A career guidance method and system based on knowledge graph

Abstract

The present invention provides a career guidance method and system based on knowledge graph, which relates to the field of data analysis technology. The method includes obtaining resume information uploaded by a user; extracting personal information in the resume information and keywords in work experience, and querying the career knowledge graph according to the keywords to obtain a plurality of matching selected occupations; judging whether the desired occupation or hobbies uploaded by the user are obtained; when the desired occupation or hobbies uploaded by the user are not obtained, pushing a career trend analysis test to the user; obtaining the user's selected desired occupation according to the user's feedback on the career trend test; obtaining a recommended occupation sequence according to the selected occupation and the selected expected occupation, and pushing the recommended occupation sequence to the user end. The recommended occupations are rich in variety and wide in scope, avoiding the problem of improper occupation selection caused by the user's limited occupation types, increasing the probability of the user selecting a suitable occupation, and reducing the probability of the user's lagging career development.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a career guidance method and system based on knowledge graph. Background Art

[0002] The employment guidance system is an information system that provides job seekers with a series of services such as job information, career search, and information viewing. Knowledge graph is a representation of semi-structured data that can provide more comprehensive and intelligent information retrieval and recommendation by connecting the relationships between different concepts and entities.

[0003] Most college students who just started to work don't know how to choose a suitable career. They usually refer to the employment choices of students in the same major in the past, or follow the advice of their elders to choose a career, and then search for relevant positions on employment websites. This career selection method obtains more messy job information and covers a limited number of careers. Sometimes college job seekers cannot choose a suitable career based on the above methods. They will change jobs frequently in the early stage of employment until they try a career that they are good at or that they may like. In the early stage of employment, they waste more time on exploring career choices, and their career development may lag behind other job seekers of the same period. Summary of the invention

[0004] The problem to be solved by the present invention is that the existing career guidance selection method covers a limited number of career types, which may lead to the selection of inappropriate careers, resulting in delayed career development.

[0005] To solve the above problems, on the one hand, the present invention provides a career guidance method based on knowledge graph, comprising:

[0006] Get the resume information uploaded by the user;

[0007] Extracting keywords from personal information and work experience in the resume information, and searching the occupational knowledge graph based on the keywords to obtain a plurality of matching occupations to be selected;

[0008] Determine whether the desired career or hobbies uploaded by the user are obtained;

[0009] When the desired occupation or hobbies uploaded by the user are not obtained, a career trend analysis test is pushed to the user;

[0010] Obtaining the user's desired occupation to be selected according to the user's feedback on the occupation tendency test;

[0011] A recommended career sequence is obtained based on the to-be-selected career and the to-be-selected desired career, and the recommended career sequence is pushed to the user end.

[0012] Optionally, the resume information includes personal age, graduation school level, major, personal education, expected salary and work experience keywords; the occupational knowledge graph includes multiple nodes, multiple nodes include occupation nodes and attribute nodes, the occupation nodes are connected to the attribute nodes through relationship lines, and the attribute nodes include age, school, major, education, salary and job content keywords;

[0013] The extracting of the personal information in the resume information and the keywords in the work experience, and searching the occupational knowledge graph according to the keywords, to obtain a plurality of matching candidate occupations include:

[0014] According to the major, the personal education or the work experience keywords in the resume information, the occupational knowledge graph is searched, and the major, the education or the job content keywords in the occupational knowledge graph are located to obtain a plurality of initial attribute nodes;

[0015] Taking the initial attribute node as a starting point, along the relationship line connected to the initial attribute node, searching for the attribute node of the age, the school or the salary on the relationship chain where the initial attribute node and the career node are located, and counting the number of the attribute nodes on each relationship chain that match the individual age, the graduation school level or the expected salary in the resume information;

[0016] When the number is greater than the number threshold, the occupation node at one end of the relationship chain corresponding to the number is selected, and the occupation of the occupation node is marked as the to-be-selected occupation.

[0017] Optionally, when the number is greater than a number threshold, the career node at one end of the relationship chain corresponding to the number is selected, and the occupation of the career node is marked as the to-be-selected occupation. Then, the career guidance method based on the knowledge graph further includes:

[0018] According to the number, the corresponding occupations to be selected are sorted from large to small to obtain a sequence of occupations to be selected.

[0019] Optionally, obtaining the user's desired occupation to be selected according to the user's feedback on the occupation tendency test includes:

[0020] Obtaining the user's interests, hobbies and personality traits according to the user's feedback on the career tendency test;

[0021] According to the interests and personality traits, the personnel characteristics required for the occupation and the job content keywords are queried to obtain the desired occupation to be selected.

