Instead of a human analyst slogging through (maybe) 10 three-page documents in an hour, Modus Operandi has developed its military intelligence “Googlizing” system, which has been “trained” via machine learning and enabled by a semantic analysis engine to provide the military intelligence community a way to quickly conduct military intelligence searches – as simply as one would do an ordinary Google search on a home computer. The difference (besides the content and data sources, of course) is the interfacing with the military’s DCGS Integration Backbone (DIB). Military Embedded Systems’ Managing Editor Sharon Hess recently interviewed Tony Barrett and Mark Wallace of Modus Operandi to get a behind-the-scenes look at this technology.
BARRETT: Modus Operandi is a software technology company focused on rapidly solving complex intelligence information discovery, integration, and fusion problems for defense and intelligence customers. Modus Operandi has offices in Melbourne, Florida, and Aberdeen, Maryland, and employs fewer than 100 employees.
I received some information from Modus Operandi about a technology described as “Googlizing military intelligence.” So what is this program or technology, and what is it called?
BARRETT: Internally I think we’re calling it Blade, but a generic technology description is: a Wiki-based semantic engine that handles intelligence data, in the interest of shortening the intelligence analyst research track.
The background says the “Googlizing” capability is enabled by a semantic engine, as you mentioned, and machine learning.
BARRETT: Yes. We are adding semantics to structured and unstructured data. That makes the data smarter as it enters the intelligence flow, so to speak. We come up with dictionaries and vocabularies that train the computer how to recognize words, ideas, and concepts that people intuitively know, so that as intelligence data is fed into the system, it automatically either correlates or corroborates intelligence to help the analyst figure out what’s important and what’s not important.
WALLACE: Also, Googlizing involves going through lots and lots of intelligence data, making a model of what [the semantics engine] has seen. Then when people search with it, they can get to that data quickly. We can crawl through lots of data in advance; people can be sending [Blade] video data saying, “Here is what we have found.” They are crawling video data and giving their results to [our system]. Other systems or our own software might be crawling through documents and figuring out what we’re storing and understanding, what we need to put in our model. Then when someone comes to the Wiki or does a search on the DIB [DCGS Integration Backbone] for something – those are two ways to get at the information. And then once they click on a link, they are in a model like you would see in Wikipedia, where you have a page on something and you have links to other pages.
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