CN110717051A - Knowledge graph construction method based on social network - Google Patents
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Abstract
The invention provides a knowledge graph construction method based on a social network, which comprises the following steps: acquiring user registration information of a plurality of internet websites and APP (application) to generate a user registration table; directionally crawling user data information which is publicly shared by each user in a specified internet website and APP application according to the information of the user registry, extracting keywords in the data information, and storing the keywords in a user data table by taking the user as a unit; generating a user data table by using the user registration table and the user data table; crawling virtual identity information of a user through a search engine to generate a virtual identity information table; and combining the user data table and the virtual identity information table to generate a virtual identity knowledge graph. The method and the device solve the problem that the friends which can be identified cannot be recommended in the conventional social contact mode, can accurately recommend people which can be identified or people with the same interest and love to the user, expand the friend circle of the user and improve the social contact experience of the user.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a knowledge graph construction method based on a social network.
Background
In the current society, most lives of people have the shadow of the internet, and the internet provides great convenience for the life and production of people and is not exceptional in life and social contact. The internet realizes the possibility of real-time communication for two people far away from the thousands of miles, thereby not only subverting the traditional paper communication mode, but also enhancing the feelings among people. But in traditional social contact, friends can be added only on the basis of the existing account of the other party. For some people who have never reached, it is almost impossible for people who are not acquainted with a prime to establish a friend relationship. The social range is limited, and people with the same characters and good mutual love can not be known. The circle of friends is restricted, and simultaneously, the self-eyeground of people is also restricted.
In the traditional social contact, the friend relationship can be established only by knowing the contact way of the other party, and the circle of friends cannot be effectively enlarged, and friends with the same aspiration cannot be found. At present, friends which can be known cannot be recommended in the social contact adopting the traditional mode, and the circle of friends of people is limited to a certain extent.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide a knowledge graph construction method based on a social network.
In order to achieve the above object, an embodiment of the present invention provides a knowledge graph construction method based on a social network, including the following steps:
step S1, obtaining user registration information of a plurality of Internet websites and APP applications, and generating a user registration table;
step S2, directionally crawling user data information which is publicly shared by each user in a specified Internet website and APP application according to the user name information of the user registry, extracting keywords in the data information, and storing the keywords in a user data table by taking the user as a unit;
step S3, generating a user data table by the user registration table and the user profile table, wherein the user data table comprises a plurality of groups of user social network information;
step S4, crawling the virtual identity information of the user through the search engine to generate a virtual identity information table, wherein the virtual identity information table comprises: a plurality of groups of user virtual identity information;
step S5, merging the user data table and the virtual identity information table to generate a virtual identity knowledge graph, where the virtual identity knowledge graph stores social network information and virtual identity data of multiple users.
Further, in the step S1, the user registration information includes: the mailbox address is used for registration, the mobile phone number, the user name, the website name and the APP name are used for registration.
Further, in the step S2, the user profile information includes: comment content, forwarding content, subject posts, blog content, microblog content and friend circle content shared by the website and the APP.
Further, in step S3, the user registration table and the user profile table are merged with each other by taking the user name of each user as a unit, so as to generate the user data table, and registration information and public sharing information corresponding to each user are recorded in each user data table.
Further, in step S4, each set of the user virtual identity information includes: nickname, username, website name, web page link, virtual identity information type, and virtual identity data.
Further, according to the virtual identity knowledge graph, all keywords corresponding to each user are inquired, recommendation relations are automatically established among the users for different users with keyword repetition rates reaching preset threshold values, and recommendation notifications are sent to the users.
Further, the preset threshold value of the repetition rate is 25% -50%.
Further, after establishing the recommendation relationship, sending a recommendation notification to an internet website and an APP application of a relevant user, wherein the recommendation notification includes: recommending a user name of the user, repeating the keyword content, and recommending a homepage link of the user.
According to the knowledge graph construction method based on the social network, the friend network knowledge graph is constructed facing the social network by searching the registration information, the public content and the virtual identity information of the user on the Internet, the interest content and the life footprint of the user are obtained, and therefore the friend network knowledge graph is recommended to other users with the same or similar information, the problem that friends which can be known cannot be recommended in the traditional social network is solved, the people which can be known or have the same interest and love can be accurately recommended to the user, the friend circle of the user is enlarged, and the social experience of the user is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow diagram of a social network based knowledge graph construction method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a knowledge graph construction method based on a social network according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The invention provides a knowledge graph construction method based on a social network, which can realize friend recommendation by constructing the knowledge graph of the social network, and accurately recommend people who may know or people who have the same interest and love to a user.
As shown in fig. 1, the method for constructing a knowledge graph based on a social network according to an embodiment of the present invention includes the following steps:
step S1, when the user registers and logs in the internet website and APP application, prompting the user in advance whether to agree to collect registration or login information so as to more accurately push people who may be known, acquiring user registration information of a plurality of internet websites and APP applications on the basis of the agreement of the user, and generating a user registration table.
In an embodiment of the present invention, the user registration information includes: and data such as a mailbox address for registration, a mobile phone number for registration, a user name, a website name and an APP application name are used for registration.
For example, the internet website and APP application with reference to fig. 2 may be: hundredth, Taobao, QQ, Wechat, microblog, know-wait applications. And storing each group of collected user registration information into a user registration information table (data table I). In the user registration information table, each user contains multiple sets of identities.
