Knowledge Modeling - Explicitation of Document Content

Explicitation of Document Content

Knowledge modeling includes the explicitation of knowledge and requirements that is available in documents, such as design manuals, (international) standard specifications and standard data sheets. In order to make such knowledge computer interpretable it need to be expressed in a formal knowledge representation language and thus transformed into a computer interpretable form. For example in the form of an expressions Gellish English. This enables that the knowledge and requirements are related to the objects in the knowledge model, whereas the whole model is again stored in a Database.
The knowledge that is contained in documents can be modeled at various levels of explicitation. A low level of explicitation keeps large parts of the specifications in the form of natural language text. This means that the text is only human interpretable, but is nevertheless related to the objects in the knowledge model. Thus software can still present the information to users when knowledge about that object is requested. The other extreme is that the content of each sentence in a documents is converted in the formal knowledge representation language and thus the objects that are mentioned in those sentences become an integral part of the computer interpretable knowledge model. For example, the knowledge that the API 617 standard contains a standard specification for compressors can be linked to the concept compressor in the knowledge model of a compressor system. This can be expressed in a knowledge representation language (using the relation type as follows:

  • compressor API 617

A higher level of explicitation means that paragraphs or sentences in natural language are related to components in the knowledge model. A full explicit model means that the natural language sentences are completely transformed into data in a database structure. For example, a specification of a minimum shaft diameter might be included in the knowledge model as follows:

  • shaft diameter 20 mm

The above described explicitation process results in Knowledge Models and Standard Specifications Models that enable their use for computer supported knowledge-aided design as well as for automated verification of designs. this is done by m.kalpana model types

At its highest-level, Knowledge Models can be categorized into following seven groups:


This type of model is used for diagnosing problems by categorizing and framing problems in order to determine the root or possible cause.

Semantic: Complaint » Possible Cause(s)

Example: I have these symptoms. What is the problem?


This type of model is designed to produce possible options for a specific case. The options may be generated using techniques such as Genetic Algorithms or Monte Carlo simulation, or retrieved from a knowledge and/or case-base system.

Semantic: Problem Description » Possible Alternatives

Example: Ok, I know the problem. What are my options?


This type of model is used mainly for the decision-making process in order to assess or select different options. Of course, there would be always at least two alternatives; otherwise there is no need for making any decision.

A Selective Model distinguishes between cardinal and ordinal results. On one hand, when a cardinal model is used, the magnitude of the result’s differences is a meaningful quantity. On the other hand, ordinal models only capture ranking and not the strength of result. Selective Models can be used for rational Choice under Uncertainty or Evaluating and Selecting Alternatives. Such a selection process usually has to consider and deal with “conflicting objectives.”

Semantic: Alternatives » Best Option

Example: Now I know the options. Which one is the best for me?


Analytical Models are mainly used for analyzing pre-selected options. This type of model has the ability to assess suitability, risk or any other desire fitness attributes. In many applications, the Analytic Model is a sub-component of the Selective Model.

Semantic: Option » Fitness

Example: I picked my option. How good and suitable is it for my objective?


This type of model provides guidance in a bidirectional or interactive process. Among the examples are many support solutions available in the market.

Semantic: Problem Statement » Solution Instruction

Example: How can I achieve that?


A Constructive Model is able to design or construct the solution, rather than instructing it. Some of the recently popularized Constructive Models are used for generating software codes for various purposes, from computer viruses to interactive multimedia on websites like

Semantic: Problem Statement » Design Solution

Example: I need a <…> with these specifications <...>.


In many cases more advanced models are constructed by nesting or chaining several models together. While not always possible, but – ideally – each model should be designed and implemented as an independent component. This will allow for easier maintenance and future expansion. A sophisticated, full-cycle application may incorporate and utilize all the above models:

Diagnostic Model » Explorative Model » Selective Model » Analytic Model » Constructive Model

Technology Options

As a best practice approach knowledge models should stay implementation neutral and provide KCM experts with flexibility of picking the appropriate technology for each specific implementation.

In general the technology solutions can be categorized into Case-based systems and knowledge-based systems. Case-based approach focuses on solving new problems by adapting previously successful solutions to similar problems and focuses in gathering knowledge from case histories. To solve a current problem: the problem is matched against similar historical cases and adjusted accordingly to specific attributes of new case. As such they don’t require an explicit knowledge elicitation from experts. Expert or knowledge-based systems (KBS) on the other hand focuses on direct knowledge elicitation from experts.

There are a variety of methods and technologies that can be utilized in Knowledge Modeling, including some practices with overlapping features. Highlighted below are the most commonly used methods.


A Decision Tree is a graph of options and their possible consequences used to create a plan in order to reach a common goal. This approach provides designers with a structured model for capturing and modeling knowledge appropriate to a concrete-type application.

Closely related to a Decision Tree, AHP (Analytic Hierarchy Process) developed by Dr. Thomas Saaty bestows a powerful approach to Knowledge Modeling by incorporating both qualitative and quantitative analysis.


Influence-based systems such as Bayesian Network (Belief Network) or ANP (Analytic Network Process) provide an intuitive way to identify and embody the essential elements, such as decisions, uncertainties, and objectives in effort to better understand how each one influence the other.


An Artificial Neural Network (ANN) is a non-linear mathematical or computational model for information processing. In most cases, ANN is an adaptive system that changes its structure based on external or internal information that flows through the network. It also addresses issues by adapting previously successful solutions to similar problems.


Inspired by biological evolution, including inheritance, mutation, natural selection, and recombination (or crossover), genetic and evolutionary algorithms are used to discover approximate solutions that involve optimization and problem searching in Explorative Models (refer to Model Types).


Expert Systems are the forefathers of capturing and reusing experts’ knowledge, and they typically consist of a set of rules that analyze information about a specific case. Expert Systems also provide an analysis of the problem(s). Depending upon its design, this type of system will produce a result, such as recommending a course of action for the user to implement the necessary corrections.


Statistical Models are mathematical models developed through the use of empirical data. Included within this group are 1) simple and/or multiple linear regression, 2) variance-covariance analysis, and 3) mixed model

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