CN111159356A - Knowledge graph construction method based on teaching content - Google Patents
Knowledge graph construction method based on teaching content Download PDFInfo
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- CN111159356A CN111159356A CN201911410556.7A CN201911410556A CN111159356A CN 111159356 A CN111159356 A CN 111159356A CN 201911410556 A CN201911410556 A CN 201911410556A CN 111159356 A CN111159356 A CN 111159356A
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
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- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
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Abstract
The invention discloses a knowledge graph construction method based on teaching contents, which comprises the following steps: s1, classifying the teaching contents according to teaching subjects to obtain teaching subject contents; s2, extracting information of the teaching subject content to obtain extracted information; s3, aligning entities in the extracted information by adopting a semantic similarity calculation method to obtain fusion information; s4, judging whether the newly added teaching content in the fusion information meets the quality standard; and S5, constructing a knowledge graph based on the fusion information after judgment processing. The knowledge graph construction method based on the teaching content can accurately and efficiently carry out similarity processing on the entity semantics of the teaching content and can improve the fusion quality of the teaching content.
Description
Technical Field
The invention relates to the field of knowledge graphs, in particular to a knowledge graph construction method based on teaching contents.
Background
The knowledge map is used as a core and a foundation for the transition from perception intelligence to cognitive intelligence of artificial intelligence, and becomes one of key technologies for the transition from networking to intelligent upgrading of various industries; at present, the academic world has not formed a unified definition for the concept of educational knowledge graph, and scholars have described the concept from different research perspectives, and the existing research can be roughly divided into the following three categories: (1) from the perspective of knowledge organization, the educational knowledge graph can be regarded as a knowledge network graph formed by knowledge points and semantic relations thereof, and the educational domain knowledge represented by the educational knowledge graph comprises individual knowledge structures and group wisdom. (2) From the cognitive perspective of the learner, the educational knowledge graph aims to express different elements related in the teaching process and various cognitive relations with educational significance, and state information of the learner on knowledge mastering is superposed on the basis of the knowledge graph, so that a cognitive diagram of the learner can be formed. (3) From the perspective of knowledge service, the educational knowledge graph can form a learning path facing knowledge learning and capability culture under the technical support of big data, artificial intelligence and the like on the basis of representing subject knowledge and knowledge relationship; the education knowledge map can also carry out semantic connection on subject knowledge and teaching resource entities in a standardized and formalized mode, so that the effective organization of online education resources is realized.
At present, the study of the first class of education knowledge graph is common, however, the class of study is only based on the analysis and extraction of the original data source, and the simple data processing is performed, and finally, the knowledge graph corresponding to the data is formed, and the accurate and efficient similarity processing is not performed on the data, and the fusion quality of the data is not effectively improved.
Therefore, in order to solve the above problems, a knowledge graph construction method based on teaching contents is needed, which can accurately and efficiently perform similarity processing on entity semantics of the teaching contents and can improve the fusion quality of the teaching contents.
Disclosure of Invention
In view of the above, the present invention is to overcome the defects in the prior art, and provide a knowledge graph construction method based on teaching contents, which can accurately and efficiently perform similarity processing on entity semantics of the teaching contents, and can improve the fusion quality of the teaching contents.
The invention discloses a knowledge graph construction method based on teaching contents, which comprises the following steps:
s1, classifying the teaching contents according to teaching subjects to obtain teaching subject contents;
s2, extracting information of the teaching subject content to obtain extracted information; the information extraction includes: entity extraction, relationship extraction and event extraction;
s3, aligning entities in the extracted information by adopting a semantic similarity calculation method to obtain fusion information;
s4, judging whether the newly added teaching contents in the fusion information meet the quality standard, and if so, retaining the newly added teaching contents in the fusion information; if not, removing the newly added teaching contents from the fusion information;
and S5, constructing a knowledge graph based on the fusion information after judgment processing.
Further, in step S2, the entity extracts an atomic information element for extracting the content of the teaching subject; the relation extraction is used for extracting semantic relations among a plurality of different entities in the teaching subject contents; and the event extraction is used for judging the category of the teaching subject content.
Further, in step S3, the calculation of semantic similarity between entities is determined according to the following formula:
where pmi (A, B) is the similarity between entity A and entity B; p (A, B) is the correlation probability of the entity A and the entity B; p (A) is the probability of occurrence of entity A; p (B) is the probability of occurrence of entity B.
