The Knowledge Sharing Foundation

Dr. Paul S. Prueitt, OntologyStream Inc

Research Professor, The George Washington University

Founder (1992) BCNGroup.org

Founder (2000) OntologyStream Inc

(Cell: 703-981-2676 --- portal@OntologyStream.com )

Copyright: Wednesday, March 12, 2003

Alerting

Reporting

GeneratingOptions

UnderstandingPossible Outcomes

Producing and MatchingModels

DiscoveringRelationships

Detecting Factsand Events

Measurement and Instrumentation

Producing Actionable Intelligence Iterative Process Model - Producing Actionable Intelligence Iterative Process Model - decomposition of function/structuredecomposition of function/structure

PersistentOntology ServicesRepresentation and

Encoding

V, N, S, R, K

V, D, N, S, R, K

V, D, N, S, R, K

V, D, N, S, R, K

V, D, N, S, R, K

V, S, R, K

V, S, R, K

V, S, R, K

S, R, K

Total Information Awareness program at DARPA developed a list of forty two types of functions that are involved in information production supporting the concept of Information Awareness.

OntologyStream used seven core functions as capabilities expressed within nine aspects of an Actionable Intelligence Process Model. A framework is thus produced with 7*9 = 63 primitives. Using this framework, OntologyStream defined a category structures that relates the forty two to the seven functions.

From the TIA list, each of forty two functions can be categorized as being related to one or more (usually one) of the seven functions in the AIPM.

Thus there is a first order decomposition of the nine aspects of the AIPM into the forty two functions on TIA’s list. The decomposition is not perfect, in that in almost every one of the nine aspects each of the functions may be seen. This distribution of function is fractal like in any natural expression of structure as function.

However, the AIPM is defensibly a model of the natural processes involved in community based information production from raw data.

Actionable Intelligence Process Model (AIPM)

Searching and FilteringStoring Analyzing EntitiesVisualizing LinksClusteringCategorizingResolving Cover TermsMatching Models / Detecting ChangesSimulationGenerating HypothesesGenerating Threat ScenariosStructured ArgumentationLearning PatternsUnderstanding IntentPerforming Risk AnalysisGenerating OptionsGenerating Plausible FuturesStorytellingCreating ExplanationsAlertingVisualizing GIS Data Understanding PoliciesPreparing Video SourcesProcessing Text SourcesProcessing SensorsProcessing Audio SourcesTranslating LanguagesIdentifying HumansSummarizing DataSummarizing TextSearching and FilteringCategorizingIndexingVisualizing SummariesCollaborationPresenting Recommendations Presenting Analysis ResultsPresenting Situation Status Presenting OptionsBuilding TeamsBuilding Context

Lessons Learned

Synchronous & AsynchronousCollaboration Tools

Visualization

Analysis Tools andEducational Processes

From the Intelligence Community and DoD

To the Center for Disease Control and to University Research Centers

First Knowledge Sharing Core

Intelligence vetting and knowledge sharing using taxonomy or ontology

For some time, human factors experts have made observations about a persistent mismatch between what the analyst knows is needed, from information technology, and what is delivered.

The bar is high, since what is needed is a type of natural intelligence that has almost infinite memory and analytic capability. This type of capability requires both perception and cognition.

The supplement to cognition will come from taxonomy and ontology, and deductive reasoning based on these constructs. The supplement to perception will come from inductive transforms and instrumentation based on rules constructs.

Rule formation is an essential part to perceptional measurement, and from perceptual measurement one may ground deductive reasoning based on ontology. Community based rule formation is a critical aspect to community based intelligence vetting and knowledge sharing.

But before we can have artificial cognition and perception, there are in fact many issues that have to be resolved, such as ontology merge and scope issues.

Viewpoint is not manageable in the same flexible fashion as is experienced in the human mind. Ontology and taxonomy manipulation is thus not the same as the experience of thinking.

Thus when the issue of taxonomy and ontology is introduced into intelligence vetting, as it is in the AIPM, one needs to carefully study how humans use tools designed to build and use these resources.