[0022] Optionally, after querying the personnel characteristics required for the occupation and the job content keywords according to the interests and personality characteristics to obtain the desired occupation to be selected, the career guidance method based on the knowledge graph further includes:

[0023] Retrieving the job content of the desired occupation to be selected, and extracting keywords from the job content;

[0024] Compare the work experience keywords in the resume information with the keywords in the work content to obtain different keywords in the work content;

[0025] According to the difference keywords, intercepting the sentences including the difference keywords in the work content;

[0026] The difference keywords and the sentences are pushed to the user terminal to remind the user to make up for the ability deficiencies.

[0027] Optionally, after comparing the work experience keywords in the resume information with the keywords in the work content to obtain the difference keywords in the work content, the knowledge graph-based career guidance method further includes:

[0028] According to the difference keywords and the corresponding desired occupations to be selected, screening the training information corresponding to the desired occupations to be selected;

[0029] Counting the number of types of the difference keywords contained in different training information;

[0030] Arranging the different training information from large to small according to the number of types of the difference keywords to form a training information sequence;

[0031] The training information sequence is sent to the user terminal.

[0032] Optionally, after calling the work content of the desired occupation to be selected and extracting keywords from the work content, the career guidance method based on the knowledge graph further includes:

[0033] Compare the work experience keywords in the resume information with the keywords in the work content to obtain overlapping keywords in the work content;

[0034] According to the number of types of the overlapping keywords corresponding to each of the desired occupations to be selected, the multiple desired occupations to be selected are sorted to obtain a sequence of the desired occupations to be selected.

[0035] Optionally, obtaining a recommended occupation sequence according to the occupation to be selected and the desired occupation to be selected, and pushing the recommended occupation sequence to the user terminal comprises:

[0036] Starting from the start ends of the to-be-selected occupation sequence and the to-be-selected expected occupation sequence respectively, a preset number of occupations are selected to obtain a first recommended occupation sequence;

[0037] Comparing the sequence of occupations to be selected with the sequence of expected occupations to be selected, extracting the occupations common to the two sequences, and obtaining a second recommended occupation sequence;

[0038] The first recommended occupation sequence and the second recommended occupation sequence are combined, and the same occupations in the combined sequence are retained to obtain the recommended occupation sequence.

[0039] Optionally, after obtaining a recommended career sequence according to the to-be-selected career and the to-be-selected desired career, and pushing the recommended career sequence to the user terminal, the career guidance method based on the knowledge graph further includes:

[0040] According to the recommended occupation viewed by the user fed back by the user terminal, calling the work content of the recommended occupation viewed by the user, and extracting keywords in the work content;

[0041] Counting the keywords corresponding to the recommended occupations viewed multiple times by the user, and obtaining the number of occurrences of each keyword;

[0042] According to the number of occurrences of the keywords, the keyword sequence is obtained from large to small;

[0043] A preset number of keywords are selected from the starting end of the keyword sequence, and the order of the occupations in the recommended occupation sequence is adjusted according to the number of times the selected multiple keywords appear in each occupation in the recommended occupation sequence to obtain a preferred sequence of recommended occupations that the user is interested in.

[0044] On the other hand, the present invention also provides a career guidance system based on knowledge graph, comprising:

[0045] Resume acquisition module, used to obtain resume information uploaded by users;

[0046] A graph query module is used to extract the personal information in the resume information and the keywords in the work experience, and query the occupational knowledge graph according to the keywords to obtain a plurality of matching candidate occupations;

[0047] A test push module is used to determine whether the desired occupation or hobbies uploaded by the user are obtained; when the desired occupation or hobbies uploaded by the user are not obtained, a career trend analysis test is pushed to the user;

[0048] A test analysis module, used to obtain the user's desired occupation to be selected based on the user's feedback on the occupation tendency test;

