A plan to deploy Ontology mediation information flow architecture for US Customs and Border Protection Presentation by OntologyStream Inc Paul Stephen Prueitt, PhD Ontology Tutorial 4, copyright, Paul S Prueitt 2005

Semantic Extraction Link Analysis Pattern recognition Ontology Tools Statistics Advanced Trade Data Harmonized Tariff Schedule Detailed work with tools over available data Practical problem: Provide the three Cs, clarity, consistency, and completeness in EACH judicial review of a commodity in passage across national boarders. Integrated collection of reified ontologies with some specific inferences and some information organization and retrieval Possible deployment as U. S. Custom’s Total Information Awareness (TIA) capability

A modular ontology management infrastructure provides various services in the context of field reporting about transactions upper level ontologies “other” upper level ontology Law governing US Customs Advanced Trade Data Economic Supply Chain Data Findings ontologyEntities ontologyGain/Risk ontology sources of data Location ontology Later application areas HTS Ontology

Written reports Structural Event. Ontology Framework In our work, human knowledge is captured separately in two computable forms: implicit (semantic extraction ontology) and explicit OWL DL ontology Gain / Risk Ontology Framework.

High Risk Ontology Expression Bio-systems Weapon-systems Commodity history analysis Entry Reports and Findings { concepts } Ontology Framework with Differential Ontology Expressions informs aligns Ontology expression about the risks measured from historical analysis of commodities US Customs cultural viewpoints expressed as sets of concepts Shipping manifests Entity histories

High Risk Ontology Expression Bio-systems Weapon-systems Commodity history analysis Entry Reports and Findings { concepts } Rapid knowledge acquisition and reporting about a transaction Ontology expression about the risks measured from historical analysis of commodities US Customs cultural viewpoints expressed as sets of concepts A transaction: Nautilus Explorer (“Nautilus”) owns and operates the M/V NAUTILUS EXPLORER, a 116-foot Canadian-flagged long-range dive boat. Nautilus would like to embark passengers in San Diego, California, on two separate occasions, for three days of diving in Mexican waters before returning to San Diego. The passengers would be embarked and disembarked at the same location in San Diego. Semi-automated generation of Reports

We define the first two dimensions of a framework to be { Anomality, Trend } union { Gain, Risk } And the other dimension to be: { measurement, assessment, name, group, event, context, rule, policy, component, function/behavior } Then, in the cross product, we have four sets of ten concepts. In fact the ten concepts are five sets of two concepts – each with an interesting “oppositional scale type” relationship. { measurement, assessment, name, group, event, context, rule, policy, component, function/behavior } This Gain/Risk Ontology Framework was “discovered” by Dr Paul Prueitt in March 2005 while thinking about US Customs Selectivity and Targeting enhancements. Dr Peter Stephenson and Dr Prueitt are extending this in the context of Cyber Security ontology mediation data analysis. gain/risk Ontology Framework (gf-OF)

{ who, where, what, how, why } x { past, present, future} Structural Event Ontology Framework The classical, existing from Greek times, six interrogatives is partitioned into three parts; {people, places, things} + { event structure with causality } + time { people, places, things } event structure 18 questions from frames (past, who, how), (past, who, why), (present, who, how) (present, who, why) (future, who, how) (future, who, why) Etc… event Structure Ontology Framework (e-SOF) e-SOF was “discovered” by Dr. Paul S. Prueitt while thinking about a US Customs ontology prototype in March 2005

Data Transfer Object (SOI) Scoped Ontology Individual Transactions Findings Entry Entry Summary Script SOI pushes information Portal pulls information databases Script pulls information Ontology Individuals have a subsumption relationship to upper abstract ontologies Ontology Framework Ontology Reasoner Scoped Ontology Individuals Human machine interface Knowledge Management visualization Knowledge Engineer visualization client visualization An event Organizing principles for end user visualization of ontology