui(0.6≤μiLess than or equal to 1), that is, the achievement degree of a student is not less than muiThis indicates that the student mastered the knowledge unit. If m students have mastered the knowledge unit, the learning time corresponding to the knowledge unit is Ti,1,Ti,2,L Ti,mThen the primary knowledge unit ZiIs defined as:
Figure BDA0001697993220000173
Ti,min＝min(Ti,1,Ti,2,L,Ti,m),Ti,max＝max(Ti,1,Ti,2,L,Ti,m)
wherein λ is1,λ2Are the weight coefficients.
(3) Amount of knowledge of subject knowledge
Achievement degree of subject knowledge: through the test unit FuAnd testing the students, wherein the ratio of the scores to the total score of the tests is the achievement degree of the students to the U learning.
Knowledge amount of subject knowledge: consider all N primary knowledge units Z of subject knowledge U1,Z2,L,ZNThe corresponding knowledge amounts are used respectively
Figure BDA0001697993220000181
And (4) showing. On the other hand, suppose that n students have learned the subject knowledge U and have performed a test, and the learning time and the test results of the students are recorded. The threshold value of the achievement degree for grasping the subject knowledge is set to be mu (0.6)<Mu is less than or equal to 1), namely the achievement degree of a certain student is not less than mu, the student is proved to master the subject knowledge. If m students master the subject knowledge, the learning time is T1,T2,L TMThen, the knowledge quantity of the subject knowledge U is defined as:
Figure BDA0001697993220000182
Tmin＝min(T1,T2,L,Tm),Tmax＝max(T1,T2,L,Tm)
wherein σ1,σ2Are the weight coefficients.
The discipline knowledge description method provided by the invention is introduced in detail, the principle and the implementation mode of the invention are explained by applying specific examples, each example is described in a progressive mode, and the explanation of the examples is only used for helping to understand the method and the core idea of the invention. It should be noted that various changes and modifications to the invention could be made by those skilled in the art without departing from the principle of the invention, and these changes and modifications also fall into the protection scope of the claims of the invention.

Claims (9)

1. A discipline knowledge description method is characterized by comprising the following steps:
s1, dividing the knowledge objects into X levels according to membership relations among the knowledge objects contained in subject knowledge to be described, wherein X is 1,2 and 3 … N, each X level contains one or a plurality of knowledge objects, and each knowledge object contains a description unit, a plurality of X-level knowledge units and a test unit; the knowledge object with the level of X being 1 is the whole subject knowledge and is called a parent knowledge object; the X-level knowledge objects with X levels larger than 1 are all corresponding X-1 level knowledge units in the corresponding X-1 level knowledge objects, and are called inheritance knowledge of the X-1 level knowledge objects; unique ID assignment is carried out on the description unit, the knowledge unit and the test unit of each level, and a knowledge object level relation storage file is formed, wherein the ID value comprises an attribute value, a level value and a sequence value;
s2, calling a knowledge object hierarchical relationship storage file, confirming the mutual supporting relationship of X-level knowledge units contained in the same knowledge object, marking the supporting relationship and the supported relationship of each knowledge unit, and storing the supporting relationship and the supported relationship into the knowledge object to form a knowledge unit supporting relationship storage file; the supported relation comprises a knowledge unit ID value and a supported relation assignment which play a supporting role for the current knowledge unit, and the supported relation comprises a knowledge unit ID value and a supporting relation assignment which are supported by the current knowledge unit;
s3, calling a supporting relation storage file, acquiring supporting relation and supported relation data of each knowledge unit, and forming a directed association chain between the knowledge units according to a supporting direction; acquiring all knowledge units positioned at the beginning of the directed association chain and marking the knowledge units as head-end knowledge units; acquiring all knowledge units positioned at the tail end of the directed association chain and marking the knowledge units as tail end knowledge units; each description unit is marked as a knowledge object learning inlet, each test unit is marked as a knowledge object learning outlet, and a directed association relation storage file is formed;
s4, a directed correlation storage file is called, a description unit in the same knowledge object is connected with a head-end knowledge unit, a tail-end knowledge unit in the same knowledge object is connected with a test unit, and the test units of all the next-level knowledge objects with a supporting function in the knowledge object are connected with the description units of all the next-level knowledge objects supported by the test units to form an in-layer directed correlation storage file;
s5, calling an intra-layer directed association relation storage file, acquiring an inherited knowledge X-level knowledge object belonging to the same X-1 level knowledge object, judging whether an X-level knowledge unit contained in the X-level knowledge object has an inherited knowledge X +1 level knowledge object, and acquiring an X-level description unit, an X-level test unit, all X-level head-end knowledge units and all X-level tail-end knowledge units of the X-level knowledge object when the judgment result is 'yes'; acquiring an X +1 level head end description unit of each X level head end knowledge unit and an X +1 level tail end test unit of each X level tail end knowledge unit; connecting an X-level description unit in the X-level knowledge object with the X + 1-level head end description unit; connecting an X-level test unit in the X-level knowledge object with the X + 1-level tail end test unit to form an interlayer directed incidence relation, and forming and updating a knowledge object attribute file;
the method further comprises the steps of:
s6, calling the attribute files of the knowledge units, assigning the importance degree of each X-level knowledge unit, and updating the attribute files of the knowledge objects;
the importance degree assignment comprises the importance degree I of the corresponding X-level knowledge unit in the knowledge object to which the corresponding X-level knowledge unit belongsi，IiAssignment of value I by expertsi,1And the supporting relation I of the other X-level knowledge units in the related knowledge objecti,2And (4) obtaining the product through calculation.
