TRAKS: Terrorist Related
Assessment using Knowledge
Similarity
Boanerges Aleman-Meza
Chris Halaschek
Satya Sanket Sahoo
CSCI 8350 – Enterprise Integration
Semantic Web Course
Large Scale Distributed Information Systems
(LSDIS) Lab
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Motivation
• identification of contributing factors to
terrorist activities
– money laundering
– identity theft
– terrorist planning
• 9/11 terrorist attacks
– claimed around 3000 lives [1],
– caused estimated “$120 billion of damage”
[2]
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Current Approaches
• Data mining techniques
• Rule-based mechanisms
• Structural match of known rules/patters
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Our Approach
• Semantic Similarity Template Match
– Capture the knowledge with OWL
– Define an ontology for money laundering, …
– Case Scenario modeled as a template
– “fill-in” the template with instance data 
scenario found ! notify the police !
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Where are the
Semantics?
• Our system uses OWL
• The template is defined with OWL and our
‘template’ and ‘core-template’ classes
• Instance data is defined in OWL
• This allows our code to work:
– with anyone's ontology/instance/template
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Where is the Semantic
similarity?
• Template example:
– [CEO] transfers Money To [InternationalBank]
• Similarity based on the ontology:
– [CTO] deposits Check In [DomesticBank]
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Three components:
– Ontology:
– Data Set:
– Template:
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Templates
[Person]
[Company]
[Country]
[City]
[Country]
[RealState]
[Loan]
[Bank]
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So far…
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Website
Templates
Servlet engine setup
Servlet implementation
Data Sets
ontology
Code prototype – step 1 (core match)
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References
[1] The Economic Cost of Terrorism
[2] September 11, 2001: A day of terror
[3] Financial Action Task Force on Money Laundering Homepage
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Responsabilities
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Website: Chris
Templates: Aleman, Chris, Satya
Servlet engine setup: Aleman
Servlet implementation: Satya
Testbed, ontology: Aleman
Code prototype: Chris
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