CN110909175B - Search engine-based online course concept knowledge graph construction method - Google Patents
Search engine-based online course concept knowledge graph construction method Download PDFInfo
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Abstract
The invention provides a search engine-based online course concept knowledge graph construction method, and belongs to the technical field of information. The method uses an external database and a natural language processing method to acquire concepts in the course text, thereby acquiring the classification result of the course field, simultaneously acquiring the search result of the concepts by using a search engine, extracting the course expansion concepts therein, and completing the construction of the concept knowledge graph. The method does not need training data, can acquire accurate and comprehensive concepts in a special scene of course text, and effectively constructs the online course concept knowledge graph.
Description
Technical Field
The invention belongs to the technical field of information, and particularly provides a search engine-based online course concept knowledge graph construction method.
Background
A large-scale open network course (MOOC) is a brand-new education mode which is started in recent years, and the method is free from the limitation of time and place, so that more people can enjoy high-quality teaching contents at any time, and the phenomenon of unequal education resources is reduced. The famous online education platforms such as edX, Coursera, on-line class and the like at home and abroad have different college resource supports, and the platforms contain competitive curriculums of a plurality of teachers, so that the development of years becomes one of important ways for the public to learn. The course text (subtitles) is an important component of the online course, contains important teaching contents of the course, is different from general text materials, has higher concept distribution density and is more difficult to understand during listening and speaking/reading, and the constructed concept knowledge graph is helpful for learning the online course; on the other hand, the course text is important data introduced to the field to which the course belongs, and the constructed concept knowledge graph can reflect the knowledge distribution of the field. However, the curriculum texts are from the oral explanation of online curriculum lecturers, and although more formal than daily communication, the text quality is inferior to written characters, and the high density and the low frequency of the concept distribution density also increase the difficulty of constructing the concept knowledge graph.
The knowledge graph mainly comprises three components of entities, relations and attributes, and the construction of the concept knowledge graph is mainly used for obtaining the concept entities and determining the correlation relation among the concepts, so that the greatest difficulty lies in concept extraction, and the problems of the category of the entities, semantic merging, attribute extraction and the like are not considered. There are many relevant researches on the construction and application of a knowledge graph, for example, wuyue constructs a knowledge graph of a microblog community, extracts 5 entities of people, things, places, events and topics and relations among the entities, and semantically improves social network search, but many important concepts have extremely low frequency of appearance in course texts, and a method for identifying the concepts is different from the 5 entities; the Liukai uses a conditional random field method to extract entities in the clinical medical record of traditional Chinese medicine, but a machine learning method based on statistics cannot identify concept words with extremely low occurrence frequency; the neural network can also solve the problem of entity extraction, Lample G et al propose a method for named entity recognition by using the training result of LSTM on the artificially labeled corpus without the prior knowledge of artificially designed features and language, but no artificially labeled data is available in the scene.
The method can effectively solve the problems of knowledge graph construction, entity extraction and the like, but the problems can not be applied to the special scene of concept extraction of the course text, and the occurrence frequency of a large number of important concepts is low and the number of on-line courses with subtitles is small due to the spoken characteristics and high-density concept distribution of the course text. Therefore, the traditional three methods based on rules, statistics or deep learning are difficult to solve the problem of constructing the concept knowledge graph of the course text. The rule-based method introduces too many non-concept words, the statistical-based method omits low-frequency concepts, and deep learning depends on a large amount of artificially labeled training data.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a search engine-based online course concept knowledge graph construction method. The method does not need training data, can acquire accurate and comprehensive concepts in a special scene of course text, and effectively constructs the online course concept knowledge graph.
The invention provides a search engine-based online course concept knowledge graph construction method, which is characterized by comprising the following steps of:
1) constructing candidate course fields, wherein each candidate course field is composed of a pair of a primary field and a secondary field; constructing a corresponding seed word set for each candidate course field;
2) selecting an online course and extracting a course concept;
2-1) selecting an online course, and performing text word segmentation and part-of-speech tagging on a course text:
2-2) screening candidate concept words by using the result of the step 2-1):
2-3) obtaining course concept words from the candidate concept words in the step 2-2) based on iterative similarity calculation of word vectors to form a course concept set:
3) classifying the course concept;
setting the course concept set obtained in the step 2) as { c1,…,cnThe first level of the domain is { l11,…,l1pThe second level of the field is { l21,…,l2q}，p<<q; calculate l1iAnd each cjThe mean value of the word2vec word vector similarity of the word2vec word vectors, the maximum mean value of the similarity is l1iIs the first-level domain to which the course belongs; calculate l2iAnd each cjThe mean value of the word2vec word vector similarity of the word2vec word vectors, the maximum mean value of the similarity is l2iIs the second level domain to which the course belongs;
4) obtaining course concept search results
Acquiring search results of course concepts in a search engine, and integrating character segments in each search result to obtain texts of the search results;
5) extracting an extended concept;
using the course concept obtained in the step 2) as a seed word, using the text obtained in the step 4) as the input of the method in the step 2), and obtaining an extended concept set { ec) related to the course concept by using the method in the step 2)1,…,ecm}; wherein ec isiThe ith expansion concept related to the course concept, and m is the total number of the expansion concepts;
6) integrating all course concepts and the expansion concepts thereof to obtain a concept knowledge graph of the online course;
for each course concept ciCalculating the similarity of the cosine by using the word2vec word vector to obtain an extended concept set { ec) with the similarity of the course concept being more than A in the extended concept seti,1,…,eci,kEach concept pair<ci,eci,j>Both are a related pair of concepts, corresponding to one edge in the concept knowledge graph;
and establishing corresponding edges in the knowledge graph by using the course concepts and the extension concepts of the online courses as nodes in the knowledge graph, wherein the similarity between the course concepts and the extension concepts is more than A, so as to obtain the concept knowledge graph of the course.
