Domain Independent Ontology Based Intelligence Vetting using Multiple Virtual Private Networks

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Domain IndependentOntology Based Intelligence
Vettingusing Multiple Virtual Private
Networks and Ontology Visualization
Dr. Paul Prueitt 1/29/2003
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Producing Actionable Intelligence Iterative
Process Model
UnderstandingPossible Outcomes
Human Action-Perception cycle
GeneratingOptions
Measurement and Instrumentation
Reporting
Persistent Ontology Services
Representation and Encoding
Alerting
Detecting Factsand Events
Producing and MatchingModels
DiscoveringRelationships
Object Sciences Corporation 12/5/2002
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Producing Actionable Intelligence Technology
Support
Synchronous AsynchronousCollaboration Tools
Visualization
Analysis Tools and Educational Processes
Presentation to user
Object Sciences Corporation 12/5/2002
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Simple Reusable Architecture
Network of Virtual Private Networks
Data
HTTP (SOAP,XML)
Email and Packet Transfer
Collaboration
Visualization
Applications

• Knowledge Sharing
• Foundation

Connecter Architecture
Persistence
Education
Object Sciences Corporation 12/5/2002
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Operational Architecture for Ontology Production
Data Source
Schema-dependent data
Categorizer and Inference Engines
Schema-independent data
Repository
Object Sciences Corporation 12/5/2002
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Knowledge Sharing Foundation
Industry
Vendor Tools
Repository
KSF
Core Engines Educational Services
Data
Real time analysis
Micro-transaction accounting system supporting
outcome metrics and revenue generation
Universities
Education
Distribution of revenue in compensation for use
of tools or educational services
Diagram from Prueitt (2003)
Important Innovation Knowledge Sharing
Foundation from The George Washington University
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Compensation for use of data, tools, educational
services, and work product
Education
Vendor Tools
Data
Repository
Embedded micro-transaction accounting system
supporting outcome metrics and revenue generation
Important Innovation IP protected
micro-transaction accounting system available
from Dr. Brad Cox
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Advanced data mining and natural language
processing Making the case that new
capabilities are within reach
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Differential Ontology Framework

• By the expression Differential Ontology we
  choose to mean the interchange of structural
  information between Implicit (machine-based)
  Ontology and Explicit (machine-based) Ontology
• by Implicit Ontology we mean an attractor neural
  network system or one of the variations of latent
  semantic indexing. These are continuum
  mathematics with only partial representation on
  the computer.
• by Explicit Ontology we mean an bag of ordered
  triples lt a , r, b gt , where a and b are
  locations and r is a relational type, organized
  into a graph structure, and perhaps accompanied
  by first order predicate logic (such as the Topic
  Maps or Cyc ontologies). This is a discrete
  formalism.

Implicit
Explicit
Diagram from Prueitt (2002)
Important Innovation Differential Ontology
from Dr. Paul Prueitt
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Opponent-Ontologies based on Latent Semantic
Indexing
We use LSI in a specific fashion to produce a
cognitive science figure-ground corollary
C ground produces LSI transform,
T ground
T ground ( C exemplar ) forms the
figure-from-ground
Ground text collection, C ground
Exemplar text collection, C exemplar
The figure-ground then measures the real time
flow of response passages
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Measurement of Response I
We have identified three collections of
semi-structured data (natural language)
Ground text collection, C ground
, Exemplar text collection, C exemplar
, Response text collection, C response
C exemplar B 1 È B n È . . . È B q
C ground set of documents that are
designed to cover linguistic variation that needs
to be picked up in a categorization process
C response the output from a web
Harvesting system

• C ground produces a linear algebra type
  transform T ground .
• This transform is defined from R m into R n
  (a transform between m and n dimensional
  Euclidean (vector) spaces).
• Exemplar sets, B i , I 1, . . . , q, are
  made for each q categories of human-specified
  response.
• For example, response messages might be outputs
  from a harvester system that is measuring Arabic
  response to world events.

