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COMPANY

Who are Knowledge Foundations clients?

Knowledge Foundations target clients are industry partners (knowledge engineering companies), VARS, OEMs, application developers, government contractors, integraters, universities and corporations. The Company completed its Mark 1 and Mark 2 R&D and advanced engineering with government teaming partners, completing projects for NASA, DoD, Office of the White House, U.S. Air Force, U.S. Navy, U.S. Army, U.S. Marines, publishing companies and others. Projects completed for these clients centered on rapid decision support for complex domains (Knowledge Superiority), virtual database/simulation integration with subject domain knowledge assets, job knowledge transfers, professional to para-professional expertise transfers and organizaional knowledge permanace.

What kinds of major projects were completed with your tools?

• NASA Lunar-Mars Demonstration Knowledgebase to trace the economic impact of space exploration on US business.
• TeCap Test & Evaluation Capabilities Knowledgebase - Decision support tool for base closings & management of $1 trillion in range facilities. Provided machine reasoned trade-off analysis, decision support rationale and cost/impact analysis.
• USAF Special Operations Drug Interdiction Roadmap Knowledgebase SBIR - Analysis and redesign of technology roadmaps for updating aircraft fleet to future requirements.
• Air Combat Vision Science & Technology Knowledgebase Assessment - Knowledge gap analysis of government documents as source for 30 year technology roadmap. Provided model for restoring lost knowledge previous stored in the heads of scientists, engineers and others at the U.S. Science and Technology laboratories prior to downsizing.
• FastPlan Decision Impact Assessment Tool - Decision support tool to manage $75 billion fleet procurement budget & future requirements. Determining the impact of budget changes within less than one hour through machine reasoning.
• Joint Advanced Strike Technology (JAST) Requirements Knowledgebase - Captured the requirements & competitive design issues for next-generation aircraft. Provided integrated database and proposal simulations used to determine project awards.
• US Navy Acquisition Center of Excellence Program Management Knowledgebase - Design of knowledgebase & business model to demonstrate how to reduce large procurement projects from 14 years to 7 years to meet challenge set forth by Congress & DoD.
• Space-based Surveillance & Tracking System Laser Discrimination Expert System - Adaptive system to recognize ballastic threats & devise methods to identify lethal targets and intercept them.
• SDI Battle Management Requirements Analysis Tool (BRAT) - Virtual knowledgebase to capture & simulate battle management against full nuclear attack.
• Universal Threat Simulation System (UTSS) - Test knowledgebase technology as the means for very large scale database integration. Proved interoperability capacity. Machine reasoning with database contents and output analysis.
• Avionics Prototyping Tool (APT) - Virtual Database & Flight Simulation SBIR Phase II. Built & tested virtual database using knowledge technology to integrate modular simulations. Automates database systems integration in sustained operational traning environment. Product used to train Lantrin pilots for the Afghan war.
• NASA Astronaut Aircraft Operations & Maintenance - Complete Knowledgebase for Flight Operations Maintenance Orders, and Aircraft Subsystems History for complete diagnostics & knowledge for aircraft readiness.
  

KNOWLEDGE

What is knowledge?
Knowledge, the state of knowing anything that decreases uncertainty, represented by the formula: Knowledge = theory + information. This state of knowingness results when theory (the conditional reasoning power we learn from enculturation, education, life experience and deep analytical thought), interacts with information (the who, what, when, where and how much facts) of situations and circumstances.

Theory represents 85% of knowledge content and answers how, why and what-if questions. Theory provides us with the understanding of how the concepts of the world work together. Theory defines the relationships between the concepts in our mind, and the meaning of those concepts based upon their relationships. Theory is a`priori, and exists in our minds before events and situations occur, providing us with the meaning of events, situations and circumstances as we learn the facts embodied within those experiences.

Information, on the other hand, represents about 15% of knowledge content. Conventional applications and databases can only capture information (who, what, when, where & how much facts). They leave 85% of knowledge content on the table, which until now, people have had to provide.

So what? Why should we care?
Over the decades, people have interacted with the computers by creating, storing, transmitting and outputting data (characters, symbols, words, objects and documents) as a means of enhance efficiency and performance. This relationship has been particularly symbotic since people have provided the theory in their brains to make sense of the data stored within the machine environment.

Now, with the advent of the internet and enterprise networks, both people and machines are overwhelmed with the glut of the very data and documents being created. The machine environment is plagued throughout with complexity, redundancy, scalability and interoperability problems, and people are unable to find the exact knowledge they need to perform their jobs. For this reason, inefficiency, reduced performance and cost has become a major problem.

