CN112084345B - Teaching guiding method and system combining body of course and teaching outline - Google Patents

Teaching guiding method and system combining body of course and teaching outline Download PDF

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CN112084345B
CN112084345B CN202010952591.8A CN202010952591A CN112084345B CN 112084345 B CN112084345 B CN 112084345B CN 202010952591 A CN202010952591 A CN 202010952591A CN 112084345 B CN112084345 B CN 112084345B
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谢波
姜波
彭潇
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Abstract

The invention relates to a method and a system for guiding learning by combining a course and an ontology of a teaching outline. The method comprises the steps of obtaining user requirements of a set application scene; determining the field and range of the application of the course and the teaching outline according to the user requirements; determining an entity according to the fields and ranges of the course and teaching outline application and a conditional random field model based on fusion characteristics; constructing a body of the course and the teaching outline according to the entity; and guiding learning according to the curriculum and the body of the teaching outline. The invention improves the accuracy of the learning guide result.

Description

Teaching guiding method and system combining body of course and teaching outline
Technical Field
The invention relates to the field of semantic search, in particular to a method and a system for guiding a body combining courses and teaching outlines.
Background
With the development of internet science and technology, new network technology has great influence in various social fields, a plurality of researches for integrating the network technology into the education field are derived, and the application of semantic web and ontology technology in the education field is already used for inventing semantic models of various learning entities. Ontology-conceptualized knowledge domain use formal language understanding can enable not only human understanding, but also computer understanding. Ontologies describe concepts in a domain and their relationships between them and provide a vocabulary for the terms used. Semantic annotations of concepts and terms can be processed and understood by humans and machines. Course and teaching outline provide important information in education organization, and play an important role in learning activities and education process, from the current situation of ontology construction research in the education field at home and abroad, most of course is taken as an example for research, and few ontology construction researches combining the course and the teaching outline are available. The resource characteristics of different fields have different ontology construction methods, and the traditional ontology construction method cannot meet the research field of people.
The contacted teaching outline is shown in a document form at present, when relevant information is searched on the network, a desired result cannot be fed back, a whole document or a large segment of characters are usually returned, and the connection between courses and the teaching outline is not well applied to actual teaching. The value students and teachers of the teaching outline cannot well explore, and at the same time, a complete system for retrieving courses and knowledge related to the teaching outline does not exist. At present, most of search systems based on key words cannot well understand the semantics of users, and returned results are not accurate enough.
Disclosure of Invention
The invention aims to provide a method and a system for guiding a student by combining a course and a body of a teaching outline, and the accuracy of a teaching guiding result is improved.
In order to achieve the purpose, the invention provides the following scheme:
a method for guiding learning by combining courses and an ontology of an instructional outline comprises the following steps:
acquiring user requirements of a set application scene;
determining the field and range of the application of the course and the teaching outline according to the user requirements;
determining an entity according to the fields and ranges of the course and teaching outline application and a conditional random field model based on fusion characteristics; the entity is a concept related to the field of courses and teaching outlines;
constructing a body of the course and the teaching outline according to the entity;
and guiding learning according to the curriculum and the body of the teaching outline.
Optionally, the determining an entity according to the fields and ranges of the course and the teaching outline application and the conditional random field model based on the fusion features specifically includes:
determining a corpus according to the field and range of the course and teaching outline application; the corpus is used for segmenting the Wikipedia page paragraphs by using a Hanlp segmentation tool and labeling;
determining parameters of the conditional random field model; the parameters include weights of the feature functions;
and determining an entity by adopting a conditional random field model with fused features according to the corpus.
Optionally, the determining an entity by using the feature-fused conditional random field model according to the corpus further includes:
layering the entities to obtain course entities and teaching outline entities;
and carrying out upper-level and lower-level layering on the course entities and the teaching outline entities.
Optionally, the building an ontology of the course and the teaching outline according to the entity further includes:
evaluating the entity construction course and the body of the teaching outline to obtain an evaluation result;
and optimizing the entity construction course and teaching outline ontology according to the evaluation result.
