CN111046194A - Method for constructing multi-mode teaching knowledge graph - Google Patents
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Abstract
The application provides a method for constructing a multi-modal teaching knowledge graph, which comprises the following steps: s1: constructing knowledge points of a target teaching material and attributes corresponding to the knowledge points; s2: acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, wherein the original data comprises pictures, audio or video resources; s3: and (4) processing the data of the step (S1) and the step (S2) by taking the teaching outline of the target teaching material as a theme to generate a teaching knowledge map. The knowledge points and the relation among the knowledge points of the target teaching materials are displayed in the form of the knowledge graph by constructing the multi-mode teaching knowledge graph, and the multi-mode teaching knowledge graph is integrated into the knowledge graph, so that the display form of knowledge is enriched, and the interestingness of a classroom is enhanced; meanwhile, an intelligent education interaction environment for multi-mode teaching and learning is created.
Description
Technical Field
The invention relates to the field of computer data processing, in particular to a method for constructing a multi-mode teaching knowledge graph.
Background
The personalized learning is a permanent theme of education innovation and development, and with the rapid development of technologies such as big data, artificial intelligence and the like, the regression of a new generation of artificial intelligence technology taking a knowledge graph as a core makes it possible for a teaching knowledge graph to assist teachers to realize teaching in an intelligent classroom. The knowledge graph is a knowledge base called a semantic network, namely a knowledge base with a directed graph structure, wherein nodes of the graph represent entities or concepts, edges of the graph represent various semantic relations between the entities/concepts, but most of the existing teaching knowledge graphs present knowledge points in a text form, and the presentation of the knowledge points in the text form or related information of the entities is quite monotonous and incomplete, so that the traditional knowledge graph only completes the collection and integration of character knowledge to a great extent, for students, the cooperation of multimedia resources such as pictures, audio, video and the like is not available, and boring characters cannot fully arouse the learning interest of the students.
Therefore, a knowledge graph relating to multimedia resources such as text, pictures, audio and video is needed.
Disclosure of Invention
In view of the above, the present application provides a method for multimodal teaching knowledge base.
The application provides a method for constructing a multi-modal teaching knowledge graph, which is characterized by comprising the following steps: the method comprises the following steps:
s1: constructing knowledge points of a target teaching material and attributes corresponding to the knowledge points;
s2: acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, wherein the original data comprises pictures, audio or video resources;
s3: and (4) processing the data of the step (S1) and the step (S2) by taking the teaching outline of the target teaching material as a theme to generate a teaching knowledge map.
Further, the step S1 specifically includes:
acquiring a target teaching material text resource;
preprocessing the text resource;
adopting TF-IDF to complete the extraction of knowledge points;
and after the knowledge points are extracted, inputting attributes according to the target teaching material course standard and the teaching outline.
Further, the preprocessing comprises text format conversion, word segmentation and new word combination.
Further, before acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, a knowledge extraction strategy is created.
Further, the knowledge extraction strategy comprises: and creating the knowledge extraction strategy according to the interest requirement of a preset teaching knowledge map.
Further, the step of processing the data of the step S1 and the step S2 with the teaching outline of the target textbook as a theme further includes creating a knowledge graph construction strategy before generating the teaching knowledge graph.
Further, the graph construction strategy at least comprises a knowledge point attribute mapping strategy: the knowledge point attribute mapping strategy is based on discipline teaching rules, teaching outlines and cultivation targets, and is obtained according to the directionality, the reciprocity and the transmissibility of knowledge points.
Further, the preset knowledge extraction strategy and the preset knowledge graph construction strategy process the data sets of step S1 and step S2 to generate a teaching knowledge graph, and then further include:
adjusting the knowledge extraction strategy and/or the map construction strategy;
and generating a new knowledge graph according to the adjusted knowledge extraction strategy and/or graph construction strategy.
Further, the method further comprises displaying a framework of the knowledge-graph, wherein the framework comprises entity information and attribute information of the knowledge-graph, and the entity information comprises one or more of text information, pictures, audio or video.