The solution to this problem is simple. Transfer human knowledge to the machine environment so the decisions that people have been making on behalf of the machine, are now made by the machine. This is exactly the capacity that theory-based semantics provides machines, only with several magnitudes of power greater than that of people. people can only hold 3 to 7+ competing ideas in their minds at any one time while reasoning through a problem. With theory-based semantics, a machine can reason with 10s, 100s and 1000s of competing ideas. For this reason it is clear that the Knowledge Age has dawned, and the Information Age is slipping into the past.

What is the economic/business value of knowledge?
Knowledge decreases uncertainty. To the degree that uncertainity is removed, the costs of "knowledge deficit drag" are decreased while operationing efficiency, performance, production, profitability and sustained competitive advantage are increased. In cases where the scope of business operations and projects are so large and complex that managers cannot "get their arms around them," theory-based semantic systems provide extraordinary advantage.

Likewise, since theory is a'priori, predictive intelligence is embedded within every concept, idea and thought pattern. This is important, because enterprise processes that have relied upon human intervention to make process choices, can now be reasoned through by machines to make the same trade-off decisions with equal or greater efficiency.

On a larger scale, conventional enterprise systems create rigid "silo" structures that institutionalize knowledge drag by systematically restrict the integration of cross-functional, cross-departmental knowledge. The cost impact ranges from misaligned IT-Business objectives, poor performance, lost opportunity and weakened competitiveness.

Theory-based semantics solves this problem thru ithe company's Mark 3 knowledge platform which has the capacity to acquire, store and intelligently apply every cross-functional, cross-departmental concept and process with the strategic constraints of company, department and unit values, objectives, missions.

KNOWLEDGE ENGINEERING

What is Knowledge Engineering all about in a nutshell?
Knowledge engineering is the methodology used to define, model and ultimately capture every form of knowledge using Knowledge Foundations' theory-based, n-dimensional semantic tools. Primary Knowledge Engineering steps includes:

• A 1/2 day focus group session with project managers and other key personnel to determine the ten most important questions that the knowledgebase must be able to answer. These ten questions ususlly become the top 20 or 30 questions.
  
  
• The sources for the domain knowledge project are determined. They typically include engineering, operations, policy, specification and other kinds of manuals and documents, but also other sources that are required for product density.
  
  
• A project team modeler defines and models the concepts, ideas and thought patterns inherent in the project documents and works with an Outliner who organizes these elements into a concept outline. Concept models are broken down into three categories:
  • Metaphysical concepts or ideas. (The abstract concept model of a category).
  • Data Form concept (a middle ground expression of the metaphysical concept and physical model), such as a picture or image of a car, airplane or tree captured in a book, manual, on film or otherwise.
  • Physical model is the actual physical "instance" itself, such as the actual car, book, manual, airplane, tree, seat, flower, etc.
    
    
• Once the model outline has been completed, Editors review for language self-consistency, subject/content accuracy and completeness. Upon completion, the source outlines are ready for integration.
  
  
• The primary source content model outlines are fed into the builder to construct the primary "backbone," or ontologies. As these outlines are fed into the Builder, they automatically self-organize into an n-dimensional web of concept and conditional relationship paths, saved on a hard drive or memory board. The primary stage of the theory-based semantic web is then tested. Thereafter, additional layers of concepts, ideas and thought patterns are layered onto the structure until every question the knowledgebase was intented to answer, can be accurately answered - no matter how complex.
  
  
• As new layers of concept models are introduced to the knowledgeasset, the builder self-organizes and self-connects all relevant concept relationships, and actually self-transcends its own structures, just as the human brain does as new theories are learned and remembered through enculturation, education and experience.
  
  
• Captured knowledge is an asset. Once captured knowledgeassets can be copied to CDs, DVDs or other media for distribution. A special K-Browser is used to query the knowledgebase and navigate along its reasoning paths.

KNOWLEDGE SCIENCE

What is Knowledge Science?
The Company's theory-based semantic technologies are based on a revolutionary knowledge science that provides an empirical foundation for understanding exactly what knowledge is, how knowledge is acquired, and how it is used by both people and machines. This is significant because scientists and technologists the world over can validate knowledge science with projects of personal or professional interest, giving them the opportunity to substantiate its validity to the scientific, technical and business communities.