[0049] The occupation analysis module is used to obtain a recommended occupation sequence based on the occupations to be selected and the desired occupations to be selected, and push the recommended occupation sequence to the user end.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a career guidance method and system based on knowledge graph, which extracts personal information and keywords in work experience in resume information uploaded by users, and queries the career knowledge graph according to the keywords to obtain multiple matching candidate occupations, and obtains some recommended occupations on one hand; on the other hand, it determines whether the expected occupations or hobbies uploaded by users are obtained; when the expected occupations or hobbies uploaded by users are not obtained, a career trend analysis test is pushed to users, and the users are helped to understand themselves when they do not understand themselves clearly; based on the user's feedback on the career trend test, the user's candidate expected occupations are obtained, and on the other hand, some recommended occupations are obtained; finally, a recommended occupation sequence is obtained based on the candidate occupations and the expected occupations, and the recommended occupation sequence is pushed to the user end. From the occupations obtained by querying and screening in the two directions, occupations worthy of recommendation are obtained in a comprehensive manner. The recommended occupations are rich in variety and cover a wide range. Not only the user's major and previous work experience, but also the user's interests and personal expectations are taken into consideration. Career recommendations can be made from multiple angles, broaden the user's employment horizons, avoid the problem of inappropriate career choices due to the small number of occupations touched upon, increase the probability of users choosing suitable occupations, and reduce the probability of users lagging behind in their career development. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of a process of a career guidance method based on a knowledge graph in an embodiment of the present invention is shown;

[0053] Figure 2 A schematic diagram of the structure of a professional knowledge graph in an embodiment of the present invention is shown;

[0054] Figure 3 A structural schematic diagram of a career guidance system based on a knowledge graph in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0056] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein.

[0057] In the description of this specification, the description with reference to the terms "embodiment", "one embodiment" and "one implementation" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or implementation are included in at least one embodiment or implementation of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or implementation. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or implementations in a suitable manner.

[0058] like Figure 1 , an embodiment of the present invention provides a career guidance method based on knowledge graph, including:

[0059] S1: Get the resume information uploaded by the user.

[0060] Specifically, resume information includes personal age, education background, work experience, contact information, expected salary and expected job (expected career), and may even include personal photos and political affiliation, etc.

[0061] S2: Extracting the personal information in the resume information and the keywords in the work experience, and querying the occupational knowledge graph according to the keywords to obtain a plurality of matching candidate occupations.

[0062] Specifically, the knowledge graph can integrate various occupational information, including the required skills, salary levels, development prospects, industries, employment requirements, etc. of different occupations. By analyzing the user's needs and combining the user's background, interests, experience or career preferences, suitable career directions and development paths are recommended. For example, based on the cooking technology and nutrition in the personal information and the Western food or coffee or pastry in the work experience, relevant positions can be found in the knowledge graph, such as barista, private chef or dessert chef. In this way, occupations that fit the user's major or work experience can be found and recommended to the user, which will be more acceptable to the user. If the user has no work experience, the career query and recommendation are mainly based on keywords such as cooking or nutrition in the user's major.

[0063] S3: Determine whether the desired occupation or hobby uploaded by the user is obtained.

[0064] Specifically, in addition to the user's major and work experience, the user's career can be further recommended based on the user's personal expectations or interests. Since the university major may not be the major chosen by the college students themselves, it is also possible that the college students are affected by many factors when choosing their majors and did not choose the major they actually want to go to. Therefore, when seeking employment, some college students do not want to work in a career related to their major, but gradually develop personal interests and hobbies in college or in subsequent life, find the direction they really like and are interested in, and want to use this as a career. According to the user's expected career or interests, search in the knowledge graph, or directly search in the system according to the keywords of the expected career or interests, and obtain the user's desired career to be selected.

[0065] S4: When the desired occupation or hobby uploaded by the user is not obtained, a career trend analysis test is pushed to the user. When the desired occupation or hobby uploaded by the user is obtained, the user's desired occupation to be selected is obtained according to the user's desired occupation or hobby.

[0066] S5: Obtaining the user's desired occupation to be selected based on the user's feedback on the occupational tendency test.

[0067] Specifically, some college students may know that they do not want to work in fields related to their majors, and have not developed any hobbies or interests, but they do not know what they want to do. In this case, we can first conduct a career trend analysis test on the user, provide various interest tests to the user, and evaluate the user's interest direction through the answers to the questions. These tests can help users better understand their areas of interest, and thus provide corresponding career recommendations. For example, the MBTI personality test and the Holland interest test can help users understand their personal interest areas, thereby understanding the user's personality characteristics, possible interests, etc., and then make career recommendations based on the analyzed interests or personality characteristics.

[0068] S6: Obtain a recommended career sequence based on the to-be-selected careers and the to-be-selected desired careers, and push the recommended career sequence to the user end.

[0069] Specifically, based on the occupations obtained from the queries and screening in the two directions, we comprehensively obtain occupations that are worthy of recommendation. The recommended occupations are rich in variety and cover a wide range.

