CN112069327A - Knowledge graph construction method and system for teaching resources of online education classroom - Google Patents
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Abstract
The invention discloses a knowledge graph construction method of on-line education classroom teaching resources, which specifically comprises the following steps: obtaining key words in teaching resources by an NLTK-word frequency statistical method, forming a feature set, storing teaching contents corresponding to the key words in a database, and forming data links; searching and matching associated vocabularies reflecting the associated attributes among the key vocabularies in the teaching resources by using a binary tree according to the key vocabularies; performing feature classification on key words in the feature set by using k-means clustering according to the associated words, and forming a logic frame according to a classification result; adding key words into corresponding nodes in the logic framework and fusing to form a knowledge graph; and updating the knowledge graph in real time according to the key vocabulary and the associated vocabulary acquired according to the progress of the online education course. The knowledge map is more consistent with the real-time teaching thought of an online classroom, the error of the key points of knowledge is small, the logic thought is clear and consistent, the key points and key points of knowledge are displayed in real time and prominently along with the progress of the online education classroom, and the attention of students is attracted constantly.
Description
Technical Field
The invention relates to the technical field of network education, in particular to a knowledge graph construction method and a knowledge graph construction system for teaching resources of an online education classroom.
Background
With the rapid development of internet technology, the large-scale popularization of intelligent terminal devices such as smart phones and tablet computers and the like, mobile network resources such as 4G and the like are not scarce any more, and digitization and mobile online learning become new ways for people to accept education. Online education, also known as distance education and online learning, refers to a method for content dissemination and fast learning through application information technology and internet technology. Compared with the traditional education mode, the network education has the advantages of dispersed learning time, unlimited learning places, strong content targeting, high online interaction efficiency, repeated learning and the like.
The Knowledge Graph (also called scientific Knowledge Graph) is a concept in the field of book informatics, is used for drawing, analyzing and displaying the interrelationship between subjects or academic research subjects, and is a visual tool for revealing and displaying the development process and the structural relationship of scientific Knowledge. In most cases, the knowledge graph is represented visually by a graph structure, and nodes are used for representing authors, academic institutions, scientific literature or keywords, and connecting lines are used for representing relationships among the nodes. However, in network education, the construction of the knowledge graph is mostly completed only according to text information in courseware, and the key points of knowledge shown after the knowledge graph is constructed have certain deviation; furthermore, the logical order between the knowledge points is chaotic. For students and teachers participating in online education, the thought of after-class warm study, thought arrangement and key review cannot be consistent with the thought of online education classes, and the knowledge point absorption is weak; meanwhile, for new students who study in class, the learning efficiency of the new students is greatly different from that of real-time online learning.
Therefore, how to research and design a knowledge graph construction method and a knowledge graph construction system for teaching resources of online education classes is a problem which is urgently needed to be solved at present.
Disclosure of Invention
The invention aims to provide a knowledge map construction method and a knowledge map construction system for teaching resources of an online education classroom, which aim to solve the problems that the existing online education knowledge map construction knowledge key points have certain deviation, disordered logic sequence and inconsistent teaching ideas cannot be consistent with a real-time online education classroom.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, a knowledge graph construction method for teaching resources of an online education classroom is provided, which comprises the following steps:
obtaining key words in teaching resources by an NLTK-word frequency statistical method, forming a feature set, storing teaching contents corresponding to the key words in a database, and forming data links;
searching and matching associated vocabularies reflecting the associated attributes among the key vocabularies in the teaching resources by using a binary tree according to the key vocabularies;
performing feature classification on key words in the feature set by using k-means clustering according to the associated words, and forming a logic frame according to a classification result;
adding key words into corresponding nodes in the logic framework and fusing to form a knowledge graph;
and updating the knowledge graph in real time according to the key vocabulary and the associated vocabulary acquired according to the progress of the online education course.
Preferably, the key vocabulary acquisition specifically includes:
traversing text information in teaching resources to obtain high-frequency words, and converting the high-frequency words into voice data;
identifying and matching the corresponding voice sections in the voice information of the teaching resources through a voice identification technology; if the matching fails, filtering high-frequency words; if the matching is successful:
judging whether the corresponding high-frequency vocabulary in the matched voice section is a fake high-frequency vocabulary or not according to the semantic parameters; if yes, filtering; if not, the words are reduced to key words.