The invention has the characteristics and beneficial effects that:
the invention can effectively complete the task of extracting the course concept by means of an external database (encyclopedia entry), rule filtering combining noun part of speech and an iteration method similar to PageRank aiming at the application requirements of the concept knowledge graph construction of the course text, and can automatically complete the classification task of the course by utilizing the course concept on the basis; on the other hand, only depending on the course concepts is not enough to construct a relatively complete concept knowledge graph, the method and the system jump out of the range limitation of the course text, successfully acquire the extension concepts of the course concepts by using the search results of the course concepts in a search engine, and complete the construction of the concept knowledge graph of the online course on the basis of the extension concepts.
Drawings
FIG. 1 is an overall flow diagram of the method of the present invention.
Fig. 2 is a flow chart of iterative similarity calculation based on word vectors in the present invention.
FIG. 3 is a diagram of exemplary text segments of hundred degree and google search results according to an embodiment of the present invention.
Detailed Description
The invention provides a search engine-based online course concept knowledge graph construction method, which is further described in detail below by combining the accompanying drawings and embodiments.
The method can be applied to Chinese texts in the given classification field: such as course captions for a class's online website. The courses of the on-line website of the classroom are divided into the fields of computers, economic management and the like, each course is composed of a plurality of videos, most of the videos provide subtitle information, and a text formed by summarizing the subtitles is called a subtitle text of the course. The embodiment takes the course subtitle text on the online of the classroom as input to describe how to use a natural language processing method to extract course concepts, how to classify courses through the course concepts, how to obtain the extension concepts of each course concept by using the search results of a search engine, and finally construct the knowledge graph of the concepts.
The invention provides a search engine-based online course concept knowledge graph construction method, the overall process is shown in figure 1, and the method comprises the following steps:
1) and constructing candidate course fields and a seed word set of each candidate course field.
The design of the candidate course field can refer to the secondary subject and supplement, each candidate course field is composed of a pair of a primary field and a secondary field, the primary field of the course is determined firstly, and the possible online course field is required to be included. For example, the primary fields of the class of the on-line platform of the classroom mainly include computer, administration, society, biology, physics and the like, then the secondary fields included in each primary field are respectively designed, and the possible fields of the on-line class are covered as much as possible, for example, the secondary fields of the computer include algorithms, operating systems, artificial intelligence, program design and the like;
constructing a high-quality seed word set suitable for the online course for each candidate course field; the seed words refer to common important concept words, in practice, seed word sets are respectively constructed according to the first-level field, and then the seed words are combined together, so that labor is saved and good effect can be achieved. The selected seed words are all representative concepts for each field of the course, and in the second step of extracting the course concepts, the seed words appearing in the candidate concept words determine that those semantically similar concepts will be the course concepts.
Taking the "data structure (autonomous mode)" course on the on-line platform of the classroom as an example, the course text T is composed of the subtitles of all the course videos, and the data structure is classified into the data structure when the classification is correct<Computer, algorithm>Wherein the computer is a first-level field, and the algorithm is a second-level field; in this embodiment, a seed word set S ═ S is established for the field of the candidate lessons1,…,sp}; generally, the classification to the computer basically meets the automatic classification requirement of the online course, and partial courses can also belong to a plurality of fields; the seed words for a course, for which there are data structures, algorithms, binary trees, arrays, recursions, etc., are taken from the set S of seed words described above. The invention obtains the course concept set C ═ { C ═ by calculation1,…,cnThe extended concept set EC ═ EC1,…,ecmFor each course concept ciHaving the set RC ═ eci,1,…,eci,kRepresents the course concept as the set of top k related expanded concepts.