[0070] In this embodiment, by extracting and obtaining the personal information and keywords in the work experience in the resume information uploaded by the user, and querying the occupational knowledge map according to the keywords, a plurality of matching occupations to be selected are obtained, and some occupations worthy of recommendation are obtained on one hand; on the other hand, it is judged whether the desired occupation or hobbies uploaded by the user are obtained; when the desired occupation or hobbies uploaded by the user are not obtained, the occupation trend analysis test is pushed to the user, and the user is helped to understand himself in the case that the user does not understand himself so clearly; according to the user's feedback on the occupation trend test, the user's desired occupation to be selected is obtained, and some occupations worthy of recommendation are obtained on the other hand; finally, according to the occupation to be selected and the desired occupation to be selected, a recommended occupation sequence is obtained, and the recommended occupation sequence is pushed to the user end. From the occupations obtained by querying and screening from the two directions, occupations worthy of recommendation are obtained comprehensively, and the recommended occupations obtained are rich in variety and cover a wide range. Not only the user's major and previous work experience are considered, but also the user's hobbies and personal expectations are taken into account. It can make occupation recommendations from multiple angles, broaden the user's employment vision, avoid the problem of improper occupation selection caused by the user's less occupation types, increase the probability of the user choosing a suitable occupation, and reduce the probability of the user's career development lagging behind.

[0071] Optionally, the resume information includes personal age, graduation school level, major, personal education, expected salary and work experience keywords; the occupational knowledge graph includes multiple nodes, multiple nodes include occupation nodes and attribute nodes, the occupation nodes are connected to the attribute nodes through relationship lines, and the attribute nodes include age, school, major, education, salary and job content keywords;

[0072] The extracting of the personal information in the resume information and the keywords in the work experience, and searching the occupational knowledge graph according to the keywords, to obtain a plurality of matching candidate occupations include:

[0073] The professional knowledge graph is queried according to the keywords of the major, the personal education or the work experience in the resume information, the keywords of the major, the education or the position content in the professional knowledge graph are located, and a plurality of initial attribute nodes are obtained.

[0074] Specifically, for example, if a student majors in mechanical engineering, has a bachelor's degree, and has no work experience, the query positioning can be based on any of the above two points. Figure 2 The attribute node of the mechanized engineering major and the attribute node of the undergraduate program are obtained to obtain two initial attribute nodes.

[0075] Taking the initial attribute node as the starting point, along the relationship line connected to the initial attribute node, search for the attribute node of the age, the school or the salary on the relationship chain where the initial attribute node and the career node are located, and count the number of the attribute nodes on each relationship chain that match the personal age, the graduation school level or the expected salary in the resume information.

[0076] Specifically, the attribute node of mechanized engineering major is used as an initial attribute node, and then the relationship chain between the attribute node of mechanized engineering major and two occupations (assembly engineer and mechanical designer) is combined to find the node matching the user's age, the college or the salary on the relationship chain. Figure 2 In the figure, the age is displayed. In practical applications, an age node can be added. The student's college is a general university, and the expected salary is a, which is within the salary range indicated by Salary 1. Thus, two attribute nodes are found in the knowledge graph, namely, General University and Salary 1. Therefore, in the relationship chain formed between the attribute node of Mechanized Engineering and Assembly Engineer, there is an attribute node that matches the user (i.e., General University); in the relationship chain formed between the attribute node of Mechanized Engineering and Mechanical Designer, there are two attribute nodes that match the user (i.e., General University and Salary 1).

[0077] When the number is greater than the number threshold, the occupation node at one end of the relationship chain corresponding to the number is selected, and the occupation of the occupation node is marked as the to-be-selected occupation.

[0078] Specifically, Figure 2 When it is necessary to recommend as many occupations as possible to users, the quantity threshold can be set as small as possible, and can be set to 0. In this case, the available occupations include assembly engineers and mechanical designers. When it is necessary to consider both the number of recommendations and the degree of fit, the quantity threshold can be appropriately increased. For example, it can be set to 1. In this case, the only available occupation is mechanical designers. In actual applications, the quantity threshold can be set to a larger value such as 2 or 3 according to actual needs.

[0079] Optionally, when the number is greater than a number threshold, the career node at one end of the relationship chain corresponding to the number is selected, and the occupation of the career node is marked as the to-be-selected occupation. Then, the career guidance method based on the knowledge graph further includes:

[0080] According to the number of matching attribute nodes, the corresponding occupations to be selected are sorted from large to small to obtain an occupation sequence to be selected. Using the above example, the number of matching attribute nodes corresponding to the mechanical designer is 2, and the number of matching attribute nodes corresponding to the assembly engineer is 1. The occupation sequence to be selected at this time is {mechanical designer, assembly engineer}. Similarly, when more positions are queried, the occupation sequence to be selected that may be obtained according to this method is {occupation 1, occupation 2, occupation 3, occupation 4, occupation 5}.

