12-10-2013_09-24-50 / [Simon_Kendal,_Malcolm_Creen]_Introduction_to_Know(BookFi.org)

Activity 14

This activity draws on your previous knowledge of communication (human to human and computer network) and systems development to help you identify sources of distortion in the knowledge transfer process.

Complete the table below with suggestions of where distortion might appear at the various interfaces in the knowledge transfer process.

Real world domain—human expert

Human expert—knowledge engineer

Knowledge engineer—knowledge base

Feedback 14

You should have been able to identify some of the following possible causes of distortion:

FIGURE 4.3. Evaluation of a knowledge base.

Validation and verification help to find these distortions in knowledge in various ways. Figure 4.3 shows some of the objectives of each method.

Activity 15

Consider how the distortion between the knowledge engineer and the knowledge base can be decreased or eliminated?

Feedback 15

There needs to be some method of checking that knowledge within the knowledge base is correct. The quickest method is to ask the human expert to check the knowledge in the knowledge base by asking the KBS questions and checking the responses. This process actually removes distortion caused by knowledge transfer from the expert down to the knowledge base itself.

Verification

Verification of a KBS is likely to involve checks for the following:

Syntactic coherence—to check that all objects in the KB are correctly defined with respect to the inference engine.

Logical coherence—to detect logical contradictions.

Contextual coherence—to check that the KB is consistent with the model of the problem.

Examples of the type of errors that verification of the KBS is trying to identify are as follows.

The number of proof cases required depends on variables such as the number of rules in the KBS and the accuracy required from the outputs. As the number of rules and the accuracy required increases, the number of proof cases also increases.

Validation may also involve checking the output from the KBS to some pre-defined measures such as:

Accuracy—how well the system reflects reality

Adequacy—how much of the required knowledge is included within the knowledge base

Realism—whether the KBS provides realistic solutions

Sensitivity—how changes in the knowledge base affect the quality of outputs

Usefulness—how useful the outputs are for solving problems

Validity—whether the outputs can be used to make accurate predictions.

The precise validation tests may vary according to the KBS being tested.

Standards in KBS Development

Using validation and verification controls will help to ensure that the finished KBS meets its objectives, and check that the knowledge base is providing correct answers to problems.

There are other factors which have contributed to the adoption of standards for the general software development process, i.e., including:

The organisation producing the software needs to provide users with quality software that operates satisfactorily.

The need to develop software within the constraints of time, money and available staff.

The finished product must be at an acceptable level.

The product must be easy to maintain, so documentation is an important area which must be addressed by any standards.

In 1991, only 13% of organisations claimed to use any formal or semi-formal method to validate their KBS. Methods being used were:

Qualitative modelling

Induction

Customer satisfaction

Regression testing

Conventional testing

In-house methods.

This was a relatively low percentage, although it probably related to the lack of experience in producing KBS at that time. Hopefully, the percentage of projects

being validated has increased significantly since then, although there is a lack of empirical evidence to support this hope.

Another method of software validation is to use the International Standards Organisation standard 9003-3, which relates to software development generally (see Figure 4.4).

FIGURE 4.4. The ISO9003-3 standard.

While this standard is not directed specifically at KBS, KBS and other application software share most of the development process. Application of the ISO 9000-3 will therefore help to provide quality KBSs.

The last area of quality to mention is the provision of appropriate documentation to allow the system to be used effectively. Documentation should be provided in three areas:

The knowledge acquisition process should be adequately documented with transcripts of interviews, etc. If this is done then the source of individual items of knowledge contained within the ES can be identified and the knowledge can then be checked.

User documentation so users understand how to use the KBS.

Technical documentation so that the KBS can be amended by another software developer if required. The technical documentation is likely to be quite detailed and be much more extensive than the user documentation.

Summary

This section has explained how knowledge can be placed into ESs using rules. You also learned how chaining is used to determine results or prove a hypothesis from that knowledge. Finally, you learned about some of the problems associated with explanation and brittleness in KBSs as well as how KBSs can be evaluated and validated.