Knowledge Science includes:

• A Physical Theory of Knowledge and Computation - to be published in a scientific journal shortly, demonstrates how theory and information combine to function successfully to permit reasoning, learning, and adaptive behaviors by humans and machines. This theory is an extension of Claude Shannon's Theory of Information, and provides the basis of understanding for creating software and chips that operate at absolute bit limits, and where computation is conserved.
  
  
• Knowledge = theory + Information formula for what knowledge is as explained above.
  
  
• Rational Practicality - Philosophy of Willard Quine that states that ideal rationality is "acting with maximum efficiency to achieve goals with complete prior knowledge of their impact and consequences."
  
  
• Knowledge Engineering Methodologies - Knowledge Engineering team process followed to capture every form of knowledge (physical and metaphysical). Brief description follows. Detailed white paper: Knowledge Asset Process
  
  
• N-dimensional, theory-based semantic architecture - Revolutionary single coding system capable of capturing all knowledge. Self-building, self-organizing, self-transcending. Solves conventional technology problems of interoperability, complexity, scalability and computational redundancy.
  
  

What does it mean that theory-based semantic systems are language independent?
Concepts, ideas and thought patterns are distinct from language. Concepts and ideas are the metaphysical and physical understanding that pop in our minds to make sense of our world. Their meaning is defined by the theories we learned through enculturation, education and life experience. Language, on the other hand, is completely separated from the precise world of meaning. Language is an aribitrarily agreed upon system of communication invented by humans as a medium to hhelp express the concepts and ideas in our minds, but becuase words have no immediate conncection to theory, they are meaningless and ambiguous. Language drifts across human society serving the social, artistic, scientific and political interests of its users, but it remains ambiguous because it cannot precisely and easily, express the conditonal constrains of theory that give concepts and ideas their meaning.

KFI's theory-based semantic systems are concept centered, not language centered. Wherever information constrains choices in a theory-based semantic web, we witness the declarative equivalent of "execution" and the biological equivalent of "thought." Both are exactly comparable to physical events. This means that machines can use and understand the same concepts and ideas a people.

THEORY-BASED SEMANTIC OPERATING SYSTEMS AND TOOLS

How are Knowledge Foundations' tools different from RDF/OWL and other "intelligent" systems and application tools?
Schema based applications, at their core, represent an attempt to stuff human intelligence (knowledge) and all its forms, into small rigid boxes. Human nature is intricately interconnected with both metaphysical and physical dimensions that show-up in every human endeavor. If there is 100% of knowledge on the table, databases can capture 15%, RDF/OWL may be able to capture another 15%, leaving 70% on the table. We believe within the next few years the semantic web people will come to realize this and know that the vision of the semantic web, as worthy as it is, simply may not have the legs to fulfill the vision. The 70% failure rate of CRM software reflects this reality perfectly.

Database-based technologies, to include triples are going to “hit-the-wall” in terms of their capacity to handle very complex situations (let alone impossibly complex situations) - especially those that include complex human behaviors like "attitudes." Binary and triple structures are two dimensional, life is three dimensional – or more. We believe that cascading 14 layers of two-dimensional structures to define a situation or circumstance, still equals two dimensions. We know there are developers out there who know this, but until they have made the leap from logic and schema based structure to theory-based semantics, you will not hear of them.

The semantic web vendor technologies are also language-based, not theory-based. These technologies rely on language and grammar to construct taxonomies and ontologies. To us, this is part of the problem, not the solution? Languages are arbitrarily agreed upon systems of communication. Words have no meaning other than what we as a culture give them (connotation), or as individuals understand them (denotation). For this reason they are extraordinarily ambiguous – the word love for example, has as many meanings as there are people. Even if the semantic web worked for some words, it fails completely when expected to capture metaphisical distinctions of morality, ethics, goodness and so forth. How can these forms answer such questions as: "What visions, roadmaps, and world events detail the nature or potential for future change?" Or, "Which strategies, conops, roadmaps, visions, notional architectures, or mission area deficiencies define new design or operational requirements?" These are the type of complex questions theory-based semantics answer with great precision.

The goal is to develop machines with the capacity to think exactly like humans. The human DNA has not changed for 100s of 1000s of years. They only thing that has changed is the number of theories in our heads, passed down from one generation to the next. What do schools and universities teach – theory. What do parents teach their children – theory. What do people learn on-the-job? Theory. All human endeavor is theory based. For machines to become equal partners with humans, they must have the capacity to capture theory and to reason with that theory. Knowledge Foundations is the only company in the world that has a technology with this capacity.

