http://www.sajim.co.za/peer23.3nr2.asp?print=1


   
  Peer Reviewed Article Vol.3(2) September 2001

 

Investigation of a Web-based expert system shell 
Dieter Vogts 
Vista University (Port Elizabeth) 
VOGTS-D@pelican.vista.ac.za 

Contents 

1. Introduction  
2. Identification problem  
3. Web expert system shell design  
4. Themes, templates and resources  
5. Consultation engine networks  
6. Into the future…  
7. Acknowledgements  
8. References  

1. Introduction 

The identification of objects has always been of importance to people, whether it be the 
identification of foodstuffs, poisonous plants, illnesses or the reason that a computer is 
malfunctioning. People are able to make these identifications based on knowledge gained 
from education and experience. Amazingly enough, people are able to make identifications 
even though no two objects are exactly alike (inexact knowledge) and even if they do not 
have all the information about an object available (incomplete knowledge). 

The knowledge required to make certain identifications is often very specific, comprising an 
in-depth knowledge of the domain in which the identification is to be performed. The 
acquisition of this knowledge may take years and be very expensive. Through the use of 
expert systems, it is sometimes possible to computerize this knowledge, allowing the 
computer to make identifications at approximately the same level as an expert. Non-expert 
users are then able to conduct consultation sessions with the expert system in order to make 
use of the expert system's knowledge. 

The medium of the WWW provides many opportunities for expert systems. Quick access can 
be made to world-wide distributed data and information, allowing for resources to be used 
from various locations and allowing anyone with an Internet connection to use the expert 
system. Multi-media based communication permits the use of many different styles of 
human-computer interaction. The expert system's knowledge bases are always up to date, 
eliminating the need for sending out updates and patches. All these, and the fact that the use 
of CGI applications make the expert system hardware independent, make that the WWW 
provides an exceptionally versatile and useful medium. 



2. Identification problem 

In the identification problem (also known as the classification problem), the domain of 
knowledge in which the classification is to be performed has a finite number of classes (or 
types) to which each object in the domain belongs. A finite set of properties is assumed to 
exist that can be used to differentiate between the classes. The exact number of properties 
that exist varies, but typically there are in the tens to hundreds. Properties have values with 
which they can be assigned. A set of property-value pairs describes an object. An object need 
not have a property-value assignment for each and every property. This is typically the case 
for objects on which certain features are not present at all. 

For example, the domain of knowledge chosen for the test case of the expert system was that 
of the identification of a number of South African Encephalartos (cycad) species. Each 
specie that could be identified was a class and a particular cycad was an object. Of the 80 
properties used in an affinity study mentioned in Osborne (Osborne 1990), only 19 were 
used. These included the curve direction of the leaves, leaf colour, cone text and seed colour. 
Values that could be used in an assignment were enumerated values, such as light green, blue 
green and dark green for leaf colour. A particular cycad could be described by a set of 
property-value assignments, such as {leaf length = 50-100 cm; leaf width = 10-20cm; leaf 
colour = dark green; rachis colour = light green}. 

Given a sample object that belongs to one of the classes, the identification problem is to 
establish the assignments for the properties of the object in order to determine the class or 
most likely classes to which it belongs. For example, given a sample cycad, determine what 
to ask the person about it to be able to identify it (as quickly and accurately as possible). For 
some domains of knowledge, it is important to try and ask for as few assignments as 
possible, as the determination of an assignment can be expensive. For example, if the domain 
of knowledge were the diagnosis of an illness, the properties could include blood pressure, 
cholesterol number and blood cell count. The determination of these values can be expensive 
and time consuming, therefore the fewer assignments asked for, the better (assuming the 
identification is correct). 

The problem of identifying an object is complicated by a number of factors: 

Incomplete information  
Inexact information  
Not all the objects in the domain may be known (even though they are finite).  

Incomplete knowledge has to do with the problem that not all the necessary knowledge may 
be known about an object in order to make the classification. For example, given only a leaf 
of a cycad, it is still possible to draw some conclusions about the class of the cycad. 

