Safety data semantic analysis method and system based on semantic network

Abstract

The invention discloses a safety data semantic analysis method and system based on a semantic network. The method comprises the steps that monitoring and data acquisition are conducted on a set target system, and safety data needed by system analysis is acquired; the acquired safety data is preprocessed to obtain preprocessed data; semantization processing is conducted on the preprocessed data, and a data semantic graph is generated; a data analysis operation is conducted on the preprocessed data according to the data semantic graph to obtain a data analysis result; the preprocessed data is visually displayed according to the data analysis result. The system comprises an acquisition module, a preprocessing module, a semantization module, a data analysis module and a displaying module. According to the method and system, due to the fact that formalized definition and description are conducted on the body model modeling process, the process for constructing a body model of a semantic network application is more intuitive, and a reference is provided for construction of the semantic network application. The method and system can be widely applied to a semantic network technology.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a semantic web-based security data semantic analysis method and system. Background technique [0002] The popularization of smart devices has allowed more users to access the mobile Internet through browsers or applications in the devices, making the data and information of the traditional Internet show an explosive growth trend. On the one hand, massive data can be consulted and retrieved by users. The information is more convenient to use and the results are more accurate. But it also puts a lot of pressure on the direct processing of information by machines. It is difficult for machines to directly process a lot of information in reality that is readable and understandable by people. In order to convert a large amount of heterogeneous information in reality into information that can be easily understood by machines, so as to improve the efficiency of data analysis and info...

AI Technical Summary

Problems solved by technology

[0004] However, the current Semantic Web technology has the following problems: lack of formalized ontology modeling process, vague description of ontology model modeling process, difficulty in establishing a basic domain ontology model to build Semantic Web applications; Semantic Web applications lack integrated built-in model search Subsystems (keyword search and specific search language search), it is difficult to carry out information retrieval on the established ontology model; Semantic Web applications lack the application of the Jena reasoning machine subsystem, it is difficult to re-mine and discover knowledge; most security environment assessment software The source of collected information is single, and the data are independent of each other. It is impossible to effectively integrate security information to evaluate the environment as a whole; many existing application built-in search engines stay at the keyword search stage, and it is difficult to find the semantic meaning and connection of internal data in the system. The accuracy of search results is poor; the ontology models built by most Semantic Web applications are described using RDF / XML. Although this description is machine-friendly, it is difficult for users to read and understand the results of application data processing.

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