The Semantic Web (term used to describe RDF/OWL network-centric application), has no science. From our perspective, it represents a development trend rather than a new technology perse. Knowledge Foundations has a Quantitative Theory of Knowledge, a definition of Knowledge (which the entire Knowledge Management industry has not defined since its inception), and a proven Knowledge Engineering Methodology that almost anyone who is conceptual, research oriented and skilled in modeling, can perform. And, a technology that operates at absolute bit limits and can capture and reason with any form of human knowledge. This is a recipe for rapid global adoption.

Theory-based semantics is ready for prime time now. These tools can be very cost effectively integrated into the backend (systems interacting with systems) providing interoperability with databases and simulations, while also serving as a repository for organizational knowledge to include financial, accounting, marketing, sales, service, legal and administrative. As a front-end tool, it can be embedded into application software such as Customer Relations Management, Customer Service Management, Supply Chain Management, organizational Value Chain Management to name a few. All knowledge can be captured using Knowledge Foundations' tools. People interact with the K-Browser, gaining knowledge from a knowledgebase at the speed of thought. Machines query a knowledgebase to achieve unimagined operational efficiency, and spiders crawl over massive knowledgebases to retrieve precise organizational or domain specific knowledge to make most strategic, mission critical trade-off, legally defensible or best practice answers.

How are theory-based semantic tools different from conventional technologies?

Architecture (Computation versus Declarative)
Conventional technologies and theory-based semantics offers two distinct architectural models. Traditional machines and software rely on execution, logic, algorithmic processing, mathematical true/false proofs and structured schemas that require the same data be processed every time an outcome is required. The conventional model provides no means for machines to learn from past computation, requiring greater performance as the demands from growth increases complexity.

The limitations of this conventional model are typical of the database/computer relationship. Human architects create data structures called database tables that must be programmed before the database can be populated with desired content. Most of this content, such as street, city, state and zip code is duplicated and stored many times over as new customer content is entered into the database. Likewise, as users execute commands to view desired content, the computer processes the same data using the same algorithims - every time. It does not take much thinking to realize how these layers of built-in redundancy can slow a system down - expecially when thinking about huges volumes of data generated by organizations and the internet. The standard solution for this limitation has been to build faster computer chips, but the physical limits of nature have stalled this trend.

Declarative semantic architectures offer a completely new "capture once, use forever" computing model that eliminates the need for logic, true/false proofs, in-advance schema construction and computational redundancy. A theory-based semantic web is not built from design, but rather emerges as a self-organizing web structure from its own self-assembly process. Semantic structures do not require outside human architects (though Knowledge Engineers do assemble content sources), and they grow to unlimited size guided by absolute bit limits of Shannon's Information Theory and Ballard's Quantitative Theory of Knowledge. These semantic structures, or knowledgebases, are pre-computed webs of linked concepts and reasoning paths justified by theory, and need only the addition of information describing a situation or circumstance to fully represent precise knowledge of that situation or circumstance understandable by both humans and machines. The semantic codes within the n-dimensional form contain only one copy of every concept or idea required by the knowledgebase, and are found without search by their own unique identifier.

Complexity (Legacy of early computing models)
The key reason for complexity is that today's computing methods evolved from the early 1940's when simple theories, hardware and computing methods were designed as work-arounds for very expensive memory and storage resources. It made economic sense to continually recompute rather then store sizable volumes of data. Today, the requirements and economics of that early model have completely reversed. Theory and data requirements are now extremely complex and memory and storage costs are low. Yet, the conventions of repeat computation and structured data schemas have continued to the point where technology is bumping-up against physical and complexity limits that were unthinkable in the 40's.

Complexity is experienced as machine and software interoperability, excessive data accumulation and the resulting problem of search. Even chip development, the savior of complexity, has reached the 4.33 GHz limit (though there is talk of transistors that operate at 604GHz?).

The semantic solution presents a revolutionary paradigm of thought and invention. N-dimensional semantics captures all knowledge in the declarative form, which is used by both humans and machines at absolute bit limits, solving the complexity problem while allowing commerce unlimited capacity to build massive stores of business knowledge.

The success of the general theory of knowledge (Knowledge = Information + Theory), establishes that information based situation awareness (the facts contained in databases) is a small part of our computing needs. The greater amount of knowledge, perhaps over 85%, is theory. Semantic technologies contain natural limits and efficiency measures that guarantees the absolute smallest bit limits on storage and execution, and the least cost in every situation. These physical limits insure there is no system that can be faster or more efficient.