Preferably, the key words in the feature set are subjected to data dimension reduction by a PCA analysis method, and then main key words are extracted.
Preferably, the logical framework specifically includes: and adjusting the frame size and/or shape of the corresponding node of the key vocabulary in the same-level characteristics according to the comment time value of the key vocabulary.
Preferably, the knowledge-graph specifically comprises: and adjusting the color category and/or the color depth inside the node frame corresponding to the key words in the same-level characteristics according to the key word evaluation information and the problem feedback information.
In a second aspect, a knowledge graph construction system for teaching resources in an online education classroom is provided, which includes:
the data acquisition module is used for acquiring key words in the teaching resources by an NLTK-word frequency statistical method to form a feature set, storing teaching contents corresponding to the key words in a database and forming data links;
the data matching module is used for searching and matching the associated vocabulary reflecting the associated attribute between the key vocabularies in the teaching resources by using a binary tree according to the key vocabularies;
the frame construction module is used for performing feature classification on key words in the feature set by using k-means clustering according to the associated words and forming a logic frame according to a classification result;
the map generation module is used for adding the key vocabulary into the corresponding nodes in the logic framework and fusing the key vocabulary to form a knowledge map;
and the map updating module is used for updating the knowledge map in real time according to the key vocabulary and the associated vocabulary acquired according to the progress of the online education course.
Preferably, the data acquisition module includes:
the acquisition unit is used for traversing text information in the teaching resources to acquire high-frequency words and converting the high-frequency words into voice data;
the matching unit is used for identifying and matching the corresponding voice sections in the voice information of the teaching resources through a voice identification technology; if the matching fails, filtering high-frequency words;
the judging unit is used for judging whether the corresponding high-frequency vocabulary in the matched voice section is a fake high-frequency vocabulary or not according to the semantic parameters after the matching unit is matched successfully; if yes, filtering; if not, the words are reduced to key words.
Preferably, the data acquisition module comprises a dimension reduction unit; and the dimension reduction unit is used for performing data dimension reduction on the keyword collection in the feature set by using a PCA analysis method and extracting main key words.
Preferably, the frame building module comprises a first adjusting unit; the first adjusting unit is used for adjusting the size and/or shape of the frame of the corresponding node of the key vocabulary in the same-level characteristics according to the comment time value of the key vocabulary.
Preferably, the map generation module comprises a second adjustment unit; and the second adjusting unit is used for adjusting the color category and/or the color depth inside the node frame corresponding to the key vocabulary in the same-level characteristics according to the key vocabulary evaluation information and the problem feedback information.
Compared with the prior art, the invention has the following beneficial effects: the invention combines the voice information and the text information in the online classroom, and the knowledge map constructed by the extracted key vocabulary and the associated vocabulary better conforms to the online classroom real-time teaching thought, the error of the key points of knowledge is small, the logic thought is clear and consistent, and students and teachers provide powerful support for temperature study, thought arrangement and key review after classes; meanwhile, by strengthening construction of knowledge key points, difficulty points and classroom feedback information, the learning efficiency of a new student learning under class is not much different from the efficiency of the student learning on line in real time; in addition, the knowledge key points and key points can be shown in real time and prominently along with the progress of the online education classroom, and the attention of students can be attracted constantly.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flowchart in example 1 of the present invention;
fig. 2 is a frame diagram in embodiment 2 of the present invention.
In the figure: 101. a data acquisition module; 102. an acquisition unit; 103. a matching unit; 104. a judgment unit; 105. a dimension reduction unit; 106. a data matching module; 107. a framework building module; 108. a first adjusting unit; 109. a map generation module; 110. a second adjusting unit; 111. and the map updating module.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.
Example 1: a knowledge graph construction method of on-line education classroom teaching resources comprises the following steps:
the method comprises the following steps: the method comprises the steps of obtaining key words in teaching resources through an NLTK-word frequency statistical method, forming a feature set, storing teaching contents corresponding to the key words in a database, and forming data links. Through data link, the text information and the voice information can be directly obtained by clicking after the knowledge graph is constructed, and a large amount of time is not needed to be spent on searching knowledge contents.
