Social relation knowledge graph generation method based on artificial intelligence and robot system

A technology of knowledge graph and relational knowledge, applied in relational databases, instruments, semantic tool creation, etc., can solve problems such as lack of comprehensive consideration of time and space

Inactive Publication Date: 2019-12-31
NANJING INST OF INTELLIGENT COMPUTING CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to address the defects or insufficiencies in the prior art and provide an artificial intelligence-based social relationship knowledge map generation method and robot system to solve the shortcomings in the prior art that do not take time and space into account when generating user relationships

Method used

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  • Social relation knowledge graph generation method based on artificial intelligence and robot system
  • Social relation knowledge graph generation method based on artificial intelligence and robot system
  • Social relation knowledge graph generation method based on artificial intelligence and robot system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0072] Such as figure 1 As shown, a method for generating a social relationship knowledge graph is provided, including the step S100 of acquiring experience, the step S200 of extracting experience, the step S300 of intersecting experience, the step S400 of acquiring intersection information, and the step S500 of generating relationship.

[0073] The experience obtaining step S100 is used to obtain the experience in each user's resume. The resume includes the user's personal resume, also includes resumes obtained from network information such as social networking sites, and also includes all information including the user's experience.

[0074] The experience extraction step S200 is used to extract the time period of each experience and the unit where the user is located in the time period from the experience of each user's resume.

[0075] Experience intersection step S300, for seeking the intersection of every two experiences belonging to different users

[0076] E.g:

[0...

Embodiment 2

[0094] Such as figure 2 As shown, according to the social relationship knowledge map generation method provided in Example 1,

[0095] Wherein, the experience acquisition step S100 includes the education and work experience acquisition step S110.

[0096] Education and work experience acquisition step S110 is used to acquire the education experience and work experience in each user's resume.

[0097] The resume can be input by the user, or obtained from Baidu Encyclopedia or other websites, and the education experience and work experience can be extracted from it.

[0098] E.g

[0099] Zhang San

[0100] Educational experience

[0101] 2010.9-2014.7 A1 University B11 College

[0102] 2014.9-2017.7 A2 University B21 College

[0103] work experience

[0104] 2017.9-2018.7 A3 company B31 department

[0105] 2018.9-2019.7 A4 company B41 department

[0106] Li Si

[0107] Educational experience

[0108] 2011.9-2015.7 A5 University B51 College

[0109] 2015.9-2018.7 A2 ...

Embodiment 3

[0131] Such as image 3 As shown, according to the social relationship knowledge map generation method provided in Example 1,

[0132] Wherein, the intersection information obtaining step S400 includes a first intersection information obtaining step S410.

[0133] The first intersection information acquisition step S410 is used to acquire the time period and unit information of the intersection part for every two experiences belonging to different users whose time period intersection is not empty and unit intersection is not empty by matching the experiences.

[0134] The specific steps for finding the unit intersection of two user experiences (one experience of user A and one experience of user B)

[0135] Extract first-level unit names (such as school names, company names, and research institute names, identified and extracted based on keywords such as "university, company, research institute"), and second-level unit names (such as college names) from the unit information e...

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Abstract

The invention discloses an artificial intelligence-based social relationship knowledge graph generation method and a robot system. The method comprises an acquisition step, an extraction step, an intersection step, an intersection information acquisition step, a relationship generation step, a knowledge graph generation step and an entity acquisition step. According to the method and the system, through a user relationship knowledge graph generation technology based on artificial intelligence and big data, the intelligence and high efficiency of user relationship knowledge graph generation areimproved.

Description

technical field [0001] The invention relates to the field of information technology, in particular to an artificial intelligence-based method for generating a social relationship knowledge graph and a robot system. Background technique [0002] In the process of realizing the present invention, the inventor found that there are at least the following problems in the prior art: the existing user relationship is only limited to the user relationship between whether they belong to the same school graduation or work in the same unit, and there is no combination of time and work place If you analyze it together, then it may happen that you study or work in the same unit but at different times, and you may be misjudged as a classmate or colleague. For example, although some users have a relationship with the same school or company, they belong to a certain school or company at different times. [0003] Therefore, the prior art still needs to be improved and developed. Contents ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36
CPCG06Q50/01G06F16/288
Inventor 朱定局
Owner NANJING INST OF INTELLIGENT COMPUTING CO LTD
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