A system for conducting lessons in conjunction with an ontology of an instructional outline, comprising:
the user requirement acquisition module is used for acquiring the user requirement of the set application scene;
the field and range determining module is used for determining the field and range of the course and teaching outline application according to the user requirements;
the entity determining module is used for determining an entity according to the field and range of the application of the course and the teaching outline and a conditional random field model based on the fusion characteristics; the entity is a concept related to the field of courses and teaching outlines;
the body construction module of the course and the teaching outline is used for constructing the body of the course and the teaching outline according to the entity;
and the teaching guidance module is used for guiding the teaching according to the courses and the body of the teaching outline.
Optionally, the entity determining module specifically includes:
the corpus determining unit is used for determining a corpus according to the field and the range of the course and teaching outline application; the corpus is used for segmenting the Wikipedia page paragraphs by using a Hanlp segmentation tool and labeling;
a parameter determination unit of the conditional random field model for determining parameters of the conditional random field model; the parameters include weights of feature functions;
and the entity determining unit is used for determining the entity by adopting a conditional random field model with fusion characteristics according to the corpus.
Optionally, the entity determining module further includes:
the first layering unit is used for layering the entities to obtain course entities and teaching outline entities;
and the second layering unit is used for carrying out upper and lower layering on the course entity and the teaching outline entity.
Optionally, the method further includes:
the evaluation result determining module is used for evaluating the entity construction course and the body of the teaching outline to obtain an evaluation result;
and the optimization module is used for optimizing the entity construction course and teaching outline ontology according to the evaluation result.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method and the system for guiding the learning by combining the curriculum and the body of the teaching outline, the field and the range of the application of the curriculum and the teaching outline are determined according to the user requirements, so that the accuracy of the field and the range of the application of the curriculum and the teaching outline is ensured; and determining entities according to the fields and ranges of the course and teaching outline application and the conditional random field model based on the fusion characteristics, solving the problem of extraction of important entities in the ontology, performing teaching guidance by using the constructed ontology, namely performing semantic search query, solving the problem of inaccurate search results caused by the traditional keyword-based query method, and further improving the accuracy of the guidance results.
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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 needed to be used in the embodiments 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 inventive exercise.
FIG. 1 is a schematic flow chart of a method for guiding learning by combining a course and an ontology of an instructional outline provided by the present invention;
FIG. 2 is a schematic diagram of a process for constructing an ontology of a course and a teaching outline according to the present invention;
FIG. 3 is a hierarchical diagram of curriculum entities provided by the present invention;
FIG. 4 is a hierarchical view of an instructional outline entity provided by the present invention;
FIG. 5 is a schematic diagram of a system structure for a specific application of a teaching method combining a course and an ontology of an instructional outline;
FIG. 6 is a schematic structural diagram of a teaching guidance system combining a course and an ontology of a teaching outline according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for guiding a student by combining a course and a body of a teaching outline, and the accuracy of a teaching guiding result is improved.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow chart of a method for guiding a lesson and an ontology of a teaching outline provided by the present invention, and as shown in fig. 1, the method for guiding a lesson and an ontology of a teaching outline provided by the present invention includes:
s101, user requirements of the set application scene are obtained. The specific determination process is as follows:
a questionnaire about the questions of interest to the class and the teaching outline was designed for students, and then questionnaires were conducted on students in the school, and the sample of the questionnaire was about 1000 persons, to obtain the question list shown in table 2-1. And according to the problem list, assuming that the application scene of the body is effective information which the student wants to obtain from the course and the course outline document. According to the application scene of the ontology, the established ontology requirement is determined to be the structured expression of the documented course outline content, so that the ontology is fully utilized to support rich services, and course management and development are improved. Part of the question list is shown in Table 2-1:
TABLE 2-1 ontology problem List
Figure BDA0002677510790000051
Figure BDA0002677510790000061
And S102, determining the field and range of the course and teaching outline application according to the user requirements.