Inexact knowledge has to do with the fact that no two objects are alike and that, when 
querying someone for information about an object, they may give answers that are 
inaccurate. For example, a person holding two different cricket balls could classify them as 
being cricket balls, even though the balls might have slightly different masses and be 
different shades of red. Also, what may seem like a light green leaf to one person may be 
seen as a grey green leaf by another. Therefore there is a degree of fuzziness that must be 
catered for. 

And lastly, not every single object in the domain may be known. More specifically, not every 
set of property-value assignments is known for objects of every class. To overcome this, 

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generalizations need to be made about property-value sets of objects in a particular class.

There are a number of methods that have been used in an attempt to solve the identification 
problem. Some approaches include using SQL to query relational databases (XID, 2000), 
rule-based expert systems (Lucas and Van der Gaag 1991), fuzzy associative memories 
(Kosko 1992), statistical pattern recognition (De V. de Kock 1991), predicate logic (Durkin 
1996), proximity-based classifications (López-Aligué et al. 1991) fuzzy logic (Kosko 1992) 
and neural networks (Michie 1994) to name a few. Each uses a slightly different approach 
when performing identification, but all work on the assumption that the type an object 
belongs too is the one that is 'closest' to it. The exact definition of 'closest is determined by 
the method used. Each method is suitable under certain conditions. 

The expert system shell developed uses a technique based on the observation that the values 
of properties are all similar to a certain degree and classes can be described by a 
representative number of samples from them. Using these heuristics as building blocks and 
some concepts from some of the aforementioned methods, a technique was developed and 
implemented called the similarity classification method (Vogts 1998). Using the similarities 
of the various properties of objects, it is possible to compare two (or more) objects and 
determine how similar they are. The knowledge of the domain is acquired by providing 
samples of various types and these are used to determine to what degree another object could 
be classified as the class. 

For example, given a number of E. trispinosus samples, and then to determine whether 
another cycad belonged to the E. trispinosus species or not, it would be compared with the 
samples known to belong to the species. The results of the comparisons are combined to 
acquire an overall degree of similarity to the class. A value of 1.0 indicates total similarity, 
while a value of 0.0 indicates complete dissimilarity. Values in between 0.0 and 1.0 indicate 
partial similarity. It is possible for a given sample to be similar to various classes to differing 
degrees. There is normally, however, one class that is a lot more similar to the samples than 
others. The similarity classification method attempts to either prove or disprove the similarity 
until some termination conditions are achieved. 

Figure 1 shows a very small domain of knowledge of simple shapes grouped into classes A, 
B and C. Three samples are examined and their degrees of similarity with each class 
displayed. It is interesting to note that even though certain objects, such as the pentagon and 
rounded rectangle, have not explicitly been said to belong to a particular class, the similarity 
classification technique can still make some observations about them. 

Figure 1 Example of a domain of knowledge of simple shapes 

 

A consultation session normally follows the following steps: 



1. Start off with a set of initial assignments (typically none). 
2. Determine the property that has not already been assigned that will on average prove 

as few classes similar to the object and disprove as many classes similar to the object.  
3. Query the user for the assignment.  
4. If the termination conditions have not been met, go to step 2.  
5. Display the classification and any other related information.  
6. Terminate the consultation session.  

At any stage, it is possible to go back and change an assignment made so far. 

3. Web expert system shell design 

A typical expert system consists of a number of interacting parts. Figure 2 shows a simple 
block diagram of the constituting parts. 

Figure 2 Expert system block diagram and the corresponding physical counterparts (in 
brackets) 

 

The knowledge base is the repository of knowledge that encapsulates the knowledge required 
by the system to exhibit the problem-solving ability of the system. It contains all the 
heuristics, hard facts and heuristic knowledge that an expert knows about the domain. In the 
implementation of this expert system, the data file containing the information for a 
knowledge base is the physical manifestation of the knowledge base. 

The working memory contains all the information and conclusions that have been derived 
during the consultation session, as well as the information that was retrieved from the user of 
the system during the process. The physical counterpart in this expert system shell is the 
query that is submitted by the Web browser. 

The inference engine, on the other hand, is the part of the expert system that utilises the 
knowledge in the knowledge base and the working memory and is able to infer new 
information or make decisions based on the information that it knows. The physical 
manifestation of this is the CGI application that is executed by the Web server when a query 
is obtained. 