The beneficial technical effect of this application: the knowledge points and the relation among the knowledge points of the target teaching materials are displayed in the form of the knowledge graph by constructing the multi-mode teaching knowledge graph, and the multi-mode teaching knowledge graph is integrated into the knowledge graph, so that the display form of knowledge is enriched, and the interestingness of a classroom is enhanced; meanwhile, an interactive intelligent education of multi-mode teaching and learning is created.
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The invention is further described below with reference to the following figures and examples:
FIG. 1 is a flow chart of the knowledge-graph construction of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings in which:
the invention provides a method for constructing a multi-mode teaching knowledge graph, which is characterized by comprising the following steps of: the method comprises the following steps:
s1: constructing knowledge points of a target teaching material and attributes corresponding to the knowledge points;
s2: acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, wherein the original data comprises pictures, audio or video resources;
s3: and (4) processing the data of the step (S1) and the step (S2) by taking the teaching outline of the target teaching material as a theme to generate a teaching knowledge map.
In this embodiment, the modality refers to a specific way for a person to receive information, and includes various aspects such as visual, auditory, and spatial feelings, and may include characters, pictures, audio, video, or real objects corresponding to the presentation form of the information. The multi-mode teaching knowledge graph is not limited to organizing knowledge in a text form, and the multi-mode knowledge is tried to be added, so that the form and the content of the knowledge are enriched. The learning resources in the subject knowledge graph have multi-mode attributes, and not only comprise learning resources in a text form, but also comprise learning resources in various forms such as videos, audios and pictures, so that different learning requirements of different students are met, and the learning resources with more pertinence are provided for the students. Such as: some students like watching videos to learn, more video learning resources can be recommended for the students; and some students prefer to see the pictures, so that more subject knowledge resources in the form of images can be recommended for the students.
By the technical scheme, the multi-mode teaching knowledge map is established to show the knowledge points of the target teaching materials and the relation among the knowledge points in the map form, and the multi-mode teaching knowledge map is integrated into the knowledge map, so that the showing form of knowledge is enriched, and the interestingness of a classroom is enhanced; meanwhile, an interactive environment for multi-modal teaching and learning is created.
In this embodiment, the step S1 specifically includes:
acquiring a target teaching material text resource; the target teaching materials comprise teaching outlines, teaching materials and teacher instruction books; the teaching knowledge graph is a knowledge base for students, and is different from knowledge graphs in other fields, the data quality requirement of the teaching knowledge graph is extremely high, and the content of the teaching knowledge graph must be accurate. The main data sources of the teaching knowledge graph are strict, so the data sources used for constructing the teaching knowledge graph comprise teaching outlines, teaching materials and teacher instruction books, and the accuracy of contents is ensured from the data sources.
Preprocessing the text resource; preprocessing comprises text format conversion, word segmentation and new word combination; the text format conversion is to unify all text formats, so as to facilitate the subsequent further processing of the text;
adopting TF-IDF to complete the extraction of knowledge points;
and after the knowledge points are extracted, inputting attributes according to the target teaching material course standard and the teaching outline.
TF-IDF (term frequency-inverse document frequency) is a commonly used weighting technique for information retrieval (information retrieval) and text mining (text mining). TF-IDF is a statistical method to evaluate the importance of a word to one of a set of documents or a corpus. The importance of a word increases in proportion to the number of times it appears in a document, but at the same time decreases in inverse proportion to the frequency with which it appears in the corpus. The main idea of TF-IDF is: if a word appears in an article with a high frequency TF and rarely appears in other articles, the word or phrase is considered to have a good classification capability and is suitable for classification.