[0081] Optionally, obtaining the user's desired occupation to be selected according to the user's feedback on the occupation tendency test includes:

[0082] The user's interests, hobbies and personality traits are obtained based on the user's feedback on the career tendency test.

[0083] According to the interests and personality traits, the personnel characteristics required for the occupation and the job content keywords are queried to obtain the desired occupation to be selected.

[0084] Specifically, for example, MBTI personality test, Holland interest test and other tests are used as career tendency tests to evaluate the user's interests through answers to questions. These tests can help users better understand their areas of interest and personality traits, and thus provide corresponding career recommendations. For example, accounting requires the personality traits of the applicant to be careful, conscientious, well-planned and organized. Through the test, it is found that the user's interests and hobbies are inclined to rational analysis work, and the personality traits are stable, well-organized and well-planned. Therefore, the user can be recommended the job of accounting assistant first.

[0085] Optionally, after querying the personnel characteristics required for the occupation and the job content keywords according to the interests and personality characteristics to obtain the desired occupation to be selected, the career guidance method based on the knowledge graph further includes:

[0086] The job content of the desired occupation to be selected is called up, and keywords in the job content are extracted.

[0087] Specifically, accounting work includes data analysis, data collation, bookkeeping, accounting, reporting, daily maintenance of account forms, etc. The key words can be data analysis, data collation, bookkeeping, accounting, reporting, and table maintenance.

[0088] The work experience keywords in the resume information are compared with the keywords in the work content to obtain the difference keywords in the work content.

[0089] Specifically, for example, the work experience keywords only include bookkeeping and table maintenance, but there are no keywords related to data analysis, data collation, accounting, and accounting reporting. Therefore, the difference keywords are data analysis, data collation, accounting, and accounting reporting.

[0090] According to the difference keywords, sentences including the difference keywords in the work content are intercepted.

[0091] The difference keywords and the sentences are pushed to the user terminal to remind the user to make up for the ability deficiencies.

[0092] Specifically, it compares work experience with the desired career to be selected, automatically analyzes the gap between personal work experience and the desired career to be selected, so as to find the gap between the user's personal ability and the expected job, and sends these missing abilities to the user to remind the user to make up for his or her own ability deficiencies so that he or she can be competent for the desired career to be selected in the future. It points out the direction for the user's career development and personal daily learning, which is conducive to the user to find gaps and fill them, grow quickly, and improve the user's success rate in interviews.

[0093] Optionally, after comparing the work experience keywords in the resume information with the keywords in the work content to obtain the difference keywords in the work content, the knowledge graph-based career guidance method further includes:

[0094] According to the difference keywords and the corresponding desired occupations to be selected, the training information corresponding to the desired occupations to be selected is filtered.

[0095] The number of types of the difference keywords contained in the different training information is counted.

[0096] The different training information are arranged from large to small according to the number of types of the difference keywords to form a training information sequence.

[0097] The training information sequence is sent to the user terminal.

[0098] Specifically, after knowing the user's missing ability content, some relevant training information can be searched accordingly based on the difference keywords and the user's desired career, such as training videos, learning documents, reference tutorials, and recommended books. However, the correlation between these searched training information and the missing content varies. According to the number of keyword types contained in the training information, the correlation between the training information and the missing ability can be reflected indirectly. For example, the training information includes accounting and reporting, but no data analysis; while another training information contains not only the keywords of accounting and reporting, but also the keywords of data analysis and data collation. At this time, the latter training information is positioned higher in the training information sequence than the former training information and can be seen first.

[0099] Optionally, after comparing the work experience keywords in the resume information with the keywords in the work content to obtain the difference keywords in the work content, the knowledge graph-based career guidance method further includes:

[0100] According to the difference keywords and the corresponding desired occupations to be selected, screening the part-time positions corresponding to the desired occupations to be selected;

[0101] Count the number of different keywords included in the work content of each part-time position;

[0102] Part-time positions are selected for recommendation based on the number of types, wherein the number of types corresponding to the selected part-time positions is greater than a preset number.

[0103] In this embodiment, the user's ability deficiencies can be supplemented by recommending part-time jobs, while also increasing college students' social practice, verifying and finding their own interests through practice, on the one hand increasing work ability, and on the other hand conducting career exploration from another perspective.

[0104] Optionally, after calling the work content of the desired occupation to be selected and extracting keywords from the work content, the career guidance method based on the knowledge graph further includes:

[0105] The work experience keywords in the resume information are compared with the keywords in the work content to obtain overlapping keywords in the work content.

