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Knowledge Management
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Capabilities - R&D (Enabling Technologies)

CACI's applied researchers are devoted to the state of the art and practice of KM technology. We make new and emerging capabilities serve our customers' needs in innovative ways. Whether we are performing deep evaluations to advise on best-of-breed tool choices, consulting on and integrating the use of new technologies in operational KM systems, or combining industry best practices and published research results to advance our customers' operations and data, we have one priority: enabling the mission. We put the best knowledge technologies to work for your mission needs. Examples of our technological capabilities include:

  • Machine translation: In today's global environment, important knowledge may develop in any language at any time. How can an analyst who speaks one language – or even several languages – identify important content that must be fully translated? We help our customers to triage their streams of potentially significant material, in part by integrating machine translation to produce approximate renditions of the original text in English. With combined coverage of over 40 languages, these tools can identify the critical information in most cases so that our customers can apply costly and time-consuming full translation only to the items that will improve their actionable knowledge the most.
  • Named entity extraction: The essential elements of information in documents are often found in their named entities: names of people, places, organizations, etc. Automated tools can extract these entities and provide them for a variety of further uses: as summaries, as indicators of documents of interest, as elements for link analysis, etc. We combine, tune, and enhance these technologies, operating in a wide variety of languages, in order to enhance the ability to rapidly make sense of large volumes of textual data.
  • Geospatial visualization: When an analytic task must combine a wide variety of information about an event, incident, environment, pattern of behavior, or overall trend, a geospatial view can provide a single lens for combining the disparate pieces of knowledge, leading to broader and deeper insights. We work to fully utilize geospatial indicators: by tagging data that is not already geospatially enabled, as well as by providing knowledge transfer, exploration, and enhancement based on location with data that does have geographic tags.

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