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Semantic Web Solves Crimes

Semantic Web Solves Crimes

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Semantic Web Solves Crimes

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  1. Semantic Web Solves Crimes 9 Feb 2006 Gregory Fairnak Consultant

  2. Speaker Bio • Consultant to Northrop Grumman Corp. • System Architect for Law Enforcement Information Exchange (LInX) • Technical Lead State of Texas Fusion Center • Activities related to Business Development • Over 15 years systems integration experience primarily in healthcare, public safety and homeland defense • Office 954-783-0907

  3. Introduction • Throughout the nation, law enforcement agencies and systems integrators are striving to facilitate the flow of information to help prevent and solve crimes. • Many large “data sharing” projects • If every officer knew what every officer knows? • Is there a place for the semantic web?

  4. SWANS 2005 • Semantic Web Applications for National Security • Mr. Tim Berners-LeeDirector World Wide Web Consortium • “Stop arguing what we call things” • “Context is what is really important” • Deborah McGuinness, Stanford University senior research scientist Artificial Intelligence Laboratory at Stanford University. • Useful areas for application • Natural fit to law enforcement records search

  5. Semantic Web - Primer • Smarter Web • Organize Data into Knowledge • More than just keyword match • Software agents roam page to page carrying out tasks for the user • Resource Description Framework or RDF is the organizing mechanism

  6. Problem areas addressed by Semantic Web • Integrating Multiple Data Sources • Semantic Drilling Down • Statements About Statements • Translation • Smarter Search • Source: McGuiness and Deans SWANS 4/17/05

  7. Our Problem • Finding records across one or more databases where there are multiple correct ways to describe and document what happened • Law Enforcement Records Management Systems • A database of incidents and reports • Original purpose to generate crime statistics • New purpose generate leads and solve crimes • Each system has own local dialect, lookup table • Data sharing architecture two predominate patterns

  8. One Approach SOPD METRO Warehouse Query BSO White, Male, Victim LHP Query fields mappings

  9. Another Approach SOPD METRO Query BSO White, Male, Victim LHP

  10. Our Problem (cont.) • User interface requires officer training • multiple ways entering search/query • requires exact data entry • Do not allow for typos, synonyms, concept searches like “robbery” or categories • Show me all, dark SUV’s, pickup trucks, red sports cars • National Institute for Justice Grant Request, • “Public safety officers and analysts need a simplified and consistent interface” • Missed Records

  11. Our Problem (cont.) • Data • Some fields are mapped to a standard (Race, Sex, Hair Color, Eye-Color) • Other fields such as Offense Descriptions, Make and Model of Vehicles or Pawned Items are not • Multiple ways to say the same thing

  12. Vision • A detective is assigned a recent liquor store robbery • Happened anywhere else, part of a series? • According to witnesses, the suspect wore a ski mask, held a sawed off shot gun, only grabbed large bills. They describe the suspect as white male, 6’ tall, with blue eyes.” • A software agent roams from page to page collecting relevant records

  13. Requirements • Fast implementation, data centric • Work with both approaches to data sharing • Based on Standards • Bird’s eye view no missed records • One Concept could have multiple expressions

  14. Demonstration • Show original agency incident records without preferred terms and categories • Explain the user interface • Show agency incident records with preferred terms and categories • Search for Robbery • Search White, Tall >6ft, Male • Liquor and ski mask

  15. Solution Description SOPD Seamark Server METRO Browser Query BSO LHP XSLT RDF JSP XRBR XPath SQL to XML

  16. Faceted Navigation • Organize information the way officer’s think – in flexible categories – for easier navigation • Organize search to support in the way witnesses think • Create categories based on SUV’s, Sports cars, gun manufacturer • Balanced Scorecard

  17. Strengths of semantic integration • Fast first project • Easy search • No missed records • Everything is accessible, “big picture” • Ontology development can grow over time • Ability to inherit from others • Human insight leveraged to the context

  18. Lessons Learned • XSLT fast, • not elegant, might not scale • No impact on production systems • Sometimes an unexpected values pops up • OWL, RDF or Spreadsheet • <element name><alt value><pref value> • <Sex> <m> <MALE> • <Make> <Chevy><Chevrolet> • OWL organizing concept layer

  19. Next Steps • Identify most important dimensions (user group) • Build out additional categories and dimensions (using OWL) • Pawn, Vehicle and Offense Types