The key vocabulary acquisition specifically comprises the following steps:
(1) text information in teaching resources is traversed to obtain high-frequency words, the high-frequency words are converted into voice data, and the high-frequency words are converted into the voice data, so that the calculated amount can be reduced.
(2) And identifying and matching the corresponding voice sections in the voice information of the teaching resources by a voice identification technology.
And if the matching fails, filtering the high-frequency vocabulary. For example: if the text information appears for many times but the speech information does not appear, the corresponding key words are not the key words of the knowledge points, or the importance of the knowledge points is weaker, so the knowledge points are filtered.
If the matching is successful:
judging whether the corresponding high-frequency vocabulary in the matched voice section is a fake high-frequency vocabulary or not according to the semantic parameters; if yes, filtering; if not, the words are reduced to key words. The semantic parameters include, but are not limited to, parameters such as mood, sentence meaning, sentence break, and the like. For example: the acquired key vocabulary is 'normal', and the vocabulary is a common vocabulary and is irrelevant to the classroom knowledge point, but is also judged as the key vocabulary due to higher occurrence frequency. At this time, whether the key vocabulary is a pseudo vocabulary or not is recognized according to the semantic parameters through a voice recognition technology. Here, "normal" is judged as a pseudo word.
The key words in the feature set are subjected to data dimension reduction through a PCA analysis method, main key words are extracted, the key words are continuously increased along with the advance of an online education classroom, and the PCA analysis method is used for removing part of the key words with low contribution rate in the feature set, so that the knowledge graph convenient to update in real time is clear, concise and clear in structure.
Step two: and searching and matching the associated vocabulary reflecting the associated attribute between the key vocabularies by using a binary tree in the teaching resources according to the key vocabularies. For example: the 'A comprises B, C', wherein A, B, C is key words, and the 'comprise' is associated words which can clearly show the logical sequence among all knowledge points, so that the constructed knowledge graph has clear structure and reasonable sequencing.
Step three: and performing feature classification on the key words in the feature set by using k-means clustering according to the associated words, and forming a logic frame according to a classification result. After the key vocabulary is subjected to feature classification, subordinate features are conveniently constructed into hidden nodes, and subordinate nodes can be displayed by clicking superior nodes, so that the occupied area of the constructed knowledge graph is small.
The logic framework specifically comprises: and adjusting the frame size and/or shape of the corresponding node of the key vocabulary in the same-level characteristics according to the comment time value of the key vocabulary, strengthening and optimizing partial nodes, and highlighting the importance of the knowledge points.
Step four: adding the key words into corresponding nodes in the logic framework, fusing to form a knowledge graph, loading the key words and corresponding data links into the corresponding nodes, clicking different operation commands of the nodes to obtain the subordinate nodes and the corresponding teaching contents stored in the database.
The knowledge graph specifically comprises the following steps: and adjusting the color category and/or the color depth inside the node frame corresponding to the key words in the peer characteristics according to the key word evaluation information and the problem feedback information, and performing reinforced optimization on part of knowledge points which are easy to error, difficult and disputed, so that newly-added students can conveniently perform reinforced learning.
Step five: and updating the knowledge graph in real time according to the key vocabulary and the associated vocabulary acquired according to the progress of the online education course. The invention acquires data in real time along with the advance of an online education classroom, and updates the knowledge map once after newly acquiring one or preset number of key words.
Example 2: a knowledge graph construction system of online education classroom teaching resources comprises a data acquisition module 101, a data matching module 106, a framework construction module 107, a graph generation module 109 and a graph updating module 111.
The data acquisition module 101 is configured to acquire a key word in the teaching resource by an NLTK-word frequency statistical method, form a feature set, store teaching content corresponding to the key word in a database, and form a data link.
And the data matching module 106 is used for searching and matching the associated vocabulary reflecting the associated attributes among the key vocabularies in the teaching resources by using a binary tree according to the key vocabularies.
And the frame construction module 107 is used for performing feature classification on the key words in the feature set by using k-means clustering according to the associated words and forming a logic frame according to a classification result.