After S102, the method further includes:
the method is characterized in that a Google search engine and reading related articles are used for searching ontologies which are constructed at home and abroad according to the field and range of course and teaching outline application, and the sources of the Google search engine and the reading related articles mainly comprise ontology models in published articles at home and abroad and ontology knowledge bases such as DAML ontology library, SHOE and the like. The multiplexing Ontology obtained by the query result mainly comprises BBC Curriculum Ontology and Bowloga Ontology.
S103, determining an entity according to the fields and the ranges of the course and the teaching outline application and a conditional random field model based on the fusion characteristics; the entities are concepts related to the fields of courses and teaching outlines.
S103 specifically comprises the following steps:
determining a corpus according to the field and range of the course and teaching outline application; the corpus is used for segmenting the Wikipedia page paragraphs by using a Hanlp word segmentation tool and labeling the Wikipedia page paragraphs. The properties of the corpus are as follows in Table 2-2:
TABLE 2-2 curriculum and corpus related to the teaching schema
Figure BDA0002677510790000062
Figure BDA0002677510790000071
Determining parameters of the conditional random field model; the parameters include weights of the feature functions. The currently common method is maximum likelihood estimation, using D ═ X i ，Y i Denotes data-in, X i Representing a sequence of observations, Y i The output entity sequence is represented and then the parameters are solved for D. The L-BFGS was used herein in experiments to perform parameter solution by dynamic programming.
The formalization of the conditional random field is described as follows:
as a undirected graph model, the joint probability of labels in a graph of a conditional random field is defined as:
Figure BDA0002677510790000072
where Y is a class label, X is a feature vector, λ K Is f k (y i-1 ，y i X, i) the corresponding weight, z (X) is a normalization factor, and z (X) is defined as follows:
Figure BDA0002677510790000073
wherein, the characteristic f k (y i-1 ，y i X, i) by transfer feature t v′v (y i-1 ，y i X, i) and status features s v′v (y i-1 ，x i ) Composition, defined as follows:
Figure BDA0002677510790000074
wherein u and
Figure BDA0002677510790000075
are each t v′v (y i-1 ，y i X, i) and s v′v (y i-1 ，x i ) The weight of (2).
And determining an entity by adopting a conditional random field model with fused features according to the corpus.
The words marked by the word segmentation marking result are divided into target words, target word parts of speech, first prepositions words and the like. These different types of words constitute different types of entities. In order to perform feature fusion on different types of entities, firstly, entries in the education field are obtained on a Wikipedia page, a course name library composed of different course name entries is obtained, and a teaching outline entry is also obtained to form a teaching outline name library. Then, a fixed expression library is screened out manually to serve as the named entity fusion characteristics, which are shown in the table 2-3, and finally, the fusion characteristics are added on the basis of a public template of the domain entity characteristics to carry out entity identification.
Table 2-3 entity identification common feature template table
Figure BDA0002677510790000081
Tables 2-4 fixed expression library for discipline entity identification
Figure BDA0002677510790000082
Since the traditional conditional random field considers the context characteristics, the entity recognition extraction accuracy reaches a higher level. The conditional random field added with the fusion features is fused with the features with pertinence, so that the accuracy is further improved.
Determining an entity by adopting a feature-fused conditional random field model according to the corpus, and then:
and layering the entities to obtain course entities and teaching outline entities.
And performing upper-level and lower-level layering on the course entities and the teaching outline entities, wherein the upper-level and lower-level layering is respectively shown in fig. 3 and fig. 4. The upper-layer ontology and one or more lower-layer ontologies use the attribute and constraint definition of OWL language to establish the relation between the paratofs and continains.
Determining the hierarchical relation among the good classes, and defining the attributes of the classes. The definition of the attribute comprises the definition of object attribute and data attribute, and the purpose of the attribute definition is to perfect the hierarchical relationship between the classes and supplement the characteristics of the classes. Object properties describe relationships to other individuals, which may be instances of the same class or other classes. For example, a course has an object property named outline, which relates to one or more classes of outline. Data attributes contain information about an individual, an instance of the class, and no relationship to other classes. For example, in the teaching outline, the value of the attribute open time is a character string representing time in the teaching outline, and the attribute lesson code represents a numeric code of lessons of the teaching outline.
