TRAKS: Terrorist Related Assessment using Knowledge Similarity

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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
Company
Person
Country
City
Country
RealState
Bank
Loan
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So far

• 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

• Website Chris
• Templates Aleman, Chris, Satya
• Servlet engine setup Aleman
• Servlet implementation Satya
• Testbed, ontology Aleman
• Code prototype Chris