And the map generation module 109 is used for adding the key vocabulary into the corresponding nodes in the logical framework and fusing to form the knowledge map.
And the map updating module 111 is used for updating the knowledge map in real time according to the key vocabulary and the associated vocabulary acquired according to the progress of the online education course.
The data acquisition module 101 includes an acquisition unit 102, a matching unit 103, and a determination unit 104. The obtaining unit 102 is configured to traverse text information in the teaching resource to obtain a high-frequency vocabulary, and convert the high-frequency vocabulary into voice data. The matching unit 103 is used for identifying and matching the corresponding voice sections in the voice information of the teaching resources through a voice identification technology; and if the matching fails, filtering the high-frequency vocabulary. The judging unit 104 is configured to, after the matching unit 103 succeeds in matching, judge whether a corresponding high-frequency vocabulary in the matched speech segment is a pseudo high-frequency vocabulary according to the semantic parameters; if yes, filtering; if not, the words are reduced to key words.
The data acquisition module 101 comprises a dimension reduction unit 105; the dimension reduction unit 105 is configured to perform data dimension reduction on the keyword collection in the feature set by using a PCA analysis method, and then extract main keyword vocabulary.
The frame construction module 107 includes a first adjustment unit 108; the first adjusting unit 108 is configured to adjust the size and/or shape of the frame of the node corresponding to the key vocabulary in the peer feature according to the key vocabulary comment time value.
The atlas generation module 109 includes a second adjustment unit 110; the second adjusting unit 110 is configured to adjust a color category and/or a color depth inside a node frame corresponding to a key vocabulary in the peer feature according to the key vocabulary evaluation information and the problem feedback information.
The working principle is as follows: the invention combines the voice information and the text information in the online classroom, and the knowledge map constructed by the extracted key vocabulary and the associated vocabulary better conforms to the online classroom real-time teaching thought, the error of the key points of knowledge is small, the logic thought is clear and consistent, and students and teachers provide powerful support for temperature study, thought arrangement and key review after classes; meanwhile, by strengthening construction of knowledge key points, difficulty points and classroom feedback information, the learning efficiency of a new student learning under class is not much different from the efficiency of the student learning on line in real time; in addition, the knowledge key points and key points can be shown in real time and prominently along with the progress of the online education classroom, and the attention of students can be attracted constantly.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (10)
1. A knowledge graph construction method of on-line education classroom teaching resources is characterized by comprising the following steps:
obtaining key words in teaching resources by an NLTK-word frequency statistical method, forming a feature set, storing teaching contents corresponding to the key words in a database, and forming data links;
searching and matching associated vocabularies reflecting the associated attributes among the key vocabularies in the teaching resources by using a binary tree according to the key vocabularies;
performing feature classification on key words in the feature set by using k-means clustering according to the associated words, and forming a logic frame according to a classification result;
adding key words into corresponding nodes in the logic framework and fusing to form a knowledge graph;
and updating the knowledge graph in real time according to the key vocabulary and the associated vocabulary acquired according to the progress of the online education course.
2. The method for constructing the knowledge graph of the online education classroom teaching resource as claimed in claim 1, wherein the key vocabulary acquisition is specifically:
traversing text information in teaching resources to obtain high-frequency words, and converting the high-frequency words into voice data;
identifying and matching the corresponding voice sections in the voice information of the teaching resources through a voice identification technology; if the matching fails, filtering high-frequency words; if the matching is successful:
judging whether the corresponding high-frequency vocabulary in the matched voice section is a fake high-frequency vocabulary or not according to the semantic parameters; if yes, filtering; if not, the words are reduced to key words.
3. The method as claimed in claim 1, wherein the key words in the feature set are subjected to data dimension reduction by PCA analysis to extract main key words.
4. The method for constructing the knowledge graph of the online education classroom teaching resource as claimed in claim 1, wherein the logical framework is specifically: and adjusting the frame size and/or shape of the corresponding node of the key vocabulary in the same-level characteristics according to the comment time value of the key vocabulary.
5. The method for constructing the knowledge graph of the online education classroom teaching resource as claimed in claim 1, wherein the knowledge graph is specifically: and adjusting the color category and/or the color depth inside the node frame corresponding to the key words in the same-level characteristics according to the key word evaluation information and the problem feedback information.
