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Lyon
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-1-4577-1373-6
pp: 265-268
ABSTRACT
It is a challenge to identify the relevant pieces for further intelligence analysis among a big chunk of data. Filters have been built to provide such a function in almost all the network traffic capture and analysis tools as well as signature-based intrusion detection systems. However, most filters only work on strings of words, numbers, and/or other symbols. This paper proposes a type of context-aware and semantically relevant filters. This proposal is built on the findings in ontological semantics [1]. A detailed case study is used to show the effectiveness and efficiency of this proposal. The result of this research indicates that a good filter for intelligence analysis should incorporate relevant linguistic theories, which can explain one major aspect of human intelligence at another level.
INDEX TERMS
Ontological semantics, filters, intelligence, analsysis
CITATION
Jim Q. Chen, "Semantic Filters in Intelligence Analysis", WI-IAT, 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies 2011, pp. 265-268, doi:10.1109/WI-IAT.2011.218
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