For word2vec word vector calculation of words/phrases, a training data set (obtained from the internet) of Baidu encyclopedia full entry text is used in the experiment, and the phrase w1…wlWord2vec word vectors of all wiThe sum of the word vectors is obtained by unitization calculation; v. the1And v2Are two word vectors with a similarity of 0.5+0.5 × cos (v1,v2) I.e. cosine similarity maps to the interval [0,1 ]]The result of (1).
In order to obtain the course concept of an online course, the invention designs a course concept extraction algorithm by means of rule filtering and iteration of an external database, then uses an automatic course classification method based on the course concept to obtain the field of the course, and in addition, the invention uses a concept expansion and concept knowledge graph construction method based on a search engine to obtain the expansion concept and finally constructs a concept knowledge graph.
2) Selecting an online course and extracting a course concept;
the course concept extraction mainly comprises three parts of text word segmentation and part-of-speech tagging, proper candidate concept word screening and iterative similarity calculation based on word vectors. The course concept is defined by nouns and noun phrases with corresponding encyclopedia entries, the encyclopedia entries require that the words/phrases have public recognized unique meanings (different from entities of a general knowledge map), and noun parts of speech are necessary requirements of the concept. A "data structure," in the context of an encyclopedia entry of a data structure, refers to a collection of data elements that have one or more relationships with each other and the composition of the relationships between the data elements in the collection. "is an example:
2-1) selecting an online course, and performing text segmentation and part-of-speech tagging on a corresponding course text:
the two-step preprocessing of word segmentation and part-of-speech tagging is needed to be carried out on the input text, the project can use the jieba library of python to carry out text word segmentation and part-of-speech tagging on the input course text, and all continuous 1-3 words are possible concept words by taking sentences as boundaries. The results of the word segmentation and part-of-speech tagging for the example are:
data structure n/is v/means n/l/exists v/each other/is a/m/a/c/uj of a plurality of m/relations n/data n/element n/uj/set v/c/uj/relation n/f/data n/element n/among r/set v/form v/r. x is the number of
2-2) screening suitable candidate concept words;
the invention uses a rule-based method to screen nouns and noun phrases, can keep low-frequency concepts in the text, and then further filters the low-frequency concepts by means of an external database (encyclopedia entry) to obtain suitable candidate concept words. For the filtering of nouns and noun phrases, regular expression matching can be used, if we connect the parts of speech of a phrase with @ n (e.g., "data n/element n" with @ n @ n), a feasible regular expression is as follows:
^(@(([av]？n[rstz]？)|l|a|v))*(@(([av]？n[rstz]？)|l))$
when the matching of the part of speech of the phrase is successful, the part of speech of the phrase is considered to be a noun part of speech. And further filtering the terms of the encyclopedia, and reserving the terms with the corresponding encyclopedia terms to obtain the candidate concept words.
After screening, 5 candidate concept words of 'data structure, data, element, data element and relation' are obtained.
2-3) iterative similarity calculation based on word vectors;
FIG. 2 is a flow chart of this step, and step 2-2) results in n candidate concept words { d }1,…,dnCalculate their word2vec word vector as { v }1,…,vnThe seed word set is S, and the initialized weight is { f }1,…,fnWhen d is reachediWhile in S, fi1, otherwise 0. Each iteration is equivalent to making a probability transition if diAnd djWithout common words, the weight is given by diIs transferred to djHas a coefficient of viAnd vjOtherwise, is 0; after each iteration, the weight { f is needed to be calculated1,…,fnDivide by max (f)1,…,fn) And (6) carrying out normalization processing.
The experimental result shows that 3 iterations are generally needed, and each concept word diOnly the d with the most similar vectors of the first 500 words is considered when transferring the weightjTo reduce the computation time. After iteration is completed, a candidate concept with the f value larger than theta is taken as a course concept, and for the course text, the fact that theta is 0.6 is reasonable, and most of concepts related to courses can be included.
For the exemplary 5 candidate concept words, the seed word should at least contain "data structure", assuming that we have 3 iterations with f values of: data structure (1.0), data (0.9), data element (0.7), element (0.5), relationship (0.3), then the final course concept word is data structure, data element.
3) Classifying the course concept;
setting the course concept set obtained in the step 2) as { c1,…,cnThe first level of the domain is { l11,…,l1pThe second level of the field is { l21,…,l2q}，p<<q; calculate l1iAnd each cjThe mean value of the word2vec word vector similarity of the word2vec word vectors, the maximum mean value of the similarity is l1iThe user selects the first-level field and the second-level field to which the curriculum belongs, and calculates l2iAnd each cjThe mean value of the word2vec word vector similarity of the word2vec word vectors, the maximum mean value of the similarity is l2iIs the second level domain to which the user selected the course belongs.
