The Knowledge Sharing Foundation


The knowledge sharing foundation concept was first developed (2003) as a suggestion supporting the US intelligence agency needs to develop information about event structure.  Previous to this, a small group of scientists have talked about the need for a curriculum, K – 12 and college, to support an advancement of cultural understanding of the complexity of natural science.  By natural sciences, we mean social and cognitive science in the context of human communication. 


The suggestion to support new intelligence technology deployments is predicated on the intelligence community’s responsible use and on the co-development of an open public understanding of the technologies employed. 


Ontologystream Inc has developed an (fairly complete) understanding of the types of text understanding technologies available within the intelligence community. 




Questions and Answers


September 16, 2003


Hyperlinks (click below)


Q-1: What is needed to support awareness of events in real time?

Q-2: What is needed to support community use of analytic tools?

Q-3: What are the benefits to Industry

Q-4: What are the Foundation Elements

Q-5: Examples of Innovation

Q-6: Why are educational processes important?

Q-7: How does the software compensation model work?

Q-8: How are test sets made available to the competitive communities?






Q-1: What is needed to support Intelligence awareness of events in real time?


A-1: The structure of data produced from measurement.


•      Real time web harvest of natural language discourse (Memetic measurement)

•      Global measurement of reports from medical professionals (Genetic measurement)

•      Measurement of social network relationships and dynamic boundaries of social systems

•      Cyber intrusion instrumentation and analysis (Cyber measurement)

•      Measurement of all NASA Earth Observation Data (see proposal to NASA)


A-2: New computer science based on stratified theory


•      With an aggregation of invariance in the structure of data.

•      With a separation of statistical and categorical artifacts in correspondence with human memory processes and human anticipatory responses

•      With the production of just in time machine-ontology formation as cognitive enhancement

•      With the development of event templates indicating meaningful constructs


A-3: Educational processes that allow users of an intelligence system to work within the limitations of machine and artificial intelligence.


•      University course credit

•      Professional Accreditation

Q-2: What is needed to support Intelligence community use of analytic tools?


A-1: A distributed collaborative framework


•      Tools expressed as un-encumbered capabilities

•      University certified educational support on all tools


A-2: Tool stability and tool interoperability


•      Separation of all module services from vendor control, with the appropriate payment for actual use of intellectual property

•      Open-results competitive testing of all modules, with modules expressed as open source software so that code can be seen and understood

•      Proposed use of CoreTalk Macromedia presentation [Mac] [Windows] and the Hilbert Engine ™


A-3: Community based compensation infrastructure


•      Commercial rights are protected with copyright and patents

•      Use-compensation based on software self-accounting to honor copyright and patents

•      Micro-transaction accounting and payment for services embedded in each software component. 


Q-3:  What are the benefits to Industry


A-1:   Establish coherence within the market space


A-2:   Advance the state of the art for information generation systems and open new markets


A-3:   Establish a new basis for innovation


A-4:   Intellectual Property mapping and patent evaluation will result in a reduction of uncertainty over ownership


Q-4: What are the Foundation Elements



A-1: Processes


•      Text Transformed into Structured Data

•      Unsupervised Pattern Mining

•      Supervised Categorization

•      Situational Logic Development

•      Logical Inference  (ΰ see current discussion about induction and deduction)

•      Procedure Learning

•      Event Detection from Data Invariance

•      Knowledge Flow Mapping

•      Social network and linguistic variation analysis



A-2: Subsystems


•      Single-algorithm Analytic Servers

•      Multiple User Domain

•      Ontology based Inference Engine



A-3: The Human Element


•      Knowledge Encoding and Propagation

•      Information Visualization

•      Cognitive Priming

•      Multi-modal interaction



A-4: Single-Algorithm Analytic Servers


•      Latent Semantic Technology

•      Self-Organizing Maps

•      Concept-Based Document Indexing

•      Context-Free Grammar Parsing

•      Clustering

•      Supervised Text Classification

•      Evolutionary Optimization

•      Associative memory and top down expectation using neural networks

•      Social network theory and analysis

Q-5: Examples of Innovation



A-1: Categorical Abstraction


•      Invariance in the data is used to construct situational logic

•      Continuum mathematics methods are used to derive an "implicit ontology" from a body of documents or other data sources

•      An "explicit ontology" is provided by human beings, e.g. in the form of categorized sentences, and then refined using iteration

•      Human feedback and inference rules are used to further refine & process the derived classifications


A-2: Event Chemistry


•      A technique for searching datasets for signs of real world events

•      Takes abstract atoms of invariance observed in data, and forms interesting combinations of them

•      Requires a Human-in-the-loop cognitive acuity to provide interpretation of meaning

•      Works naturally with the output of semi-supervised text classification, clustering and categorization methodology

•      Fits naturally with "chemical compound" metaphor, where a period table of atomic elements are discovered and used in event detection


A-3:  Referential Bases


•      Post relational database technology, using new types of algorithms

•      (type:value) pair data constructions encode localization of information without schema

•      (type:value) pair data construction organizational processes has well delineated correspondence to human memory and anticipation

•      Referential bases support stratified processing so the ontology constructions can be formative and situational


Q-6: Why are educational processes important?


A-1: The systemic development of educational processes involves


•      the development of consensus on what are the separated techniques in computational intelligence

•      The mapping of scholarly literature helps in comprehensive mapping of patent disclosure and copyright


A-2: As this consensus develops,


•      the description of general systems theory, cognitive and social science is made available within the academic community

•      a "liberal arts" education in the knowledge sciences is made available to intelligence analysts


Q-7: How does the software compensation model work?


A-1: Analytic features are to be replicated from existing software and implemented as separated components.


•      A mapping of all software based innovation in the area of computational intelligence is developed based on latent semantic technology indexing of patents and copyrights

•      In cases where the core technology has legitimate ownership, then licenses are arranged

•      In cases where the core technology is developed by the government then the core engines are made public domain

•      Each core technology component is rendered in binary with an internal accounting module that reports usage as part of a knowledge flow mapping and use compensation (when appropriate)


Q-8: How are test sets made available to the competitive communities?


A-1:   The system of core objects is open to innovation. 


•   Negotiations to acquire a new innovation occur through Intellectual Property mapping processes and comprehensive testing of object inherit capability


•   Innovations targeted for acquisitions are studied in highly structured usability testing that includes deep education in the innovations' inherit capabilities. 


•   These acquisition studies are conducted in the public view and are not governed by commercial processes.



Knowledge Sharing and SenseMaking diagram








Dr. Paul S. Prueitt


Research Professor

The George Washington University

Founder: Ontology Stream Inc

Director: BCNGroup.org