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2013
Conference Paper
Titel
Elaborated search for OSINT
Alternative
Elaborated search in the context of OSINT
Abstract
National and global security is becoming increasingly reliant on rapidly making sense of and managing the growing amount of intelligence data available today. To this end, data of various formats, like text, audio, images, etc. are collected from various sources, e.g., HUMINT (Human Intelligence) and OSINT (Open Source Intelligence). They are a potential source of valuable information that, as a strategic asset, could provide a competitive advantage. But actually processing this massive amount to enable the discovery of possibly valuable information is a significant challenge. The goal is to obtain a combined view of disparate data for drawing collective inference. As part of the sense-making process, the ability to find and harvest relevant data and then to transform and enrich it for analysis has been one of the keys especially for OSINT. Thus, OSINT faces a twofold challenge: first, to manage the volume of the available data, and second, to activate the value of unstructured data assets. This paper demonstrates the application of natural language processing technologies to support the exploratory analysis of unstructured data.