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Measurement of Response II
Finding the correct structure and content of the
exemplar set is the key C exemplar B 1 È
B 2 È . . . È B q
T ground ( B i ) N i is the set of points
(neighborhood) in R n formed by the i th exemplar
bin Creating these neighborhoods is easy.
However, validating that the neighborhoods have
fidelity to the task at hand requires the use of
an Ontology Lens that bring fidelity into focus.
For text unit t e C response , T ground ( t
) is a point in R n
The distance T ( B i ) , T ( t ) is now
defined as the distance centroid i , T ( t )
where the centroid is a point in R300 the
stands in for the image of the i th exemplar
bin. The similarity between a response passage
and the linguistic contents of each of the
exemplar bins is approximated as a single
positive real number.
The Ontology Lens shows structural relationships
between the categories of the exemplar set, and
allows specialists to restructure the contents of
the exemplar bins so that the categories exhibit
a high degree of independence, as measured by the
degree of relationship.
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Production of Concept Metrics
Implicit Ontology
T ground (B 1)
T ground (B 2)
T ground (B 3)
Explicit Ontology
d e C response
T ground (B 4)
T ground (B 5)
For each response message, d, the implicit
ontology produces a set of concept metrics, mk
, and these concept metrics are used as the
atoms of a logic. These atoms are used to
produce an explicit ontology. The logic is then
equipped with a set of inference rules.
Evaluations rules are then added to produce an
inference about opinions of the authors of the
response set.
Diagram from Prueitt (2002)
Important Innovation Concept Metrics from
Object Science Corporation
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Inter-Role Collaboration using Ontology
Role and Event Specific View
Knowledge Worker
Views
Knowledge Repository
Periodic Update
Synchronous Collaboration
Knowledge Worker
Views
Object Sciences Corporation 12/5/2002
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Tri-level Architecture
The Tri-level architecture is based on the study
of natural systems that exist as transient
stabilities far from equilibrium. The most basic
element of this study is the Process Compartment
Hypothesis (PCH) that makes the observation that
systems come into being, have a stable period
(of autopoiesis) and then collapse. Human
cognition is modeled in exactly the same way.
Human mental events are modeled as the
aggregation of elements of memory shaped by
anticipation.
The tri-level architecture for machine
intelligence is developed to reflect the PCH. A
set of basic event atoms are developed through
observation and human analysis. Event structures
are then expressed using these atoms, and only
these atoms, and over time a theory of event
chemistry is developed and reified.
Diagram from Prueitt (1995)
Important Innovation Tri-level architecture
from Dr. Paul Prueitt
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cA/eC
Neuroscience informs us that the physical process
that brings the human experience of the past to
the present moment involves three stages. 1)
First, measured states of the world are parceled
into substructural categories. 2) An
accommodation process organizes substructural
categories as a by-product of learning. 3)
Finally, the substructural elements are evoked by
the properties of real time stimulus to produce
an emergent composition in which the memory is
mixed with anticipation. Each of these three
processes involves the emergence of attractor
points in physically distinct organizational
strata. The study of Stratified Complexity
appeals first to foundational work in quantum
mechanics and then to disciplines such as
cultural anthropology and social-biology.
categoricalAbstraction (cA) is the measurement of
the invariance of data patterns using finite set
of logical atoms derived from the measurement.
eventChemistry (eC) is a theory of type that
depends on having anticipatory processes modeled
in the form of aggregation rules, where the
aggregation is of the cA logical atoms.
Diagram from Prueitt (1995)
Important Innovation eventChemistry from Dr.
Paul Prueitt
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gF/cA/eC
Evocative generalFramework (gF) theory constructs
cA/eC knowledge bases directly in conversation
with humans
We have projected a physical theory of structural
constraint imposed on any formative processes, to
a computational architecture based on
frameworks. Various forms are conjectured to
exist as part of emergent classes, and in each
case each class of emergent types has a periodic
table like, in many ways, the atomic period
table. The Sowa-Ballard Framework has 18
semantic primitives.
Ballard/Sowa Framework
According to Alvin Toffler, knowledge will
become the central resource of the advanced
economy, and because it reduces the need for
other resources, its value will soar. (Alvin
Toffler, Power Shift, 1990). Data warehousing
concepts, supported by the technological advances
which led to the client/.server environment and
by architectural constructs such as the Zackman
Framework, can prepare organizations to tap their
inner banks of knowledge to improve their
competitive positions in the twenty-first-century.

