Enterprise relationship mining method

Abstract

An enterprise relationship mining method belongs to the field of data mining, and comprises the following steps of defining the relationships , wherein the enterprise relationships comprise a legal person relationship, a shareholder relationship, a job relationship, a branch mechanism relationship, an external investment relationship and a competition relationship; acquiring the data, wherein theenterprise data comprises the business license information, the shareholder information, the employee information, the branch mechanism information and the business range labeling information; cleaning the data, checking the data consistency, and processing the invalid values and the missing values; multi-source data fusion, integrating all the information obtained by investigation and analysis, and performing unified evaluation on all the information; extracting the relationship. The enterprise relationship mining is a core for constructing an enterprise relationship graph, and the enterpriserelationship graph can display the enterprise relationships to the users in the form of structured graphs, so that the users can quickly understand and further explore the enterprise relationships. The enterprise social circles, the enterprise investment circles, the enterprise stock right structures, the enterprise actual controllers, the enterprise risk assessment and the like can be discoveredby mining the enterprise relations.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to an enterprise relationship mining method. Background technique [0002] In 2012, Google proposed the concept of knowledge graph to enhance the function of search engine. The knowledge map is a structured symbolic representation of the objective physical world, and it is also a network knowledge base, which is formed by entities with attributes linked by relationships, and the relationships also contain their own attributes. From the perspective of graph theory, the knowledge graph is essentially a conceptual network, its nodes represent entities in the objective physical world, and edges represent various semantic relationships between entities. There are various relationships between enterprises and enterprises, and between enterprises and people. Through these relationships, an enterprise relationship network, that is, an enterprise knowledge graph, can be con...

AI Technical Summary

Problems solved by technology

[0006] (2) The classification of business relationships defined in the scheme is unreasonable

[0007] (3) The rule-based relationship extractor has high accuracy and low recall rate, and poor performance on new data sets, which is not conducive to expansion

The secondary data sources are enterprise websites such as Tianyancha, Qichacha, Qixinbao, etc., which are comprehensive, but not as timely as government websites.

The third-party structured relational data is a well-mined relationship provided by the data service provider, and the authority of the relational data cannot be guaranteed

[0014] (2) The staff information on the recruitment website is poor in real-time and inaccurate

[0016] (4) The knowledge reasoning, cluster analysis and other methods used in character name alignment are not introduced in detail

[0017] (5) The method used for attribute decision-making is not clearly described

In order to quickly build enterprise relationships, some introduce third-party data when mining relationships, which cannot guarantee the authority of relational data

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