Paper Info
Title
Semantic Filters in Intelligence Analysis
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.
Year
DOI
Venue
2011
10.1109/WI-IAT.2011.218
Web Intelligence/IAT Workshops
Keywords
Field
DocType
relevant linguistic theory,detailed case study,human intelligence,analysis tool,relevant piece,intelligence analysis,semantic filters,major aspect,big chunk,semantically relevant filter,good filter,computational linguistics,ubiquitous computing,intrusion detection system,computer networks,authentication,intelligence,protocols,authentication protocol,authorization,semantics,computer network,digital signatures
Data mining,Ontology,Authentication,Computer science,Natural language processing,Artificial intelligence,Ubiquitous computing,Intrusion detection system,Intelligence analysis,Information retrieval,Human intelligence,Computational linguistics,Semantics
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
1
Name
Order
Citations
PageRank
Jim Q. Chen11018.92