Further, in step S4, the determining whether the newly added teaching content in the fusion information meets the quality standard includes:
s41, establishing a quality evaluation model; the quality evaluation model is as follows:
wherein, L is quality grade; k is a radical ofiTo the knowledge obtained; k is a supervision wordFeeding; m is the knowledge number of newly added teaching contents in the fusion information; i is a mark symbol and takes the value as a positive integer;
s42, calculating according to the quality evaluation model to obtain quality grades, and carrying out normalization processing on the quality grades to obtain a quality grade sequence Ln(ii) a Wherein n is the number of grades and takes the value of a positive integer;
s43, setting a quality grade threshold value η, and judging a quality grade sequence LnQuality level L corresponding to newly added teaching contentjAnd if the value is larger than the quality level threshold η, the newly added teaching content does not accord with the quality standard, otherwise, the newly added teaching content accords with the quality standard, wherein j is a mark symbol and takes a positive integer from 0 to n.
The invention has the beneficial effects that: the invention discloses a knowledge graph construction method based on teaching contents, which comprises the steps of extracting information of the teaching contents, carrying out similarity processing on extracted information entity semantics to obtain fusion information of the teaching contents, carrying out quality grade processing on the fusion information quality, and finally constructing the knowledge graph of the teaching contents based on the processed fusion information, thereby obtaining the teaching knowledge graph with clear structural lines and accurate data classification.
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings, in which:
the invention discloses a knowledge graph construction method based on teaching contents, which comprises the following steps:
s1, classifying the teaching contents according to teaching subjects to obtain teaching subject contents; in this embodiment, the teaching contents are classified according to different teaching subjects, for example, the teaching subjects include languages, mathematics, english, history, geography, and the like, and the teaching contents of different subjects are classified into one category, so that a plurality of teaching subjects of different subjects are obtained.
S2, extracting information of the teaching subject content to obtain extracted information; the information extraction includes: entity extraction, relationship extraction and event extraction;
s3, aligning entities in the extracted information by adopting a semantic similarity calculation method to obtain fusion information;
s4, judging whether the newly added teaching contents in the fusion information meet the quality standard, and if so, retaining the newly added teaching contents in the fusion information; if not, removing the newly added teaching contents from the fusion information;
and S5, constructing a knowledge graph based on the fusion information after judgment processing.
In this embodiment, in step S2, information extraction is performed on the content of the teaching subject, that is, knowledge extraction is performed from data sources of different grades and different chapters. The knowledge extraction comprises entity extraction, relation extraction and event extraction;
the entity extraction is used for extracting atomic information elements in teaching subject contents, and generally comprises labels such as terms, definitions/concepts, time/date, affiliation and the like, and the specific label definition can be adjusted according to different tasks; wherein the entity extraction further involves entity identification and linking; the entity recognition is to recognize the entity in the sentence or text, and the link is to link the entity with the corresponding entity in the knowledge base. The method relates to identification and disambiguation technology of entities.
The relation extraction is used for extracting semantic relations among a plurality of different entities in the teaching subject contents; the method comprises the steps of extracting relations based on a machine learning method, specifically, predefining the categories of the relations, manually labeling some data, designing feature representation for the data, selecting a classification method to classify the data (such as SVM, NN, naive Bayes and other methods) to obtain classification results, and evaluating the classification results.
The event extraction is used for judging the category of the teaching subject content, extracting event information interested by a user from natural language, and presenting the event information in a structured form, such as the time, the place, the occurrence reason, participants and the like of the event occurrence. Specifically, an event extraction method based on a dynamic multi-pooling convolutional neural network is used for extracting events of the teaching subject contents.
In this embodiment, in step S3, after information extraction is completed, the entities are aligned according to semantic similarity by using a natural language processing technique, so as to eliminate contradiction, ambiguity, and redundancy rate of the entities, and reduce complexity of a storage space and composition. The following common semantic similarity calculation method is adopted:
where pmi (A, B) is the similarity between entity A and entity B; p (A, B) is the correlation probability of the entity A and the entity B; p (A) is the probability of occurrence of entity A; p (B) is the probability of occurrence of entity B.
In probability theory, if a is not correlated with B, p (a, B) ═ p (a) p (B); if the correlation between the two is larger, p (A, B) is larger compared with p (A) p (B). The log is derived from the theory of information theory, and can be simply understood as that after taking the log of the function p (x), a probability is converted into an information quantity (which is multiplied by-1 to become a positive number), and when taking the base 2, the variable can be simply understood as how many bits can be used for representing the variable.