2. The discipline knowledge description method of claim 1, wherein the mutual supporting relationship of the X-level knowledge units contained in the same knowledge object is confirmed, and the supporting relationship and the supported relationship of each knowledge unit are marked, specifically: when X is equal to N, the support relationship is a necessary condition for learning that the current knowledge unit is the knowledge unit supported by the current knowledge unit; when X ≠ N, the support relationship is a necessary condition for learning that the X + 1-level knowledge unit included in the inheritance knowledge of β current knowledge units is the knowledge unit supported by the current knowledge unit.
3. The subject knowledge description method of claim 2, wherein the support relationship further includes a support coefficient η, when X is N, when there is a mutual support relationship between X-level knowledge units, the support coefficient η of the knowledge unit that plays a supporting role is 1; when X ≠ N, η ≠ β/α, where α is the number of X + 1-level knowledge units included in the inherited knowledge of the knowledge unit serving as a support.
4. The subject knowledge description method of claim 1, wherein Ii＝max(Ii，1，Ii，2)，
Figure FDA0003415692660000031
Ii，2＝max(Ii，21，Ii，22，…，Ii，2m) Wherein, Ii，1qAssigning the importance degree of the X-level knowledge unit in the knowledge object to which the X-level knowledge unit belongs to the expert, wherein n is the number of the experts, Ii，1，maxMaximum value assigned to expert, Ii，1，minAssigning a minimum value to the expert; i isi，2mAnd supporting coefficients of the current knowledge unit to other m X-level knowledge units in the knowledge object.
5. The subject knowledge description method of claim 1, wherein when X ≠ 1, assigning the importance level further comprises assigning an importance value I for the corresponding level X knowledge unit within the overall subject knowledgei，u。
6. The subject knowledge description method of any one of claims 1-5, further comprising the steps of:
s7, calling the attribute file of the knowledge object, obtaining the time T required by the learning of the corresponding knowledge unit of all the users who master the knowledge unit aiming at each knowledge unit, generating the knowledge quantity value of the knowledge unit, storing the knowledge quantity value in the knowledge object, and updating the attribute file of the knowledge object.
7. The subject knowledge description method of claim 6, wherein the corresponding user is marked as a user grasping the knowledge unit when a degree of achievement γ of the corresponding knowledge unit by the user is greater than a preset threshold μ, wherein the degree of achievement γ is ax/AmWherein A ismIs the total test score of the test unit in the knowledge object in which the corresponding knowledge unit is located, AxAnd testing scores of the test units in the knowledge object where the corresponding knowledge units are located for the current user.
8. The subject knowledge description method of any one of claims 1-5, 7, further comprising the steps of:
and S8, acquiring the attribute file of the knowledge object, calling N-level knowledge units with X-level as N-level, assigning values to the knowledge types of all the N-level knowledge units, wherein the knowledge types comprise a concept class, a principle class, a method class, a fact class, a viewpoint class and an instance class, storing the knowledge types in the knowledge object, and updating the attribute file of the knowledge object.
9. The subject knowledge description method of claim 6, wherein the method further comprises the steps of:
and S8, acquiring the knowledge object attribute file, calling N-level knowledge units of X-N levels, assigning knowledge types of all the N-level knowledge units, wherein the knowledge types comprise a concept class, a principle class, a method class, a fact class, a viewpoint class and an instance class, storing the knowledge types in the knowledge object, and updating the knowledge object attribute file.
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