Step S2, directionally crawling user profile information that each user publicly shares in the appointed Internet website and APP application according to the user name information of the user registry, extracting keywords in the profile information, and storing the keywords in the user profile table by taking the user as a unit.
In an embodiment of the invention, the user profile information comprises: comment content, forwarding content, subject posts, blog content, microblog content, friend circle content and the like shared by the website and the APP.
Step S3, generate a user data table (data table II) from the user registry and the user profile table, wherein the user data table includes a plurality of sets of user social network information.
Specifically, in this step, the user registration table and the user data table are merged with each other by taking the user name of each user as a unit, so as to generate a user data table, and registration information and public sharing information corresponding to each user are recorded in each user data table.
Step S4, crawling the virtual identity information of the user through the search engine to generate a virtual identity information table, wherein the virtual identity information table comprises: and the virtual identity information of the plurality of groups of users comprises a plurality of identity data.
In an embodiment of the present invention, each group of user virtual identity information includes: nickname, username, website name, web page link, virtual identity information type, virtual identity data, and the like.
Step S5, merging the user data table and the virtual identity information table to generate a virtual identity knowledge graph, where the virtual identity knowledge graph stores multiple sets of user information, and each set of user information includes social network information and virtual identity data of the user.
In addition, the invention can further inquire all the keywords corresponding to each user according to the virtual identity knowledge graph, automatically establish recommendation relations among the users for different users with the keyword repetition rate reaching a preset threshold value, and send recommendation notifications to the users. For example: informing the user as "a person who may know" or "a person who has a common interest in love with you"
And after the recommendation relation is established, sending a recommendation notice to the internet website and the APP application of the relevant user. Wherein recommending the notification includes: recommending a user name of the user, repeating the keyword content, and recommending a homepage link of the user. After receiving the recommendation notification, the user can click to enter the homepage of the recommendation user if the user is interested in the recommendation notification, and a friend relationship is established.
In the embodiment of the present invention, the predetermined threshold value of the repetition rate is 25% to 50%. Preferably, the preset threshold value of the repetition rate is 40%. It should be noted that the threshold may be adjusted, and the higher the threshold is, the higher the similarity of the recommended users is.
According to the knowledge graph construction method based on the social network, the friend network knowledge graph is constructed facing the social network by searching the registration information, the public content and the virtual identity information of the user on the Internet, the interest content and the life footprint of the user are obtained, and therefore the friend network knowledge graph is recommended to other users with the same or similar information, the problem that friends which can be known cannot be recommended in the traditional social network is solved, the people which can be known or have the same interest and love can be accurately recommended to the user, the friend circle of the user is enlarged, and the social experience of the user is improved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A knowledge graph construction method based on a social network is characterized by comprising the following steps:
step S1, when a user registers and logs in the Internet websites and the APP, acquiring user registration information of a plurality of Internet websites and APP on the basis of obtaining user agreement confirmation, and generating a user registration table;
step S2, directionally crawling user data information which is publicly shared by each user in a specified Internet website and APP application according to the user name information of the user registry, extracting keywords in the data information, and storing the keywords in a user data table by taking the user as a unit;
step S3, generating a user data table by the user registration table and the user profile table, wherein the user data table comprises a plurality of groups of user social network information;
step S4, crawling the virtual identity information of the user through the search engine to generate a virtual identity information table, wherein the virtual identity information table comprises: a plurality of groups of user virtual identity information;
step S5, merging the user data table and the virtual identity information table to generate a virtual identity knowledge graph, where the virtual identity knowledge graph stores social network information and virtual identity data of multiple users.
2. The social network based knowledge graph building method according to claim 1, wherein in the step S1, the user registration information includes: the mailbox address is used for registration, the mobile phone number, the user name, the website name and the APP name are used for registration.
3. The social network based knowledge graph building method according to claim 1, wherein in the step S2, the user profile information includes: comment content, forwarding content, subject posts, blog content, microblog content and friend circle content shared by the website and the APP.
4. The method for building a knowledge graph based on a social network as claimed in claim 1, wherein in the step S3, the user registry and the user profile table are merged by taking a user name of each user as a unit, so as to generate the user data table, and each user data table records registration information and public sharing information corresponding to each user.
5. The social network based knowledge graph building method according to claim 1, wherein in the step S4, each group of the user virtual identity information includes: nickname, username, website name, web page link, virtual identity information type, and virtual identity data.
6. The social network-based knowledge graph construction method according to claim 1, wherein all keywords corresponding to each user are queried according to the virtual identity knowledge graph, recommendation relations are automatically established among the users for different users with keyword repetition rates reaching a preset threshold, and a recommendation notification is sent to the users.
7. The social network-based knowledge graph construction method according to claim 6, wherein the preset threshold value of the repetition rate is 25% -50%.
8. The social network-based knowledge graph construction method of claim 6, wherein after establishing a recommendation relationship, a recommendation notification is sent to an internet website and an APP application of a relevant user, wherein the recommendation notification comprises: recommending a user name of the user, repeating the keyword content, and recommending a homepage link of the user.
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| CN201910961006.8A CN110717051A (en) | 2019-10-11 | 2019-10-11 | Knowledge graph construction method based on social network |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11641330B2 (en) | 2020-08-06 | 2023-05-02 | International Business Machines Corporation | Communication content tailoring |
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