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THE KNOWLEDGE INDUSTRY:
Knowledge Science, the underlying force behind the Knowledge Industry, represents a paradigm shift away from almost every form of conventional thinking related to organizational strategy, information management and technology.

Knowledge is what distinguishes one organization from another and provides organizations their ability to survive within the marketplace. For this reason, in the future, Knowledge Age organizations will be required to capture their knowledge both as a means of survival and as a demand by stakeholders who recognize knowledge as the core asset of a company.

Likewise, most all companies will have a Chief Knowledge Officer with skilled Knowledge Engineers in every division and department. The stakes are high. As the global economy becomes more competitive, corporations and governments will be forced to embrace the “knowledge power curve” reality.

Entering the Knowledge Age - Exiting the Information Age.
Information Age technology is based on the mindset that expects commands be executed and data compiled before a process or data output is produced. Algorithms, a good example of this thinking, processes data along one path, in one direction, in order to produce a desired output. When that same content output is required, the command/compile/process has to run again. Declarative semantics requires that data be processed only once. Thereafter, the output has unlimited availability without repeat computation.

The process redundancy issue also applies to database architectures. Conventional database schemas require duplications of table/index/editor/report functionality for each database concept model, defined by what are called tables. Database tables are the containers for information content (the who, what, when, where and how much facts) related to customer, employee, supplier, purchase order, G/L, A/P, A/R and so forth. These tables are then linked to provide relationship connections. A small to average database may contain between 10 and 25 tables, costing less then $300 for an off-the-shelf product up to hundreds of thousands of dollars for a custom database. The very largest databases may have 250 tables or more, costing millions of dollars to develop and maintain.

Knowledge Age technology is based on a declarative semantic mindset as defined by Dr. Ballard, which he and others have development over the last two decades. Conventional technologies still play their part in the Knowledge Age, but declarative (non-processing), semantic technologies far exceed Information Age technologies because they have the capacity to capture millions of concept models and their conditional relationships. For this reason, highly defined, complex simulations of organizations, departments, projects or job functions can be developed that will allow users to answer their most complex how, why and what if questions.

Imagine a spherical jungle jim with a complex matrix of 10s of 1000s of interrelated connections that can be pathed forward (how), backward (why), or in any direction (what if). This is the reality of the Knowledge Age.

Distinctions between the Knowledge Age and the Information Age:

Knowledge Science is based on natural law (quantum mechanics), and represents reality, which includes the uncertainties of nature. Information Age thinking is based on mathematics and especially first order logic, which is man made and has little to do with the reality of natural law. For this reason, there is no evolutionary path out of the closed logic, language and technologies that are derived from current day mathematics. Knowledge Science is 15 or more years beyond OWL, RDF, XML and descriptive/linguistic representations of “context” or “roles”. As form variations derived out of mathematics, these languages and technologies cannot offer exponential complexity scaling like declarative semantic technologies.

The Knowledge Age is about How, Why, and What if answers because conditional meaning content can be captured, representing 85% of all knowledge content. The Information Age is about Who, What, When, Where, and How much representing only the facts, or 15% of knowledge content.

Currently, because Information Age technologies have limited capacity to capture "conditional content", by default, employees, consultants, vendors, customers and others own the knowledge. Without these individuals and sources of knowledge, it would not exist. In fact, even when there is a work force in existence, vital knowledge can be lost forever as determined in the NASA project study conducted by KFI in the 90's. In the Knowledge Age, organizations own their own knowledge.

Knowledge Age decision tools provide declarative “most logical,” “strategic,” “legally defensible” or “best practice” answers within seconds. In the Information Age decisions may take hours, days, weeks or months to research before answers can be determined. Once concluded, these decision outcomes may then be erroneous due to incomplete or out-dated knowledge resources, inexperienced interpretation or ideological bias.

When presented with decision options from KFI's tools, every user (novice, expert, or machine) will also see all the predictions of modeled theory, i.e. HOW might they achieve their objective; WHY do they need “this or that”; PREDICTION of who and what will be affected by there decision vs. some other choice; CONSEQUENCE to what extent will their goals be realized, and what unexpected consequences might also befall them and others. Contrary to the power of these "wisdom" capabilies, Information Age technologies strip most meaning content away so that what remains is raw information, which requires that people apply their own knowledge to make the information meaningful.