In this embodiment, in step S4, for the newly added fused teaching content, quality evaluation and semantic similarity detection are required to be performed, and then the qualified part is added to the knowledge base to ensure the quality of the knowledge base. Wherein, judging whether the newly added teaching content in the fusion information meets the quality standard comprises:
s41, establishing a quality evaluation model; the quality evaluation model is as follows:
wherein, L is quality grade; k is a radical ofiTo the knowledge obtained; k is a supervision corpus, and the supervision expected is set manually;madding the knowledge number of the teaching contents into the fusion information; i is a markerThe number is a positive integer;
s42, calculating according to the quality evaluation model to obtain quality grades, and carrying out normalization processing on the quality grades to obtain a quality grade sequence LnThe quality class sequence LnThe quality grade value is composed of a plurality of quality grade values; wherein n is the number of grades and takes the value of a positive integer;
s43, setting a quality grade threshold η (generally, η is set to 0.8), and judging a quality grade sequence LnQuality level L corresponding to newly added teaching contentjAnd if the value is larger than the quality level threshold η, the newly added teaching content does not accord with the quality standard, otherwise, the newly added teaching content accords with the quality standard, wherein j is a mark symbol and takes a positive integer from 0 to n.
Through the judgment processing, the newly added teaching contents meeting the quality standard are reserved, and the newly added teaching contents not meeting the quality standard are removed from the fusion information set. Thereby making the entire knowledge base more hierarchical.
In this embodiment, in step S5, after the teaching information is fused, analogizing iteration processing is performed according to steps S2-S4, so that more teaching fusion information can be obtained, the teaching fusion information is displayed to obtain a teaching knowledge graph, and the definition of each knowledge point in the knowledge graph, the division of the hierarchical structure, and the knowledge chain are also implemented correspondingly. The multi-source heterogeneous data is integrated through the knowledge graph, the simple and easy-to-use subject knowledge graph is constructed, a teacher is helped to master dynamic and associated subject information, teaching according to the material is facilitated, and the teaching and research efficiency is improved. Provides structured and integrated learning resources for students and enhances the learning interest of the students.
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 (4)
1. A knowledge graph construction method based on teaching contents is characterized by comprising the following steps: the method comprises the following steps:
s1, classifying the teaching contents according to teaching subjects to obtain teaching subject contents;
s2, extracting information of the teaching subject content to obtain extracted information; the information extraction includes: entity extraction, relationship extraction and event extraction;
s3, aligning entities in the extracted information by adopting a semantic similarity calculation method to obtain fusion information;
s4, judging whether the newly added teaching contents in the fusion information meet the quality standard, and if so, retaining the newly added teaching contents in the fusion information; if not, removing the newly added teaching contents from the fusion information;
and S5, constructing a knowledge graph based on the fusion information after judgment processing.
2. The pedagogical-content-based knowledge graph construction method of claim 1, characterized in that: in step S2, the entity extracts atomic information elements used for extracting teaching subject contents; the relation extraction is used for extracting semantic relations among a plurality of different entities in the teaching subject contents; and the event extraction is used for judging the category of the teaching subject content.
3. The pedagogical-content-based knowledge graph construction method of claim 1, characterized in that: in step S3, the calculation of semantic similarity between entities is determined according to the following formula:
where pmi (A, B) is the similarity between entity A and entity B; p (A, B) is the correlation probability of the entity A and the entity B; p (A) is the probability of occurrence of entity A; p (B) is the probability of occurrence of entity B.
4. The pedagogical-content-based knowledge graph construction method of claim 1, characterized in that: in step S4, the determining whether the newly added teaching content in the fusion information meets the quality standard includes:
s41, establishing a quality evaluation model; the quality evaluation model is as follows:
wherein, L is quality grade; k is a radical ofiTo the knowledge obtained; k is a supervision corpus; m is the knowledge number of newly added teaching contents in the fusion information; i is a mark symbol and takes the value as a positive integer;
s42, calculating according to the quality evaluation model to obtain quality grades, and carrying out normalization processing on the quality grades to obtain a quality grade sequence Ln(ii) a Wherein n is the number of grades and takes the value of a positive integer;
s43, setting a quality grade threshold value η, and judging a quality grade sequence LnQuality level L corresponding to newly added teaching contentjAnd if the value is larger than the quality level threshold η, the newly added teaching content does not accord with the quality standard, otherwise, the newly added teaching content accords with the quality standard, wherein j is a mark symbol and takes a positive integer from 0 to n.
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