Compensation for use of data, tools, educational services, and work product

RepositoryEducation Vendor ToolsData

Embedded micro-transaction accounting system supporting outcome metrics and revenue generation

Important Innovation: IP protected micro-transaction accounting system available from Dr. Brad Cox Diagram from Prueitt (2002)

Polling instrumentation based on AIPM targeted at understanding the frustrations of intelligence analysis

Vulnerabilities and threats correspond. Likewise, polling results can be compared to an objective evaluation of software features.

The AIPM was developed from a partial model by adding the two aspects,

{ { measurement, instrumentation }, { representation, encoding } }

These two aspects reflect core parts of the problem of perception. The elements of cognition and perception are “within each other”, and this is an issue that we must deal with.

So each aspect of the AIPM has both perceptual and cognitive facets.

Perhaps the descriptive enumeration of the significant aspects of actionable intelligence is complete and correct with this addition.

If this is not the case, the model can be changed. However, whether perfect or simply sufficient; a model of this type can be used to enumerate polling/survey items.

We have not attempted this poll, as yet.

Socio-metrics is a bit more involved that one might at first expect. Even in a polling methodology, one finds that questions that a liberal education about the issues is relevant.

Core Engines&

Educational Services

Repository

Education

Vendor Tools

Data Real time analysis

Industry

Universities

Testing, evaluating and procurement of technologies and methodologies for automated intelligence information production

With the evolutionary characteristics of the Knowledge Sharing Foundation concept, it is possible to realize a direct relationship between science based innovation and collective intelligence as expressed in education, business, government processes.

The Knowledge Sharing Foundation is an open source environment where all software code is available for public inspection. Compensation to patent and intellectual property owners is enforced using patent and copyright law. Compensation is accounted through the use of a micro-transaction instrumentation within each element of the tool set and the educational processes.

The evaluative process is moved from a current (often only) paper based evaluation that is out-of-context and based on marketing materials, to an in-context use model based on actual acceptance within the market place.

SituationalOntology

Knowledge Workers

Education, Business, Government ProcessesKnowledge Sharing Foundation

Science of Knowledge Systems

A willing coalition of companies and a university proposes the development of a Knowledge Sharing Core

The principle of descriptive enumeration is claimed to have the ability to specify sufficient elements so that as a whole the set of these elements has specific capability.

The First Knowledge Sharing Core is to be developed through the contractual performance of seven corporations .

A University will provide distance learning on the functions and capabilities of the tools that are made available by these corporations within the First Core.

The micro-transaction accounting system is proprietary and essential, and provides a level of protection to companies who choose to expose their intellectual property through disclosure and inclusion into a Knowledge Core.

OntologyStream Inc itself hold some essential IP as required to produce a Knowledge Core. However, the success of OntologyStream is designed to trigger competition between OntologyStream and third parties.

In this way, we wish to accelerate the development of a marketplace for knowledge technologies.

Transaction Layer

Presentation Layer

Knowledge (concepts) existing in the minds of Humans

CCM

LegendCCM = Continuous Connection Model

NLP++ = Natural Language Processing Language

DOF = Differential Ontology Framework

I-RIB = In-memory Referential Information Base

Parsing, tagging, routing, categorizing, clustering

DOF

Ontology LensSchema

Logics

Topic Maps

Gap between Human private experience and the Computer

Diagram from Prueitt (2002)

NLP++

RAM memory

Lines System files

I-RIBs

A funding instrument is now offered to government agencies and/or to venture capital

The funding instrument includes a Objective Plan and a Business Plan. The Objectives Plan sets out the elements of the First Knowledge Sharing Core, with a focus on Biodefense Information Awareness (BIA). The Business Plan capitalized a corporate entity called The OntologyStream Inc.

OntologyStream Inc has authorized 300,000 shares of stock, at $4 per share, and reserved 700,000 shares for future capitalization, founders and initial intellectual property.

The First Knowledge Sharing Core is focused on BIA. Professional contacts and agreements with corporations have been formalized. A budget of 400K has been established to manage OntologyStream for 6 months and build the BIA distributed environment, tools that perform each of the nine aspects of the AIPM, and develop initial medical and ICD code taxonomies.

Doctors, Clinics, Hospitals

ICD codes

CDC

Medical Insurance Providers

Medical ResearchBioinformatics

Ontology

Public PolicySyndromic Surveillance

Conceptual Indexing of Research Literatures

Privacy and security control