Educated beyond common sense. A follow onto Knowledge Normalization Methodology

Educated beyond common sense

Educated beyond common sense. A follow onto Knowledge Normalization Methodology

Title: Knowledge Normalization Methodology.

Introduction to CAMBO, a multi-Expert System Generator, and a new paradigm knowledge normalization (patent pending). "Any entity's knowledge, which can be described in "English Grammatical Sentences", can be extracted and software (Rule processing) managed through a Normalized Knowledge Base." Unlike Single EXPERT system generators, AION, Eclipse, XpertRule, RuleBook, CAMBO's kernel logic; Knowledge Normalization Methodology is based upon a methodology that closely observes the rules of medical science in identifying, applying and developing the science of machine intelligence.

Abstract of the Disclosure.

The invention's name is CAMBO an acronym for Computer Aided Management By Objective. The title is a "multi-EXPERT System Generator", and the vision an "artificial intelligent bridge between technology and the ability to automate the instruments of the MBO methodology, namely: Charters, Organization Charts, Operational Plans, Project Management, Performance Planning and others all containing the knowledge, expressed in 'English Grammatical Sentences', upon which an enterprise conducts business. It would require the design of a unique combination of advanced methodology and technology capabilities built upon and work in concert with current state of the art, 'Data Normalized', Relational Data Base structure. The "AI Bridge" would include an advanced methodology for Normalizing Knowledge, a unique definition for a unit or element of knowledge, an advanced structure for a Spatial Relational Knowledge Base and a 5th generation programming language to support a Natural Language Processing interface.

What is it? Each employee of a business enterprise is a human expert possessing a measure of talent, developed through the experience of practical application, interaction and training with other employees. The result of job related experience is an acquired understanding of: How, When and Where each employee is expected to contribute towards the operation of an enterprise. This understanding is expressed and communicated through simple conversational language, in which each language sentence represents a "knowledge element." CAMBO describes knowledge in segments called elements with each element structured as a simple sentence, in which the employee expresses a single step, that when added to other related elements provide a complete description of a job related task. CAMBO imposes a single convention upon the formulation of each element, as a control parameter to support CAMBO's fifth generation programming language called "Language Instructions Per Sentence" (LIPS). The convention requires that each sentence must begin with an action verb, selected from the CAMBO-LIPS "Action Verb List."

"Analytical thinking requires the ability to compose business issues into logical and functional models that correctly reflect business processing, coupled with the skill to communicate the results to all levels of an organization"

Normalizing Knowledge

Knowledge engineering, which includes methodologies, techniques and tools, produces knowledge models for populating a storyboard layout for the design of a multi-expert system. Each knowledge engineering model is a particular life cycle view of activity and it models the functionality of a knowledge engine that drives the events within the life cycle. These models identify, capture, profile and relate the language of the enterprise for which the multi expert system supports. Knowledge engineering models the relationship between the business practices of an enterprise and the functionality of an ERP methodology and this information contributes toward the knowledge normalization process. The methodology for knowledge normalization expresses a knowledge element as an English grammatical sentence. Knowledge engineering codifies the business, science and engineering knowledge into its most basic form, the English Grammatical Sentence (EGS).

Each EGS is grouped into rule-sets that become part of a knowledge domain and because the knowledge normalization process establishes cross-domain relationships, the knowledge of many disciplines unite to answer questions. The procedure for asking questions is a simple, intuitive and interactive web based menu system that leads the user through a question and answer cycle - including a cross discipline review of the issues leading to a final answer. It responds as though the questions were asked of numerous engineers or business managers, in different disciplines, all contributing their knowledge towards identifying and answering issues on a specific business or engineering requirement. However, while the methodology for data normalization remains as a standard for developing a relational data base, the processes described are integrated with the processes for knowledge normalization. Data element: definitions, profiles, format, relationships, where-used and ontological associations all compliment the process of knowledge normalization.

"The methodology for knowledge normalization expresses a knowledge element as an English grammatical sentence. Knowledge engineering codifies the business: philosophy, science, engineering, and art's (the four prime domains of knowledge) "Relational Knowledge Base", into its most basic form, language"

Knowledge elements contain the logic by which data elements are created and manipulated. The status or condition of any individual data element is the result of a computer program, executing a series of knowledge elements (rules). Knowledge engineering is used to front end the data methodologies for: ORACLE, SYBASE, DB2, FoxPro, SAS, SAP, IEF, Microstrategy and all RDB application generating software.

For those readers that remember Herman Hollerith, congratulations for still being part of the technology evolution landscape. We have witnessed and been part of a most incredible industry journey. For this reason my hope is that your common sense, derived from decades of structured thinking, will view knowledge normalization as the next logical step for the technology evolution. Within this article I have expressed a direction for next generation technology towards a language based methodology to normalize knowledge. And while it does not replace relational data base structure, knowledge normalization (storyboards, rules and NLP) will identify and relate data elements and function as a front end designer for building a RDB.

Knowledge normalization raises many questions about design, structure and most importantly its business application. Consider the capability of reviewing, monitoring and testing new ideas about the manner in which, an enterprise conducts business. Reviewing the decision-making relationships that reflect the rationale and reasoning logic of the individual employee job description. Monitoring the working relationships between employees in which each job description, knowledge elements (rules) are associated with all other employee job responsibilities. Testing the knowledge storyboard by changing the knowledge elements (English grammatical sentences) and coordinating your changes with ALL employees' decision-making job description rules (English grammatical sentences).

The future of Knowledge normalization will reflect a repeat of the post data normalization technology evolution. Business intelligence, project management and process control about the manner in which, an enterprise conducts business will need 'application developers' to reprogram their products. The migration from data base systems to knowledge based systems will pass from gradually integrating and adapting new technology interface to adopting the benefits of real time decision-making management.

* Special appreciation and kudos to the folks at Wikipedia, the free encyclopedia, that provides an open forum for the development and communication of new ideas.

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