Instantiation of the course ontology is to fill in data for the class after the attribute constraint in step 6. The class in step 6 defines only one class category and has no exact instance. The example of the course ontology is filled by crawling the data of Wikipedia by using a spider, and the example of the teaching outline ontology is instantiated in a mode of analyzing an Excel file by using a POIFSFileSystemm to extract the information in the table and store the information in the mysql database and manually supplement other examples.
S104, constructing an ontology of the course and the teaching outline according to the entity, wherein a specific flow chart is shown in FIG. 2.
And semantically representing the ontology and describing the ontology by using RDF or OWL language.
And S105, conducting learning guidance according to the courses and the body of the teaching outline.
After S105, further comprising:
evaluating the entity construction course and the body of the teaching outline to obtain an evaluation result; the ontology is evaluated from consistency, integrity and validity, and two methods are mainly adopted for verifying consistency:
(1) inference verification is carried out by using a self-contained inference machine Hermist 1.3.8.413 of the Prot g tool.
(2) And adding some SWRL rules to the ontology by using a Prot g e tool, and starting an inference engine to verify the consistency of the ontology.
And optimizing the entity construction course and teaching outline ontology according to the evaluation result.
The method for guiding the learning by combining the curriculum and the body of the teaching outline is applied to a specific system, the system comprises a semantic understanding module, a body library query module, a problem expansion recommendation module and a data visualization module, and as shown in fig. 5, the demonstration research of the invention is realized through the system. The system is developed in a mode of separating a front end from a rear end, the front end is developed by adopting an MVVM mode and using a progressive frame Vue. The system is mainly divided into three modules for realization: 1) a semantic understanding module: the semantic understanding module mainly uses HanLP to perform relevant processing on natural sentences input by users; 2) ontology library query module: the ontology base is mainly queried by constructing a SPARQL query statement; 3) the problem extension recommendation module: using a cosine similarity algorithm to calculate sentence similarity and returning to a problem list; 4) data visualization: the front end visualization mainly displays the answers of the questions in the form of a knowledge card, displays a logic structure by a chart, and returns the recommended questions in the form of a list.
The cosine similarity calculation of the question recommendation module:
storing the questions input by the user into the database every time, performing semantic similarity calculation with sentences in the question list in the database when the questions are inquired next time, returning the sentences with high similarity to generate the question list and returning the question list.
Cosine distance, also called cosine similarity, is a measure of the magnitude of the difference between two individuals using the cosine value of the angle between two vectors in a vector space. The cosine of the angle θ between vector a and vector b is calculated as follows:
Figure BDA0002677510790000101
if the vectors a and b are not two-dimensional but n-dimensional, the above cosine calculation is still correct. Assuming that a and b are two n-dimensional vectors, a is, b is, the cosine of the angle θ between a and b is equal to:
Figure BDA0002677510790000102
fig. 6 is a schematic structural diagram of a teaching system combining a course and an ontology of a teaching outline, as shown in fig. 6, the teaching system combining a course and an ontology of a teaching outline provided by the present invention includes: the system comprises a user requirement acquisition module 601, a domain and scope determination module 602, an entity determination module 603, a course and teaching outline ontology construction module 604 and a teaching guidance module 605.
The user requirement obtaining module 601 is configured to obtain a user requirement of a set application scenario;
the domain and scope determining module 602 is configured to determine a domain and scope of the course and the teaching outline application according to the user requirement;
the entity determining module 603 is configured to determine an entity according to the field and range of the application of the course and the teaching outline and a conditional random field model based on the fusion features; the entity is a concept related to the field of courses and teaching outlines;
the body construction module 604 of the course and teaching outline is used for constructing a body of the course and teaching outline according to the entity;
the teaching guidance module 605 is used for guiding the teaching according to the curriculum and the body of the teaching outline.
The entity determining module 603 specifically includes: a corpus determining unit, a parameter determining unit and an entity determining unit of the conditional random field model.