The experimental results show that about 85% of the lessons automatically select the correct lesson field, approaching 100% correct if the first 3 fields of automatic selection are considered. For example, for the aforementioned data structure (autonomous mode) lesson, mathematics, computers are the first 2 primary domains selected automatically, and function theory, algorithms are the first 2 secondary domains selected automatically.
4) Obtaining course concept search results
The search results of the course concepts in the search engine can be obtained by using a simple web crawler, and the text T' of the search results can be obtained by integrating the text segments in each search result, wherein the text segments refer to the text contents framed in the lower graph. The more search results are obtained within a reasonable range, the more comprehensive the expansion concept is obtained, and the more suitable the first 3 pages are taken. The text fragment of the hundred degree and google search results for the "data structure" in the embodiment of the present invention is shown in fig. 3.
5) Extracting an extended concept;
using the course concept obtained in the step 2) as a seed word, using the text obtained in the step 4) as the input of the concept extraction algorithm in the step 2), and repeating the concept extraction algorithm once to obtain an extended concept { ec } related to the course concept1,…,ecm}. For example, the course concept is a data structure, the search engine is used to obtain the search result of the above graph, and the concept extraction algorithm extracts concept words such as data structure, data, elements, data elements, relationships, storage, and collections from the course concept.
6) Integrating all course concepts and the expansion concepts thereof to obtain a concept knowledge graph of the online course;
the course text of one online course can obtain a course concept set { c) after the steps 2) to 5) are carried out1,…,cn} and extended concept set ec1,…,ecmFor each course concept ciThe similarity of the cosine is solved by using word2vec word vectors, and an extended concept set { ec) with the similarity of A (0-A-1) can be found out from the extended concept seti,1,…,eci,kEach concept pair<ci,eci,j>Are both related pairs of concepts, corresponding to a "related" edge in the concept-knowledge graph. The higher the C value is, the stricter the requirement of the obtained concept on the correlation degree is, the fewer the number of the relevant edges of the knowledge graph is, and 0.7 is an engineering reasonable A value.
The course concept and the extension concept of an online course are used as nodes in the knowledge graph, and the concept pair with the similarity of the course concept and the extension concept above A establishes a 'related' edge in the knowledge graph, so that the obtained concept knowledge graph of the course is beneficial to students to understand the course; if the course classification condition obtained in the step 2 is utilized to integrate all courses in a specific primary field/secondary field to establish a large concept knowledge graph, the development condition of one field can be known.
The subject matter of the invention has been presented in detail by way of the foregoing examples, and it should be appreciated that the above description should not be construed as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (1)
1. A search engine-based online course concept knowledge graph construction method is characterized by comprising the following steps:
1) constructing candidate course fields, wherein each candidate course field is composed of a pair of a primary field and a secondary field; constructing a corresponding seed word set for each candidate course field;
2) selecting an online course and extracting a course concept;
2-1) selecting an online course, and performing text word segmentation and part-of-speech tagging on a course text;
2-2) screening candidate concept words by using the result of the step 2-1);
2-3) obtaining course concept words from the candidate concept words in the step 2-2) based on iterative similarity calculation of word vectors to form a course concept set;
3) classifying the course concept;
setting the course concept set obtained in the step 2) as { c1,…,cnThe first level of the domain is { l11,…,l1pThe second level of the field is { l21,…,l2q}，p<<q; calculate l1iAnd each cjThe mean value of the word2vec word vector similarity of the word2vec word vectors, the maximum mean value of the similarity is l1iIs the first-level domain to which the course belongs; calculate l2iAnd each cjThe mean value of the word2vec word vector similarity of the word2vec word vectors, the maximum mean value of the similarity is l2iIs the second level domain to which the course belongs;
4) acquiring a course concept search result;
acquiring search results of course concepts in a search engine, and integrating character segments in each search result to obtain texts of the search results;
5) extracting an extended concept;
using the course concept obtained in the step 2) as a seed word, using the text obtained in the step 4) as the input of the method in the step 2), and obtaining an extended concept set { ec) related to the course concept by using the method in the step 2)1,…,ecm}; wherein ec isiThe ith expansion concept related to the course concept, and m is the total number of the expansion concepts;
6) integrating all course concepts and the expansion concepts thereof to obtain a concept knowledge graph of the online course;
for each course concept ciCalculating the similarity of the cosine by using the word2vec word vector to obtain an extended concept set { ec) with the similarity of the course concept being more than A in the extended concept seti,1,…,eci,kEach concept pair<ci,eci,j>Are all related toA concept corresponding to an edge in the concept knowledge graph;
and establishing corresponding edges in the knowledge graph by using the course concepts and the extension concepts of the online courses as nodes in the knowledge graph, wherein the similarity between the course concepts and the extension concepts is more than A, so as to obtain the concept knowledge graph of the course.
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