Fr the system to interact with the expert system, a fourth module is needed, namely the user 
interface. This is the window through which the system is able to return information to the 
user and query him/her for further information. It is also the part of the system that the 
knowledge engineer will most likely want to change depending on the prospective users. 
Therefore a very flexible way of specifying the user interface is needed. HTML and Internet 
browsers provide an exceptionally flexible, elegant and easy-to-use method for this. The 

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inclusion of multimedia such as images, sound and video are trivial to effect, provided that 
the browser has the correct plug-ins available. 

A consultation engine (the name given to a knowledge base and inference engine) is 
consulted by passing a query to the CGI application. A complete representation of the 
working memory of the consultation needs to be passed in as information about requests to 
the server are not stored by the application. A typical request to the consultation engine looks 
something like this: 

http://a.site.somewhere/cgi-bin/zce.dll?:kb=cycads&leaf+colour=light+green&:th=text+only

There are a number of different parameter types that can be specified in the query. 
Parameters are separated from one another by an ampersand, '&'. Those starting with a colon, 
':', are system parameters and are used to set various aspects of the consultation. All queries 
must include a :kb parameter that specifies the name of the knowledge base to be used in the 
consultation. An optional system parameter is :th that specifies the theme (see below) to be 
used when generating the output page. If a theme is not specified, then the default theme of 
the knowledge base is used instead. Property-value pairs are specified as <property 
name>=<value> pairs with spaces replaced by pluses. If the same parameter is specified 
more than once, the last occurrence of it is used. This allows for the easy replacement or 
deleting of parameters. The following queries are equivalent: 

leaf+colour=light+green&leaf+length=10+-+20&leaf+colour=dark+green 
leaf+length=10+-+20&leaf+colour=dark+green 
leaf+colour=dark+green&leaf+length=10+-+20 

4. Themes, templates and resources 

Before a consultation is begun, the interaction style or theme needs to be specified. A theme 
is a collection of templates that specify how a user and the expert system will interact. This is 
particularly useful for customizing the expert system to different user groups and needs. 
Common reasons for having different themes include: 

Interacting with the users in their own language  
Providing special interfaces for people with visual and auditory disabilities  
Scaling the interface up or down for performance reasons  

Templates are used to describe a particular part of the entire user interface and the method of 
interaction with the user of the consultation engine. Every class and property in the expert 
system has a template associated with it for each theme. The template is simply a text file 
describing the document layout in HTML. Special tags are included that the system fills in 
with data in response to a query. 

The special tags used begin with a hash symbol that distinguishes them from normal HTML 
tags. When a Web browser encounters these, it normally ignores them, but when the 
consultation engine processes them, it typically substitutes them with other HTML or 
performs an operation. 

There are a number of different types of tags: 

General information  
Focus selection  

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Navigational  
Field information  
Resources  
Iteration  

Each type has a different purpose and eventual result. General information tags allow the 
client to request basic information about the consultation engine such as the name of the 
knowledge base, the date of creation, name of the author and version number. Focus tags 
allow the current data list being examined to change to another. Navigational tags allow for 
the navigation through the current data list. Field information tags allow the client to request 
different types of data (typically calculated) of the current item being examined. Resource 
tags contain information about various resources associated with a particular item in the 
current data list. Iteration tags repeat whatever is contained between their <#begin> and 
<#end> tags a number of times (typically once for each item in the data list). 

For example, the template below 

<html>  
<title> 
<#kb> consultation session v<#version> 
</title>  
<body> 
<b>Question</b><br>  
<#assign> 
<#property resource=English_Question><br>  
Select your answer from one of the following:<br>  
<#first> 
<#begin>  
<li><a href=<#value field=url>><#value resource=English_Text></a>  
<#end>  
</body>  
</html> 

once processed, could be transformed into the HTML document below. 

<html> 
<title> 
Cycads consultation session v0.1 
</title>  
<body> 
<b>Question</b><br>  
What is the shape of the sample plant's leaf?<br>  
Select your answer from one of the following:<br>  
<li><a href=http://www.asite.za/cgi-bin/zce.dll?:kb=cycads&Leaf+shape=Rounded> 
Rounded</a>  
<li><a href=http://www.asite.za/cgi-bin/zce.dll?:kb=cycads&Leaf+shape=Pointed> 
Pointed</a>  
</body>  
</html> 

Figure 3 shows a screenshot of an actual template that has been filled in with data by the 
consultation engine. 