6. A knowledge graph construction system of online education classroom teaching resources is characterized by comprising the following components:
the data acquisition module (101) is used for acquiring key words in the teaching resources by an NLTK-word frequency statistical method to form a feature set, storing teaching contents corresponding to the key words in a database and forming data links;
the data matching module (106) is used for searching and matching the associated vocabulary reflecting the associated attribute between the key vocabularies in the teaching resources by using a binary tree according to the key vocabularies;
the frame construction module (107) is used for performing feature classification on key words in the feature set by using k-means clustering according to the associated words and forming a logic frame according to a classification result;
the map generation module (109) is used for adding the key words into corresponding nodes in the logic framework and fusing the key words to form a knowledge map;
and the map updating module (111) is used for updating the knowledge map in real time according to the key vocabulary and the associated vocabulary acquired according to the progress of the online education course.
7. The system of claim 6, wherein the data collection module (101) comprises:
the acquisition unit (102) is used for traversing text information in the teaching resources to acquire high-frequency words and converting the high-frequency words into voice data;
the matching unit (103) is used for identifying and matching the corresponding voice sections in the voice information of the teaching resources through a voice identification technology; if the matching fails, filtering high-frequency words;
the judging unit (104) is used for judging whether the corresponding high-frequency vocabulary in the matched voice section is a fake high-frequency vocabulary or not according to the semantic parameters after the matching unit (103) succeeds in matching; if yes, filtering; if not, the words are reduced to key words.
8. The system of claim 6, wherein the data collection module (101) comprises a dimension reduction unit (105); and the dimension reduction unit (105) is used for performing data dimension reduction on the key word collections in the feature set by using a PCA analysis method and extracting main key words.
9. The knowledge-graph construction system of online education classroom teaching resources of claim 6 wherein said framework construction module (107) includes a first tuning unit (108); the first adjusting unit (108) is used for adjusting the frame size and/or shape of the corresponding node of the key vocabulary in the same-level characteristics according to the key vocabulary comment time value.
10. The knowledge graph construction system of online education classroom teaching resources of claim 6, wherein the graph generation module (109) includes a second adjustment unit (110); and the second adjusting unit (110) is used for adjusting the color category and/or the color depth inside the node frame corresponding to the key vocabulary in the peer characteristics according to the key vocabulary evaluation information and the problem feedback information.
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| CN113268602A (en) * | 2021-03-29 | 2021-08-17 | 江西融思科技有限公司 | Tissue knowledge graph construction method and device |
| CN113297338A (en) * | 2021-07-27 | 2021-08-24 | 平安科技（深圳）有限公司 | Method, device and equipment for generating product recommendation path and storage medium |
| CN113486124A (en) * | 2021-05-24 | 2021-10-08 | 山东佳联电子商务有限公司 | Bank bad asset management and management system, method, equipment and storage medium based on PCA and knowledge graph technology |
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| 姚巧红;武亚男;李爽;李玉斌;郑宏亮;: "基于关键词聚类的翻转课堂研究分析", 电化教育研究 * |
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| CN112579796A (en) * | 2020-12-30 | 2021-03-30 | 南京云起网络科技有限公司 | Knowledge graph construction method for teaching resources of online education classroom |
| CN113268602A (en) * | 2021-03-29 | 2021-08-17 | 江西融思科技有限公司 | Tissue knowledge graph construction method and device |
| CN113486124A (en) * | 2021-05-24 | 2021-10-08 | 山东佳联电子商务有限公司 | Bank bad asset management and management system, method, equipment and storage medium based on PCA and knowledge graph technology |
| CN113486124B (en) * | 2021-05-24 | 2022-02-11 | 山东佳联电子商务有限公司 | Bank bad asset management and management system, method, equipment and storage medium based on PCA and knowledge graph technology |
| CN113297338A (en) * | 2021-07-27 | 2021-08-24 | 平安科技（深圳）有限公司 | Method, device and equipment for generating product recommendation path and storage medium |
| CN113535982A (en) * | 2021-07-27 | 2021-10-22 | 南京邮电大学盐城大数据研究院有限公司 | Big data-based teaching system |
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