Scalability (Structure versus machine behavior)
Scalability to conventional technology means the ability to add more processors, more memory, more algorithms, more machines, more users or more layers of software to solve the next problem. At a deeper level, the fundamental belief behind computer algorithms and database schemas requiring logic, mathematics, language and grammars, automatically rules out machine learning and the ultimate capacity of machines to change their own behavior - a fundamental characteristic of true scalability.

Knowledge Foundations' emerging knowledge science establishes that both execution and storage should be proportional to information content, so both information and theory storage remain scalable to unlimited size. Self-organizing semantic webs in contrast to conventional technology, automatically reorganize and scale to meet the demands of newly introduced content. In providing the theories used now by knowledge science, and in demonstrating them in small to massive projects, the Company has achieved ultimate limit scalability. Research is continuing to create a technology that will allow semantic webs to learn and change their own behaviors from direct experience.

Systems Lifecycle Costs (Development)
When organizations make strategic changes to operations such as adding new divisions, products, services or expanding into new locations, there is most always a need to purchase additional hardware and software. The cost of these software acquisitions include purchase, development & integration, training, maintenance, losses from down-time and lost opportunity due to interruptions during the normal course of business. Though today's machines and software are less costly than a decade ago, development costs for backend integration are 15 times the cost of the software license itself, or higher, and takes several months, or years to completely implement. Enterprise software programs that cost $100,000, cost an additional $1,500,000 to develop and integrate.

Declarative semantics presents a unique strategic purchase option. As an all knowledge system (theory & information), theory-based semantics cost-effectively integrates backend "systems-to-systems" without machine interoperability problems. Development time and costs are less and interoperability more efficient. Of equal or greater value, is that knowledge engineers can effectively capture existing systems data and document content, plus the actual job knowledge of employees, consultants and subject experts. Owning knowledge gives organizations previously unrealized use of vital knowledge to solve operational problems such as "the right hand knowing what the left hand is doing," the ability to track budgetary expenditures through the organization hierarchy with value/expenditure assessments per expenditure, or provide employees machine delivered, immersive, on-the-job training.

Organizations gain even greater value because captured job theory can be stored and used for decades, centuries, even thousands of years. For this reason, KnowledgeAssets thar are owned by an organization can be bartered, licensed or sold to a global market. Installed platforms emerge as continuous knowledge commerce where knowledge environments stays standard for decades and centuries and where content becomes more valuable than the system or the technology itself.

Systems Lifecycle Costs (Maintenance and change configuration)
Work environments are not static. They require on-going change to accommodate management objectives, evolving markets, technology trends and organizational growth. Structured architectures, computational processing and database schemas, once developed and configured, are costly to change because structural changes often cause daisy-chained malfunction errors within machines and software, or human error due machine forced procedural changes.

The semantic answer to this costly problem is that change configuration is an integral part of a semantic system. The theory-based semantic architecture is self-organizing, self-adjusting and self-transcending. It is a "high granularity environment" where every concept is defined by its relationships to other concepts and ideas, and not by a schema, naming convention or linguistic identifiers. In a theory-based semantic web, every idea appears just once and every other relationship attaches to it in a massive n-dimensional space.

System Lifecycle Costs (Efficiency, performance and profitability)
The real cost of conventional technology is the drag it imposes on operations. It is estimated that companies lose as much as 25% or more of employee work-time while they search for document sources and content required for their work. Since this time could be dedicated toward customer retention efforts, lost time also represents lost opportunity. When employees, temporaries and consultants cannot find the answers they need to perform their jobs on-the-fly when they need it most, efficiency, effectiveness and performance suffers. The cost, as revealed by a Fortune 100 Human Resource Professional, is both economic and psychological.

N-dimensional, theory-based semantic design is extraordinarily powerful, yet people friendly, because it works the way people naturally think. Because there is no search, but a concept query and navigate function using the K-Browser, people find precisely what they are looking for at the speed of thought. Sales people who need to see all the documents related to a customer, or more precisely, specific content within those documents related to contract provisions, can find them within seconds and minutes. Likewise, an HR person can know exactly the job responsibilities, performance rating, supervisor, pay rate and testing scores of an employee at anytime during the employees employment. Through Knowledge Engineering, every form of knowledge can be captured with proven veracity time past, present and yes, as a predictive tool, the future.

© Knowledge Foundations, Inc. 2004- 2006