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Abstract:
Recent military operations have redefined the way modern warfare is waged. In a deliberate effort to achieve and retain information dominance and decision superiority, many innovative technologies have emerged to assist the human war fighter. Unquestionably, these technologies have generated resounding successes on the battlefield, the likes of which have never been seen. With all the success, however, there are still areas for improvement as the potential exists for further reducing already short sensor-to-shooter times. The current World Wide Web (WWW) is largely a human-centric information space where humans exchange and interpret data (2 Berners-Lee, 1 1999). The Semantic Web (SWEB) is not a separate Web, but an extension of the current one in which content is given well-defined meaning, better enabling computers and people to work in cooperation (Berners-Lee et al). The result is the availability of the various backgrounds, experiences, and abilities of the contributing communities through the self-describing content populating the SWEB (2 Berners-Lee, 1999). This thesis assesses current SWEB technologies that promise to make disparate data sources machine interpretable for use in the construction of actionable knowledge with the intent of further reducing sensor-to-shooter times. The adoption of the SWEB will quietly be realized and soon machines will prove to be of greater value to war fighting. When machines are able to interpret and process content before human interaction and analysis begins, their value will be further realized. This off-loading, or delegation, will produce faster sensor-to-shooter times and assist in achieving the speed required to achieve victory on any battlefield.
| Limitations: |
 APPROVED FOR PUBLIC RELEASE |
| Description: |
Master's thesis |
| Pages: |
289 |
| Report Date: |
SEP 2003 |
| Report Number: |
A445814 |
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