Figure 3 Example of a filled-in template 



 

For the templates to fill in useful information, the classes, objects, properties and values in 
the expert system have named resources that are substituted into the document when resource 
tags are encountered. A resource is simply a text string that is user definable. 

Uses of resources include: 

providing information in a particular language  
a media URL  
information about image maps  
JavaScript code (or parts thereof) to be placed in the final document  
a VRML representation of a class, object, property or value  
links to other sites containing more information.  

To create a new theme, all that is required is to create the new templates and add them to the 
knowledge base. If the templates use resources that do not exist in the knowledge base, then 
these must be added as well. 

During a consultation session, it is easy to change to a different theme. This is achieved by 
appending the system parameter :th=<new theme name>. 

5. Consultation engine networks 

Owing to the design of the expert system shell and the WWW it is possible to connect 

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various consultation engines together. Whether the consultation engines reside on different 
servers or the same server is inconsequential (see Figure 4). 

Figure 4 Example of a consultation engine network 

 

The classification (or partial classification) of one consultation engine may be used as input 
into another consultation engine. This can be done, as a consultation engine will only 
examine and operate on parts of the query that are pertinent to it. All other information is left 
untouched. Thus it is possible to send a request for another consultation engine, passing all 
the information gathered about the current consultation, but appending additional 
information to get the new consultation running. 

For example, a 'general practitioner' consultation engine could determine that a patient has a 
very good chance of having a heart disease. This consultation engine could then invoke a 
'heart specialist' consultation engine to determine if the patient is indeed suffering from a 
heart disease or not and, if so, what it is. 

The only proviso is that various knowledge bases should not share the same name space for 
properties as this may cause some confusion. Overcoming this is fairly straightforward if a 
naming convention of knowledge_base_name.property_name and 
knowledge_base_name.class_name is used to identify properties and classes respectively. 

An ability of being able to create networks of consultation engines is that larger knowledge 
bases can be built up of smaller logical knowledge bases. There are many advantages to 
doing this, the most obvious being cost, speed of execution and manageability. 

6. Into the future… 

There are many practical applications for an expert system of this nature. The fact that it can 
be accessed remotely means that community computer centres of underprivileged areas could 
become a place of more than just education. Interest has been expressed in the system by a 
South African telecommunications company for the use of the expert system in a hand-held 
computer to be used by technicians who need to repair lines, relays and switches. Further 
research is being conducted into other possible uses and improvements of the current system.

7. Acknowledgements 

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This paper is based on an MSc dissertation entitled Design of a Multimedia 
Environment/Expert System Shell for the Identification Problem, at the University of Port 
Elizabeth (supervisor: Professor G. de V. de Kock, Department of Computer Science). 

8. References 

1. De V. de Kock, G. 1991. Expert system shell for the classification problem. South 
African Journal of Science, 87:179-182.  

2. Durkin, J. 1996. Expert systems design and development. New Jersey: Prentice Hall.  
3. Kosko, B. 1992. Neural networks and fuzzy systems: a dynamical systems approach to 

machine intelligence. New Jersey: Prentice Hall.  
4. López-Aligué, F.J., Acevedo-Sotoca, I. and Jarmillo-Moran, M.A. 1991. The fuzziness 

of fuzzy partitions. Pattern Recognition Letters, 12:265-271.  
5. Lucas, P. and Van der Gaag, L. 1991. Principles of expert systems. New York: 

Addison-Wesley Publishing.  
6. Michie, C.J. 1994. Machine learning, neural networks and statistical classification. 

London.  
7. Osborne, R. 1990. Encephalartosumbelunziensis. Encephalartos, 21:3-9.  
8. Vogts, D. 1998. Design of a multimedia environment/expert system shell for the 

identification problem. Unpublished MSc dissertation. Port Elizabeth: University of 
Port Elizabeth.  

9. XID Services Inc. 2000. Expert identifications systems. Available at WWW: 
http://www.xidservices.com/.   

  

   

   

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