For this reason, Knowledge Age technologies help optimize human talents and job skills by providing relevant, user friendly, “how to,” “best use,” and “cross-functional” answers to train and assist users in their work. Information Age technologies limit answers to information or documents, which then require the user to study, define and analyze the content themselves.

Knowledge Science Vs the Practice of Knowledge Management

Over the last several years, companies have embraced the importance of organizational knowledge. Riding on the casual use of the term “knowledge” and its marketing sizzle, companies have defined knowledge as their policies and procedures, employee, supplier and affiliate contact yellow pages, operation manuals, and a set of FAQ’s which some companies are calling a “Knowledgebase.”

Likewise, following along in this stream of thinking, companies have created new positions such as Chief Knowledge Officer, disseminated organizational resources through server and web publishing technologies, and made attempts to capture employee knowledge by video taping meetings and strategy sessions. These efforts, as valuable as they may be, only represent the fulfillment of “Best Practices,” and in fact, have little to do with content based knowledge acquisition.

Declarative knowledge assets contain (all or in part) the knowingness of employee, consultant, customer, vendor, affiliate and subject experts. Plus the cross-functional discipline perspectives of management, finance, law, human resource, engineering, marketing, sales, service and other disciplines of thought and experience. Also included are relevant constraints such as laws, values, aesthetics, policies, politics, etc., which are required to produce comprehensive, fully reasoned answers to complex questions. This is the power of declarative knowledge.

Knowledge Engineering

Knowledge Foundations provides the answer to Information Age pains. The company is spearheading the development of the Knowledge Industry through its declarative semantic tools and knowledge engineering methodologies.

The company is currently providing support to corporations, engineering, consulting and software integration companies who wish to “seize the moment” and become part of the Knowledge Age solution as Knowledge Engineers within corporations or Knowledge Engineering service companies.

Beginning in the fall of 2004, the University of California, Irvine, and Knowledge Foundations through an affiliate, is providing a course in Knowledge Engineering taught by Dr. Ballard. Upon completion, graduates will become available as Knowledge Engineering consultants or employees. (review course outline)

The Knowledge Age and Global Impact Through the 21st Century
KFI envisions the future of the Knowledge Industry to be as follows:

1985 – 2004

• Development of Mark 2 stand-alone declarative semantic technologies.
• Completion of 50 knowledge engineering test projects of national importance.
• Establishment of Knowledge Foundations as a full service tool company.
• First Knowledge Engineering courses offered through UC Irvine extension course and San Diego affiliate. (review course outline)
  
  

2005 – 2008

• Release of Mark 3, Beta and Release 1, server based declarative semantic system tools.
• The world at large begans shift from an Information mentality to a Knowledge mentality.
• Development of Knowledge Industry to include National and State Knowledge Associations.
• KFI teaming with corporate managers, consulting companies, integrators and others to support the knowledge engineering projects.
• Push by organizations to acquire knowledge permanence.
• Organizations become more efficient, profitable and powerful.
• Governments began to provide better governance and service.
• Countries and large publishers compete for Knowledge Dominance.
• Entrepreneurs catch the vision and form companies that specialize in knowledge capture to take advantage of the expected trillion dollar knowledge industry.
• Managers and sales executive carry a “Knowledge Buddy” with them.
• Professionals from every industry learn how to leverage their knowledge and skills to become more efficient and skillful.
  
  

2008 – 2015

• Machines have the ability to conceive of ideas, create theories and reason the way people do.
• Machines become co-creative partners with people to provide service, entertainment, companionship and a better way of life.
• Knowledge engineering is a fact of life.
• Intelligent, knowledge based machines make it possible for intelligent, unmanned space exploration.
  
  

2016 – 2040

• The Global economy and knowledge based commercial and government organizations began process of becoming thoroughly intertwined.
• The population will have doubled by 2040 to 12 billion people.
• Every person will have a knowledge companion that provides them with unlimited communication capabilities, practical and emotional consultation, education and entertainment.
  
  

2041 – 2099

• Manned and unmanned deep space exploration. Inter planetary travel.
  
  

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