Domain Independent Ontology Based Intelligence Vetting using Multiple Virtual Private Networks and Ontology Visualizati

1. Domain IndependentOntology Based Intelligence Vettingusing Multiple Virtual Private Networks and Ontology Visualization Dr. Paul Prueitt 1/29/2003

2. Producing Actionable Intelligence Iterative Process Model UnderstandingPossible Outcomes Human: Action-Perception cycle Measurement and Instrumentation GeneratingOptions Reporting Persistent Ontology Services Representation and Encoding Alerting Detecting Factsand Events Producing and MatchingModels DiscoveringRelationships Object Sciences Corporation 12/5/2002

3. Enterprise Middleware HeterogeneousDatabases Transaction Components Data Schemas, Ontologies Producing Actionable Intelligence Technology Support Analysis Tools and Educational Processes Visualization Synchronous & AsynchronousCollaboration Tools Presentation to user Object Sciences Corporation 12/5/2002

4. Simple Reusable Architecture Network of Virtual Private Networks Data Email and Packet Transfer HTTP (SOAP,XML) Collaboration Visualization Applications • Knowledge Sharing • Foundation Connecter Architecture Persistence Education Object Sciences Corporation 12/5/2002

5. Operational Architecture for Ontology Production Data Source Schema-dependent data Schema-independent data Categorizer and Inference Engines Repository Object Sciences Corporation 12/5/2002

6. 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 Important Innovation: Knowledge Sharing Foundation from The George Washington University Diagram from Prueitt (2003)

7. 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

8. Advanced data mining and natural language processing Making the case that new capabilities are within reach

9. 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 { < a , r, b > }, 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

10. 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, Cground Exemplar text collection, Cexemplar The figure-ground then measures the real time flow of response passages

11. Measurement of Response I We have identified three collections of semi-structured data (natural language) Ground text collection, Cground , Response text collection, Cresponse , Exemplar text collection, Cexemplar 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 minto 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.

12. Measurement of ResponseII 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 eC 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 R^300 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.

13. Production of Concept Metrics Implicit Ontology T ground (B 1) T ground (B 2) T ground (B 3) Explicit Ontology de Cresponse T ground (B 5) T ground (B 4) 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

14. 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

15. 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

16. 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

17. 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

18. Situational Logic Construction A latent technology transform, T ground , is used to produce simple metrics on membership of documents from the response collectionCresponse 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

19. 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)

20. Minimal Work Flow Production of the Explicit Ontology Cresponse= Cresponse= Cresponse Implicit Ontology 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 { < a, r , b > } Where a and b are categories defined by the exemplar set, , and r is a measure of relationship.

21. 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

22. 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 Measurement and Instrumentation GeneratingOptions V, S, R, K Reporting V, D, N, S, R, K Persistent Ontology Services 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