From Open Source Intelligence to Decision Making: a Hybrid Approach

by   Vitaliy Tsyganok, et al.

We provide an overview of tools enabling users to utilize data from open sources for decision-making support in weakly-structured subject domains. Presently, it is impossible to replace expert data with data from open sources in the process of decision-making. Although organization of expert sessions requires much time and costs a lot, due to insufficient level of natural language processing technology development, we still have to engage experts and knowledge engineers in decision-making process. Information, obtained from experts and open sources, is processed, aggregated, and used as basis of recommendations, provided to decision-maker. As an example of a weakly-structured domain, we consider information conflicts and operations. For this domain we propose a hybrid decision support methodology, using data provided by both experts and open sources. The methodology is based on hierarchic decomposition of the main goal of an information operation. Using the data obtained from experts and open sources, we build the knowledge base of subject domain in the form of a weighted graph. It represents a hierarchy of factors influencing the main goal. Besides intensity, the impact of each factor is characterized by delay and duration. With these parameters taken into account, main goal achievement degree is calculated, and changes of target parameters of information operation object are monitored. In order to illustrate the suggested hybrid approach, we consider a real-life example, where we detect, monitor, and analyze actions intended to discredit the National academy of sciences of Ukraine. For this purpose, we use specialized decision-making support and content monitoring software.


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