[0106] According to the number of types of the overlapping keywords corresponding to each of the desired occupations to be selected, the multiple desired occupations to be selected are sorted to obtain a sequence of the desired occupations to be selected.

[0107] In this embodiment, the number of overlapping keywords can indirectly reflect the matching degree between an individual's current work experience and the desired occupation to be selected. The greater the number of overlapping keywords, the higher the matching degree, and the more qualified the user is for the desired occupation to be selected. Priority recommendation can be made, and targeted recommendations can be made based on different users to increase the probability of users choosing suitable occupations and enhance the user's employment guidance experience.

[0108] Optionally, obtaining a recommended occupation sequence according to the occupation to be selected and the desired occupation to be selected, and pushing the recommended occupation sequence to the user terminal comprises:

[0109] Starting from the starting ends of the to-be-selected occupation sequence and the to-be-selected expected occupation sequence respectively, a preset number of occupations are selected to obtain a first recommended occupation sequence.

[0110] The sequence of occupations to be selected is compared with the sequence of expected occupations to be selected, and occupations common to the two sequences are extracted to obtain a second recommended occupation sequence.

[0111] The first recommended occupation sequence and the second recommended occupation sequence are combined, and the same occupations in the combined sequence are retained to obtain the recommended occupation sequence.

[0112] Specifically, for example, the first five occupations in the two sequences are selected, and a total of ten occupations are obtained to form the first recommended occupation sequence. Then, the occupations common to the two sequences are searched to obtain the second recommended occupation sequence. If the occupation appears in both sequences, it means that the occupation has a certain degree of match with the user's current ability and is also the occupation that the user is currently interested in. This occupation also needs to be recommended first, even if it is at the back of the two sequences. The first recommended occupation sequence and the second recommended occupation sequence are merged, and the repeated occupations are selected and retained to avoid repeated recommendation of the same occupation, and the recommended occupation sequence is obtained.

[0113] Optionally, after obtaining a recommended career sequence according to the to-be-selected career and the to-be-selected desired career, and pushing the recommended career sequence to the user terminal, the career guidance method based on the knowledge graph further includes:

[0114] According to the recommended occupation viewed by the user fed back by the user terminal, the work content of the recommended occupation viewed by the user is called, and keywords in the work content are extracted.

[0115] Specifically, for example, if a user views occupation 1 on the user side, the work content of occupation 1 is called and the keywords of the work content of occupation 1 are extracted; if the user continues to view occupation 2, the content of occupation 2 is called and the keywords of the work content of occupation 1 are extracted; and so on, keywords are continuously accumulated.

[0116] The keywords corresponding to the recommended occupations viewed multiple times by the user are counted to obtain the number of occurrences of each keyword.

[0117] According to the number of occurrences of the keywords, the keyword sequence is obtained from largest to smallest.

[0118] Specifically, mechanical design appears in the occupational work contents that have been viewed many times, and the number of mechanical design appearances is relatively high, indicating that content related to mechanical design is the work content that users are currently more interested in, and this keyword will be at the front position of the keyword sequence.

[0119] A preset number of keywords are selected from the starting end of the keyword sequence, and the order of the occupations in the recommended occupation sequence is adjusted according to the number of times the selected multiple keywords appear in each occupation in the recommended occupation sequence to obtain a preferred sequence of recommended occupations that the user is interested in.

[0120] Specifically, this embodiment continuously analyzes the keywords that the user is currently interested in based on the user's real-time viewing situation, and reversely searches the recommended career sequence based on the keywords of interest to find out the number of keywords of interest contained in these keywords, and moves the careers that contain a larger number of keywords of interest to the front of the recommended career sequence for priority recommendation. The larger the number, the frontier the position, which makes it easier for users to see the careers they are interested in first and reduces user search time.

[0121] like Figure 3 As shown, an optional embodiment of the present invention provides a career guidance system based on a knowledge graph, including:

[0122] The resume acquisition module 100 is used to acquire the resume information uploaded by the user.

[0123] The graph query module 200 is used to extract the personal information in the resume information and the keywords in the work experience, and query the occupational knowledge graph based on the keywords to obtain a plurality of matching candidate occupations.

[0124] The test push module 300 is used to determine whether the desired occupation or hobbies uploaded by the user are obtained; when the desired occupation or hobbies uploaded by the user are not obtained, a career trend analysis test is pushed to the user.

[0125] The test analysis module 400 is used to obtain the user's desired occupation to be selected based on the user's feedback on the occupation tendency test.