The corpus determining unit is used for determining a corpus according to the field and range of the course and teaching outline application; the corpus is used for segmenting the Wikipedia page paragraphs by using a Hanlp segmentation tool and labeling;
the parameter determining unit of the conditional random field model is used for determining the parameters of the conditional random field model; the parameters include weights of the feature functions;
and the entity determining unit is used for determining an entity by adopting a conditional random field model with fusion characteristics according to the corpus.
The entity determining module 603 further comprises: a first layered unit and a second layered unit.
The first layering unit is used for layering the entities to obtain course entities and teaching outline entities;
and the second layering unit is used for carrying out upper and lower layering on the course entity and the teaching outline entity.
The invention provides a teaching guidance system combining a course and a body of a teaching outline, which further comprises: an evaluation result determining module and an optimizing module.
The evaluation result determining module is used for evaluating the entity construction course and the body of the teaching outline to obtain an evaluation result;
and the optimization module is used for optimizing the entity construction course and teaching outline ontology according to the evaluation result.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (4)

1. A method for guiding learning by combining courses and an ontology of a teaching outline is characterized by comprising the following steps:
acquiring user requirements of a set application scene;
determining the field and range of the application of the course and the teaching outline according to the user requirements;
determining an entity according to the fields and ranges of the course and teaching outline application and a conditional random field model based on fusion characteristics; the entity is a concept related to the field of courses and teaching outlines;
constructing a body of the course and the teaching outline according to the entity;
conducting teaching according to the curriculum and the body of the teaching outline;
the determining of the entity according to the fields and the ranges of the course and the teaching outline application and the conditional random field model based on the fusion characteristics specifically comprises the following steps:
determining a corpus according to the field and range of the course and teaching outline application; the corpus is used for segmenting the Wikipedia page paragraphs by using a Hanlp segmentation tool and labeling;
determining parameters of the conditional random field model; the parameters include weights of the feature functions;
determining an entity by adopting a conditional random field model with fusion characteristics according to the corpus;
determining an entity by adopting a conditional random field model with fused features according to the corpus, and then:
layering the entities to obtain course entities and teaching outline entities;
carrying out upper and lower level layering on the course entities and the teaching outline entities;
determining the hierarchical relation between the good classes, and defining the attributes of the classes; the definition of attributes includes the definition of object attributes and data attributes.
2. The method as claimed in claim 1, wherein the step of building an ontology of courses and outline according to the entity further comprises:
evaluating the entity construction course and the body of the teaching outline to obtain an evaluation result;
and optimizing the entity construction course and teaching outline ontology according to the evaluation result.
3. A system of leading learning of body that combines course and outline of education which characterized in that includes:
the user requirement acquisition module is used for acquiring the user requirement of the set application scene;
the field and range determining module is used for determining the field and range of the course and teaching outline application according to the user requirements;
the entity determining module is used for determining an entity according to the fields and the ranges of the course and the teaching outline application and a conditional random field model based on the fusion characteristics; the entity is a concept related to the field of courses and teaching outlines;
the body construction module of the course and the teaching outline is used for constructing the body of the course and the teaching outline according to the entity;
the teaching guidance module is used for guiding teaching according to the courses and the body of the teaching outline;
the entity determination module specifically includes:
the corpus determining unit is used for determining a corpus according to the field and the range of the course and teaching outline application; the corpus is used for segmenting the Wikipedia page paragraphs by using a Hanlp segmentation tool and labeling;
a parameter determination unit of the conditional random field model for determining parameters of the conditional random field model; the parameters include weights of the feature functions;
the entity determining unit is used for determining an entity by adopting a conditional random field model with fusion characteristics according to the corpus;
the entity determination module further comprises:
the first layering unit is used for layering the entities to obtain course entities and teaching outline entities;
the second layering unit is used for carrying out upper and lower level layering on the course entity and the teaching outline entity; determining the hierarchical relation between the good classes, and defining the attributes of the classes; the definition of attributes includes the definition of object attributes and data attributes.
4. The teaching system of claim 3, wherein said teaching system further comprises:
the evaluation result determining module is used for evaluating the entity construction course and the body of the teaching outline to obtain an evaluation result;
and the optimization module is used for optimizing the entity construction course and teaching outline ontology according to the evaluation result.
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