Knowledge modeling

Knowledge modeling is a process of creating a computer interpretable model of knowledge or standard specifications about a kind of process and/or about a kind of facility or product. The resulting knowledge model can only be computer interpretable when it is expressed in some knowledge representation language or data structure that enables the knowledge to be interpreted by software and to be stored in a database or data exchange file.
Knowledge-based engineering or knowledge-aided design is a process of computer-aided usage of such knowledge models for the design of products, facilities or processes. The design of products or facilities then uses the knowledge model to guide the creation of the facility or product that need to be designed. In other words it used knowledge about a kind of object to create a product model of an (imaginary) individual object. Similarly, the design of a particular process implies the creation of a process model, which design activity can be guided by the knowledge that is contained in a knowledge model about such a kind of process. The resulting process model, product model or facility model is typically also stored in a database.

Usually the knowledge representation language only allows to represent knowledge (about kinds of things), whereas another language or data structure is required to represent and store the information models about individual things. If the knowledge representation language enables to express both, then the knowledge model and the information model can be expressed in the same language (or data structure). An example of a language that enables the expression of knowledge as well as information about individual things is Gellish English.

The basis of a knowledge model of an assembly physical object is a decomposition structure that specifies the components of the assembly and possible the sub-components of the components. For example, knowledge about a compressor system includes that a compressor system consists of a compressor, a lubrication system, etc, whereas a lubrication system consists of a pump system, etc. Assume that this knowledge is expressed in a knowledge representation language that expresses knowledge as a collection of relations between two kinds of things, whereas in that language a relation type is defined that is called <shall have as part a>. Then a part of a knowledge model about a compressor system will consist of the following expressions of knowledge facts:

compressor system shall have as part a compressor
compressor system shall have as part a lubrication system
lubrication system shall have as part a pump system
pump system shall have as part a pump

Such a knowledge model will be further extended with knowledge and specifications about the properties of the components, their fabrications and possibly testing and maintenance requirements.

Similarly, a knowledge model of a process is basically a specification of the sequence of process stages. This sequence is determined by the fact that a kind of stream is output of a kind of process stage, whereas that same type of stream in input in the next process stage. So the defined streams have roles as inputs to process stages, whereas the same streams are outputs of other process stages. For example:

water shall be input in a boiler
steam shall be output of a boiler
steam shall be input in a heater
condensate shall be output of a heater
etc.

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 <is specified in> as follows:

compressor <is specified in> 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 <shall have on scale a value greater than> 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:

1 DIAGNOSTIC MODELS

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?

2 EXPLORATIVE MODELS

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?

3 SELECTIVE MODELS

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?

4 ANALYTIC MODELS

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?

5 INSTRUCTIVE MODELS

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?

6 CONSTRUCTIVE MODELS

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 MySpace.com.

Semantic: Problem Statement » Design Solution

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

7 HYBRID MODELS

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.

1 DECISION TREE & AHP

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.

2 BAYESIAN NETWORKS & ANP

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.

3 ARTIFICIAL NEURAL NETWORK

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.

4 GENETIC & EVOLUTIONARY ALGORITHMS

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).

5 EXPERT SYSTEMS

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.

6 STATISTICAL MODELS

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

Knowledge modeling

Explicitation of document content

References

Further reading