The term frequency in the TF-IDF algorithm represents the total times of occurrence of the term t in the domain document, the term frequency of the term t is calculated according to the occurrence times, and the number needs to be normalized in actual calculation. The reverse file frequency is used for distinguishing the entries in progress of the document, and the main meaning is that if the value of the reverse file frequency is increased along with the decrease of the included entries t, the entries t have better distinguishing capability. The reverse file frequency value is the total number of documents divided by the document tree containing the entry t, and the logarithm of the result is taken. Namely, it is
Wherein, tfijRepresenting the frequency of occurrence of the term t in the document j, D representing the total number in the target textbook, dfiThe number of documents showing the entry appearing in the target teaching material.
In this embodiment, before acquiring a plurality of raw data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, a knowledge extraction policy is created. And creating the knowledge extraction strategy according to the interest requirement of a preset teaching knowledge map. The proportion of videos, pictures and audios in the knowledge map can be increased for increasing interest.
And processing the data of the step S1 and the step S2 by taking the teaching outline of the target teaching material as a theme, and creating a knowledge graph construction strategy before generating the teaching knowledge graph. And processing the knowledge points, the pictures, the videos and the audio sets through a knowledge extraction strategy and a map construction strategy to generate a knowledge map. The process of generating the knowledge graph comprises knowledge extraction, attribute mapping and disambiguation normalization processing, wherein the knowledge extraction refers to element extraction and attribute value extraction of knowledge points based on target teaching materials and original data acquired from a multi-source data source, and after the knowledge extraction, the knowledge points and attributes corresponding to the original data acquired from the multi-source data source can be extracted. Disambiguation normalization refers to normalizing data directed to the same entity.
The map construction strategy at least comprises a knowledge point attribute mapping strategy: the knowledge point attribute mapping strategy is based on discipline teaching rules, teaching outlines and cultivation targets, and is obtained according to the directionality, the reciprocity and the transmissibility of knowledge points. The subject knowledge graph is not merely a stack of knowledge points or teaching resources, but includes relational connections between knowledge points, knowledge points and teaching resources. The relationship between nodes in the discipline knowledge graph has three main characteristics: firstly, the direction is the direction, the relation between knowledge points has definite direction and is not disordered and disordered; secondly, the interactivity; finally, transitivity, for example, describing the preorder relationship between knowledge points, i.e., preorder course and postcedent course, because learning is a progressive process, it is very important to find out the relationship between knowledge points after the preoccupation, and it is also very important to construct the hierarchical structure of the teaching knowledge graph.
The preset knowledge extraction strategy and the preset knowledge graph construction strategy process the data sets of the step S1 and the step S2, and after the teaching knowledge graph is generated, the method further comprises the following steps:
adjusting the knowledge extraction strategy and/or the map construction strategy;
and generating a new knowledge graph according to the adjusted knowledge extraction strategy and/or graph construction strategy. In order to meet the actual teaching requirements, teachers can modify knowledge extraction strategies or knowledge maps according to actual education purposes or teaching requirements, and therefore new knowledge maps are generated in map generation software according to the adjusted knowledge extraction strategies and/or map construction strategies, so that different education requirements can be met.
In this embodiment, the method further includes displaying a framework of the knowledge-graph, where the framework includes entity information and attribute information of the knowledge-graph, and the entity information includes one or more of text information, pictures, audio, or video. The teaching knowledge graph is constructed for showing in teaching, so that after the teaching knowledge graph is constructed, the teaching knowledge graph can be displayed on an existing display, and the showing of knowledge in teaching and learning is achieved.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (9)
1. A method for constructing a multi-modal teaching knowledge graph is characterized by comprising the following steps: the method comprises the following steps:
s1: constructing knowledge points of a target teaching material and attributes corresponding to the knowledge points;
s2: acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, wherein the original data comprises pictures, audio or video resources;
s3: and (4) processing the data of the step (S1) and the step (S2) by taking the teaching outline of the target teaching material as a theme to generate a teaching knowledge map.
2. The method of constructing a multi-modal educational knowledge graph according to claim 1, wherein: the step S1 specifically includes:
acquiring a target teaching material text resource;
preprocessing the text resource;
adopting TF-IDF to complete the extraction of knowledge points;
and after the knowledge points are extracted, inputting attributes according to the target teaching material course standard and the teaching outline.