[0126] The occupation analysis module 500 is used to obtain a recommended occupation sequence according to the occupations to be selected and the desired occupations to be selected, and push the recommended occupation sequence to the user end.

[0127] In this embodiment, the graph query module 200 extracts keywords from personal information and work experience in the resume information uploaded by the user obtained by the resume acquisition module 100, and queries the occupational knowledge graph based on the keywords to obtain multiple matching candidate occupations, thereby obtaining some recommended occupations on one hand; on the other hand, the test push module 300 determines whether the desired occupation or hobbies uploaded by the user are obtained; when the desired occupation or hobbies uploaded by the user are not obtained, a career trend analysis test is pushed to the user to help the user understand himself when the user does not have a clear understanding of himself; the test analysis module 400 obtains the user's candidate desired occupation based on the user's feedback on the career trend test, thereby obtaining some recommended occupations on the other hand; finally, the occupation analysis module 500 obtains a recommended occupation sequence based on the candidate occupations and the desired occupations, and pushes the recommended occupation sequence to the user end. From the occupations obtained by querying and screening in the two directions, occupations worthy of recommendation are obtained in a comprehensive manner. The recommended occupations are rich in variety and cover a wide range. Not only the user's major and previous work experience, but also the user's interests and personal expectations are taken into consideration. Career recommendations can be made from multiple angles, broaden the user's employment horizons, avoid the problem of inappropriate career choices due to the small number of occupations touched upon, increase the probability of users choosing suitable occupations, and reduce the probability of users lagging behind in their career development.

[0128] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the scope defined by the claims.

Claims

1. A career guidance method based on knowledge graph, characterized in that: include: Get the resume information uploaded by the user; Extracting keywords from personal information and work experience in the resume information, and searching the occupational knowledge graph based on the keywords to obtain a plurality of matching occupations to be selected; Determine whether the desired career or hobbies uploaded by the user are obtained; When the desired occupation or hobbies uploaded by the user are not obtained, a career trend analysis test is pushed to the user; Obtaining the user's desired occupation to be selected according to the user's feedback on the occupational trend analysis test; Obtaining a recommended career sequence according to the to-be-selected career and the to-be-selected desired career, and pushing the recommended career sequence to a user terminal; The resume information includes personal age, graduation school level, major, personal education, expected salary and work experience keywords; the professional knowledge graph includes multiple nodes, multiple nodes include career nodes and attribute nodes, the career nodes are connected to the attribute nodes through relationship lines, and the attribute nodes include age, school, major, education, salary and job content keywords; The extracting of the personal information in the resume information and the keywords in the work experience, and searching the occupational knowledge graph according to the keywords, to obtain a plurality of matching candidate occupations include: According to the major, the personal education or the work experience keywords in the resume information, the occupational knowledge graph is searched, and the major, the education or the job content keywords in the occupational knowledge graph are located to obtain a plurality of initial attribute nodes; Taking the initial attribute node as a starting point, along the relationship line connected to the initial attribute node, searching for the attribute node of the age, the school or the salary on the relationship chain where the initial attribute node and the career node are located, and counting the number of the attribute nodes on each relationship chain that match the individual age, the graduation school level or the expected salary in the resume information; When the number is greater than the number threshold, the occupation node at one end of the relationship chain corresponding to the number is selected, and the occupation of the occupation node is marked as the to-be-selected occupation.

2. The career guidance method based on knowledge graph according to claim 1, characterized in that: When the number is greater than the number threshold, the occupation node at one end of the relationship chain corresponding to the number is selected, and the occupation of the occupation node is marked as the occupation to be selected, further comprising: According to the number, the corresponding occupations to be selected are sorted from large to small to obtain a sequence of occupations to be selected.

3. The career guidance method based on knowledge graph according to claim 2 is characterized in that: The method of obtaining the user's desired occupation to be selected based on the user's feedback on the occupation trend analysis test includes: Obtaining the interests and personality characteristics of the user according to the user's feedback on the career trend analysis test; According to the interests and personality traits, the personnel characteristics required for the occupation and the job content keywords are queried to obtain the desired occupation to be selected.

4. The career guidance method based on knowledge graph according to claim 3 is characterized in that: After querying the personnel characteristics required for the occupation and the keywords of the job content according to the interests and personality characteristics to obtain the desired occupation to be selected, the method further includes: Retrieving the job content of the desired occupation to be selected, and extracting keywords from the job content; Compare the work experience keywords in the resume information with the keywords in the work content to obtain different keywords in the work content; According to the difference keywords, intercepting the sentences including the difference keywords in the work content; The difference keywords and the sentences are pushed to the user terminal to remind the user to make up for the ability deficiencies.