There is a definite goal to the credit application—so backward chaining can be used to check from this goal to ensure that all necessary data has been obtained. In this situation, the data refers to a person’s name, address, etc. Each item of data scores so many points, and a critical number of points will be needed to obtain specific services from the bank.

However, where the goal is not achieved, other evidence may be available to check the person’s status—so forward chaining could be used to determine what information has already been obtained, and what additional information will be needed to meet the goal. For example, if a person has recently moved jobs or to a new address, then additional evidence (perhaps last address or detailed employment history) will be needed to ‘top up’ the points to reach the goal number.

Answer 2

(a)This is an example of a brittle system. The system worked perfectly when it was developed but when it was used to solve a problem outside of its knowledge the system failed.

(b)The knowledge engineer developed a system that was working with 100% accuracy when installed—it was only later that the system failed as the circumstances it was working in changed. Still in today’s litigious society that may not prevent the knowledge engineer from being sued. So what could they have done to legally protect themselves? The best protection the knowledge engineer has is to demonstrate that they developed the system following a professional methodology and to a professional standard and that the process was documented. Thus, if the system was properly validated and this was documented the documentation would show that the system was working well when the system was installed. Further the knowledge within the system would also be documented and this would indicate which chemicals the system could correctly diagnose. Management at the chemical factory were then, one presumes, made aware that if new chemicals were stored on site the knowledge in the system would need to be updated to cover these.

(c)One way of preventing this accident would have been to develop an ontology within the KBS. If the system understood what chemicals it could diagnose then it could have checked the list of chemicals stored at the factory against this ontology. Knowing that new chemicals were stored at the factory the KBS could have refused to attempt a diagnosis as it would then know that its knowledge base was inadequate.

SECTION 4: SEMANTIC NETWORKS

Introduction

This section introduces semantic networks and their use within KBSs.

Objectives

By the end of this section you will be able to:

explain how semantic networks represent data

discuss the advantages and disadvantages of semantic networks.

Knowledge Representation in Semantic Networks

One of the oldest and easiest to understand knowledge representation schemes is the Semantic Net, which is a graphical representation of knowledge that shows objects and their relationships.

In these networks, objects are shown by nodes, and links between the nodes describe the relationship between two objects, for example,

Mary is an instance of trainer, and trainer is a type of consultant.

A trainer trains a programmer and a programmer is an employee.

Joe is an instance of programmer.

From this we can clearly see the relationship that may exist between Mary and Joe.

Activity 16

This activity will help you begin to visualise a semantic network.

Draw a diagram representing the relationships between Mary and Joe, indicating, in the process, the relationship between a trainer, consultant, programmer and employee.

Feedback 16

Your diagram should be similar to this:

Consultant

Joe

Such a diagram is the beginning of a semantic network but this can be improved by more closely defining the nature of the relationships.

Activity 17

This activity shows you how to describe the relationships in a semantic network.

1.Identify which lines on the diagram might be labelled ‘is a’, i.e., to indicate that object A is an instance of object B.

2.Apply the labels to your diagram.

3.Decide on an appropriate label for the line that should not be labelled with this relationship.

4.Apply an arrowhead to the lines to indicate the direction of the ‘is a’ relationship.

Feedback 17

Your diagram should now look like this:

Semantic networks are a powerful and flexible graphical way of representing knowledge. They are often used as a communication tool between the knowledge engineer and the expert during the knowledge acquisition phase of a project.

Inheritance

Inheritance is concerned with how one object inherits the properties of another object.

Activity 18

This activity helps you grasp the concept of inheritance.

In the diagram you created in the previous activities, identify from which classes Mary and Joe inherit properties.

Feedback 18

You should have been able to recognise that Mary, in being a trainer, inherits the properties of the consultant class and that Joe, in being a programmer, inherits the properties of the employee class.

It is possible to describe this graphical representation of knowledge simply and precisely and this will help to achieve the objectives of the semantic network as a