Zackman Framework
Diagram from Prueitt (2001)
Important Innovation Framework software from
Drs. Paul Prueitt and Richard Ballard
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Situational Logic Construction
A latent technology transform, T ground , is used
to produce simple metrics on membership of
documents from the response collection C response
in the categories defined by the contents of
the bins C exemplar B 1 È B n2 È . . . È
B q
These bins are represented in the situational
logic as the logical atoms A , from which a
specific logic is constructed. These atoms are
then endowed with a set of q real numbers that
are passed to an Inference Processor.
The set of q real numbers are computed from a
formal evaluations of the structural
relationship between logic atoms using the
Ontology Lens. Atom a ? r1 , r2 , . . . ,
rq The process of developing a situational
logic is to be modeled after quasi axiomatic
theory. In this model, new data structure are
in-put as axioms, and then a process of reduction
of axioms to logical atoms occurs. The reduction
also requires the Ontology Lens, (invented 2002
by Prueitt).
Diagram from Prueitt (2002)
Important Innovation Situational logics from
Paul Prueitt
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• The Ontology Len (discovered by Prueitt, 2002) is
  a structural focus instrument that is designed
  to allow non-computer scientists to specify high
  quality exemplar sets. This is done with an
  Implicit Ontology to Explicit Ontology (IO-EO)
  loop.
• When the user puts a new unit into a bin or
  removes a unit from a bin, then the IO-EO loop
  will produce a different result.
• It is the human responsibility to govern the
  IO-EO loops so that the results have the
  properties that the human wants, mostly
  independence of categories, but perhaps some
  specific (and maybe interesting) category
  entanglements.

A graphic representation of what we call a LSI
structural similarity matrix. The similarity
is called structural because the exact notion of
semantic similarity is not known from this
algorithmic computation by itself. The
paragraphs of a small exemplar set (see appendix
A) are ordered as labels for the columns and
rows. One would expect that a paragraph would
be structurally similar with itself, and this is
in fact what one sees as a set of dots
(representing a value of 1) down the diagonal.
Diagram from SAIC (2002)
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Minimal Work Flow Production of the Explicit
Ontology
Implicit Ontology
C response C response C response
T ground (B 4)
T ground (B 5)
T ground (B 3)
T ground (B 2)
T ground (B 1)
Ontology Lens
Schema-independent data
Schema-independent data is developed from the
Ontology Lens, in the form of a set of
syntagmatic units lt a, r , b gt Where a
and b are categories defined by the exemplar set,
, and r is a measure of relationship.
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Searching and Filtering Storing Analyzing
Entities Visualizing Links Clustering Categorizing
Resolving Cover Terms Matching Models /
Detecting Changes Simulation Generating
Hypotheses Generating Threat Scenarios Structured
Argumentation Learning Patterns Understanding
Intent Performing Risk Analysis Generating
Options Generating Plausible Futures Storytelling
Creating Explanations Alerting Visualizing GIS
Data Understanding Policies Preparing Video
Sources Processing Text Sources Processing
Sensors Processing Audio Sources Translating
Languages Identifying Humans Summarizing
Data Summarizing Text Searching and
Filtering Categorizing Indexing Visualizing
Summaries Collaboration Presenting
Recommendations Presenting Analysis
Results Presenting Situation Status Presenting
Options Building Teams Building Context
Synchronous AsynchronousCollaboration Tools
From the Intelligence Community and DoD
Lessons Learned
Visualization
To the Center for Disease Control and University
Research Centers
Analysis Tools and Educational Processes
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Producing Actionable Intelligence Iterative
Process Model - decomposition of
function/structure
V, S, R, K
UnderstandingPossible Outcomes
Human Action-Perception cycle
V, N, S, R, K
V, S, R, K
GeneratingOptions
Measurement and Instrumentation
V, S, R, K
Reporting
Persistent Ontology Services
V, D, N, S, R, K
Representation and Encoding
Alerting
S, R, K
Detecting Factsand Events
V visualization, D data mining, N natural
language processes, S support decision making,
R structuring of reasoning K knowledge
representation T technical support
V, D, N, S, R, K
Producing and MatchingModels
V, D, N, S, R, K
DiscoveringRelationships
V, D, N, S, R, K