3. The method of constructing a multi-modal educational knowledge graph according to claim 2, wherein: the preprocessing comprises text format conversion, word segmentation and new word combination.
4. The method of constructing a multi-modal educational knowledge graph according to claim 1, wherein: before acquiring a plurality of original data from a plurality of data sources according to the knowledge points and the attributes of the knowledge points, the method also comprises the step of creating a knowledge extraction strategy.
5. The method of constructing a multi-modal educational knowledge graph according to claim 4, wherein: the knowledge extraction strategy comprises the following steps: and creating the knowledge extraction strategy according to the interest requirement of a preset teaching knowledge map.
6. The method of constructing a multi-modal educational knowledge graph according to claim 1, wherein: and processing the data of the step S1 and the step S2 by taking the teaching outline of the target teaching material as a theme, and creating a knowledge graph construction strategy before generating the teaching knowledge graph.
7. The method of constructing a multi-modal educational knowledge graph according to claim 6, wherein: the map construction strategy at least comprises a knowledge point attribute mapping strategy: the knowledge point attribute mapping strategy is based on discipline teaching rules, teaching outlines and cultivation targets, and is obtained according to the directionality, the reciprocity and the transmissibility of knowledge points.
8. The method of constructing a multi-modal educational knowledge graph according to claim 7, wherein: the preset knowledge extraction strategy and the preset knowledge graph construction strategy process the data sets of the step S1 and the step S2, and after the teaching knowledge graph is generated, the method further comprises the following steps:
adjusting the knowledge extraction strategy and/or the map construction strategy;
and generating a new knowledge graph according to the adjusted knowledge extraction strategy and/or graph construction strategy.
9. The method of constructing a multi-modal educational knowledge graph according to claim 1, wherein: the method also includes displaying a framework of the knowledge-graph, the framework including entity information and attribute information of the knowledge-graph, the entity information including one or more of textual information, pictures, audio, or video.
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| CN111597356A (en) * | 2020-05-27 | 2020-08-28 | 上海乂学教育科技有限公司 | Intelligent education knowledge map construction system and method |
| CN112015907A (en) * | 2020-08-18 | 2020-12-01 | 大连东软教育科技集团有限公司 | Method and device for quickly constructing discipline knowledge graph and storage medium |
| CN112131405A (en) * | 2020-09-28 | 2020-12-25 | 中国科学技术大学 | AR tumor knowledge graph multi-mode demonstration method based on intelligent search |
| CN112287037A (en) * | 2020-10-23 | 2021-01-29 | 大连东软教育科技集团有限公司 | Multi-entity mixed knowledge graph construction method and device and storage medium |
| CN113407733A (en) * | 2021-07-12 | 2021-09-17 | 南京尚哲智能科技有限公司 | Course knowledge graph automatic generation method and system based on culture scheme |
| CN113806550A (en) * | 2021-04-15 | 2021-12-17 | 京东科技控股股份有限公司 | Generation method and device of personalized knowledge graph and computer equipment |
| CN115796132A (en) * | 2023-02-08 | 2023-03-14 | 北京大学 | Teaching material compiling method and device based on knowledge graph |
| CN117033665A (en) * | 2023-10-07 | 2023-11-10 | 成都华栖云科技有限公司 | Method and device for aligning map knowledge points with video |
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| CN117033665A (en) * | 2023-10-07 | 2023-11-10 | 成都华栖云科技有限公司 | Method and device for aligning map knowledge points with video |
| CN117033665B (en) * | 2023-10-07 | 2024-01-09 | 成都华栖云科技有限公司 | Method and device for aligning map knowledge points with video |
| CN117973526A (en) * | 2024-03-21 | 2024-05-03 | 暗物质(北京)智能科技有限公司 | Teaching video knowledge graph construction method, device, equipment and storage medium |
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Application publication date: 20200421 |