Data mining based text data network construction system

Abstract

The invention discloses a data mining based text data network construction system. The system comprises a data collection module, a data mining module, a social network construction module, a visual display module and a database, wherein the data collection module receives newly added text data, and performs word segmentation on the text data sequentially; the data mining module extracts an eigenvalue of the newly added text data subjected to the word segmentation in the database, compares the newly added text data with stored text data subjected to eigenvalue extraction in the database, and performs association mapping on data with an eigenvalue closest to the eigenvalue of the newly added data; the social network construction module constructs a file by taking a sender of the text data received by the data collection module as a file name and taking a newly added text data name, a name of the data subjected to the association mapping and a sender of data corresponding to the data subjected to the association mapping as file attributes, and forms a social network ontology library; and a visual display module analyzes data corresponding to the file, and performs display in a manner of line segments or nodes.

Description

technical field [0001] The invention relates to a text data network construction system based on data mining technology. Background technique [0002] Expert knowledge is a valuable resource for enterprises, and expert knowledge is mainly embodied in the form of text data. Therefore, the management of expert knowledge is the management of text data created by experts. At present, there are two ways to manage expert knowledge. On the one hand, build expert yellow pages or expert maps. However, this method takes the research field declared by the expert itself as the connection point, and the positioning of the expert's research direction is too broad and difficult to grasp. The expert's precise research technology also ignores the diversity and subtlety of the expert's research field and the dynamic change or adjustment of the research direction; on the other hand, the text analysis technology is used to intelligently analyze the expert's research results and automatically a...

AI Technical Summary

Problems solved by technology

At present, there are two ways to manage expert knowledge. On the one hand, build expert yellow pages or expert maps. However, this method takes the research field declared by the expert itself as the connection point, and the positioning of the expert's research direction is too broad and difficult to grasp. The expert's precise research technology also ignores the diversity and subtlety of the expert's research field and the dynamic change or adjustment of the research direction; on the other hand, the text analysis technology is used to intelligently analyze the expert's research results and automatically analyze the expert's research field. Although this method is conducive to accurately grasping the research direction of experts, it only focuses on the experts themselves, ignoring the degree of correlation between experts, and does not realize the aggregation analysis of expert groups in the same field, same direction, and same technical point

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