Theory-based semantics

Theory-based semantics is a phrase used by Richard L. Ballard to describe knowledge representations that are based on the premise that the binding element of human thought is "theory," and that theory constrains the meaning of concepts, ideas and thought patterns according to their associative relationships. For this reason, knowledge stores of theory-based semantic representations do not just represent meaning, they precisely embody the very knowledge they are intended to represent - they KNOW. Ballard's Knowledge Science says that knowledge (Knowledge = Theory + Information), is any input of theory or facts that reduces question uncertainty. From this perspective, theory represents 85% or more of knowledge with information (data, facts of situations and circumstances), representing 15% or less. The built-in, a`priori intelligence of theory also defines concept values and purpose, which in turn, determines each concepts influence on every other concept, idea or thought pattern with which it is associated. Theory-based semantics holds that this state of intelligence is valid whether a concept is held in the mind, or is represented within the machine environment. Once learned by people or machines, theory endures with great tenacity, changing only when new paradigms of thought subsumes or replaces the well-justified theories that we use to understand our world. The endurance of data and facts, however, are quite different. They are in a constant state of flux as situations and circumstances dynamically change one moment to the next. From the standpoint of theory-based semantics, most appearances of change are not new - they are only new to us. The facts may be different, but most often, the theory that defines situations and circumstances remains the same.

Foundations in Theory-based Representations

Biological science and philosophies such as epistemology, offer unique viewpoints on how we "know," but the definition of knowledge and exactly how we know is an on-going debate among many in the academic and scientific communities. In the case of theory-based semantics, the most influential foundations of understanding come from Carl Sagan and his landmark production called . In , Sagan presents the argument that once the requirements for evolution exceeded the capacity of DNA, nature emerged with the miracle of the brain. Sagan argues that what makes the brain unique is its capacity to store knowledge and to remember with a capacity many magnitudes greater than DNA. Sagan presented neurological explanations of how the brain works, bridging the brain's memory function to the conclusion that repeated patterns of thought, such as those produced by theory (the conditional reasoning power that we learn from enculturation, education, life experience and deep analytical thought), work hand-in-hand. Theory-based semantics was born out of these teachings and originally formulated by Ballard between 1987 and 1993 while at UC, Irvine working on natural language systems, and later, while developing complex decision support systems for the U.S. Government. Ballard reasoned that the brain and its methodology for storing and remembering content was the ideal model for software systems that KNOW.

Knowledge Science & Technology

While a professor of physics and computer science at UC, Irvine, Ballard realized while working tri-diagonal matrix problems, that the continuous fractions in mathematics caused explosive errors in calculation. This problem inspired him to pursue the solution for "Conservation in Computation" just as Claude Shannon did for communications in his landmark publication in 1948 entitled A Mathematical Theory of Communication.

Later in the late 80's, while working on complex problems related to the U.S. Government's Star Wars project, Ballard realized that in addition to the continuous fraction problem of mathematics, reliance upon systems of logic was also inappropriate for assessing complex situations where there were no right answers, or where answer uncertainty was extreme. The reason is that systems of logic demand self-consistency in the form of verifiable (true/false) truth conditions. For this reason, he considered logic-based systems to be unsuitable for faithfully modeling metaphysical beliefs and axiological knowledge, because the true/false requirements of logic are contrary to the most basic experience of nature and the natural creativity of the human intellect. His conclusion was that these two systems of thought, though important, play a much smaller role in human understanding then conventional thinking assumes.

=KNOWLEDGE = THEORY + INFORMATION=

Knowledge and Ballard's descriptive formula "Knowledge = Theory + Information," is a core principle underlying theory-based semantic technologies.

The knowledge formula asserts that knowledge is an expression of knowingness that results through the interaction between theory and information. Theory and information are dependent upon each other before an expression of knowledge is achieved. For this reason, theory and information are essential to theory-based semantic representations, and a machine's capacity to KNOW and reason like people. Knowledge can be faithfully represented from documents, drawings, illustrations, forms, spreadsheets, books, contracts, policies and procedures, reference sources and from the very minds of employees, consultants and subject experts. This is achieved because every metaphysical, physical and dataform representation can be faithfully modeled using theory-based semantics. The following breakdown helps to better illustrate the point.

THEORY represents 85% or more of knowledge. It is the thought element that gives meaning to concepts, ideas and thought patterns, by justifying the relationships and meanings of the facts of situations and circumstances. Theory answers our "how," "why" and "what-if" questions (whether they are conscious or unconscious). Circumstances and situations may change rapidly, but the underlying theory that gives them meaning, do not. Theory is “A’ Priori” (known before the fact). It is learned through enculturation, education, life experience and deep analytical thought. It is the theories in our brain that shapes our behavior and the way we assess our world. Well-justified theories such as those validated most successful by science, engineering, business, law and so forth, are most valuable. Theory considers all possibilities regardless of a given situation or circumstance. For this reason, theory is universal. Learn once, use forever.

INFORMATION represents 15% or less of knowledge. Information is the instances of fact that exist in time and space that can be processed by the senses, measured and counted. It is the "who," "what," "when," "where" and "how much" facts of situations and circumstances. Information is “A’ Posteriori”, known after the fact. The data and objects commonly captured and transported through conventional data systems is information, not knowledge, though they may contain knowledge. People are required to use the theory in their brains to make sense of the data in conventional data systems.

Enduring Value of Knowledge

Theories endure for decades, centuries and for thousands of years. Most of the theories in our brains that shape our understanding of basic social relationships were conceived 20,000 to 40,000 years ago, passed down through the generations. Most of our core financial theories, such as "buy low, sell high," or "the principles of simple interest" and "compound interest," were conceived and put into use by our ancestors millenniums ago. New theories are constantly being conceived, such as social trends and fads, but only those that are most practical to society endure.

References

Fundamental Definitions in Knowledge Science & Engineering, Dr. Richard L. Ballard, December 2004 (Course book for 10-week Knowledge Engineering course UC, Irvine). 400 pages.

Theory-based Semantics, "Science and Technology" by Dennis L. Thomas, 2007

ASEE presentation "Creating Systems That Know."

Knowledge Industry Formation, Richard L. Ballard, Ph.D., 2003

ee Also

* Artificial Intelligence
* Knowledge representation
* Computational Intelligence