5. The career guidance method based on knowledge graph according to claim 4 is characterized in that: After comparing the work experience keywords in the resume information with the keywords in the work content to obtain the difference keywords in the work content, the method further includes: According to the difference keywords and the corresponding desired occupations to be selected, screening the training information corresponding to the desired occupations to be selected; Counting the number of types of the difference keywords contained in different training information; Arranging the different training information from large to small according to the number of types of the difference keywords to form a training information sequence; The training information sequence is sent to the user terminal.

6. The career guidance method based on knowledge graph according to claim 4 is characterized in that: After calling the job content of the desired occupation to be selected and extracting keywords from the job content, the method further includes: Compare the work experience keywords in the resume information with the keywords in the work content to obtain overlapping keywords in the work content; According to the number of types of the overlapping keywords corresponding to each of the desired occupations to be selected, the multiple desired occupations to be selected are sorted to obtain a sequence of the desired occupations to be selected.

7. The career guidance method based on knowledge graph according to claim 6 is characterized in that: The step of obtaining a recommended occupation sequence according to the occupation to be selected and the desired occupation to be selected, and pushing the recommended occupation sequence to the user terminal comprises: Starting from the start ends of the to-be-selected occupation sequence and the to-be-selected expected occupation sequence respectively, a preset number of occupations are selected to obtain a first recommended occupation sequence; Comparing the sequence of occupations to be selected with the sequence of expected occupations to be selected, extracting the occupations common to the two sequences, and obtaining a second recommended occupation sequence; The first recommended occupation sequence and the second recommended occupation sequence are combined, and the same occupations in the combined sequence are retained to obtain the recommended occupation sequence.

8. The career guidance method based on knowledge graph according to claim 1, characterized in that: After obtaining a recommended occupation sequence according to the occupation to be selected and the desired occupation to be selected, and pushing the recommended occupation sequence to the user terminal, the method further includes: According to the recommended occupation viewed by the user fed back by the user terminal, calling the work content of the recommended occupation viewed by the user, and extracting keywords in the work content; Counting the keywords corresponding to the recommended occupations viewed multiple times by the user, and obtaining the number of occurrences of each keyword; According to the number of occurrences of the keywords, the keyword sequence is obtained from large to small; A preset number of keywords are selected from the starting end of the keyword sequence, and the order of the occupations in the recommended occupation sequence is adjusted according to the number of times the selected multiple keywords appear in each occupation in the recommended occupation sequence to obtain a preferred sequence of recommended occupations that the user is interested in.

9. A career guidance system based on knowledge graph, characterized in that: include: Resume acquisition module, used to obtain resume information uploaded by users; A graph query module is used to extract the personal information in the resume information and the keywords in the work experience, and query the occupational knowledge graph according to the keywords to obtain a plurality of matching candidate occupations; A test push module is used to determine whether the desired occupation or hobbies uploaded by the user are obtained; when the desired occupation or hobbies uploaded by the user are not obtained, a career trend analysis test is pushed to the user; A test analysis module, used to obtain the user's desired occupation to be selected based on the user's feedback on the occupation trend analysis test; A career analysis module, used for obtaining a recommended career sequence according to the to-be-selected career and the to-be-selected desired career, and pushing the recommended career sequence to a user terminal; The resume information includes personal age, graduation school level, major, personal education, expected salary and work experience keywords; the professional knowledge graph includes multiple nodes, multiple nodes include career nodes and attribute nodes, the career nodes are connected to the attribute nodes through relationship lines, and the attribute nodes include age, school, major, education, salary and job content keywords; The extracting of the personal information in the resume information and the keywords in the work experience, and searching the occupational knowledge graph according to the keywords, to obtain a plurality of matching candidate occupations include: According to the major, the personal education or the work experience keywords in the resume information, the occupational knowledge graph is searched, and the major, the education or the job content keywords in the occupational knowledge graph are located to obtain a plurality of initial attribute nodes; Taking the initial attribute node as a starting point, along the relationship line connected to the initial attribute node, searching for the attribute node of the age, the school or the salary on the relationship chain where the initial attribute node and the career node are located, and counting the number of the attribute nodes on each relationship chain that match the individual age, the graduation school level or the expected salary in the resume information; When the number is greater than the number threshold, the occupation node at one end of the relationship chain corresponding to the number is selected, and the occupation of the occupation node is marked as the to-be-selected occupation.

Images