G.R.E.E.N.

An Expert System to identify Gymnosperms

Antonio B. Bailón, Eva Gibaja, Ramón Pérez

Department of Computer Science and Artificial Intelligence

University of Granada

Carmen Quesada

Herbario de la Universidad de Granada

ywords: Gymnosperms, Identification Keys, Expert Systems, Artificial Intelligence, World Wide Web, Iberian

Peninsula, Certainty Factors

Abstract: The application of Artificial Intelligence (AI) techniques to the problem of botanical identification is not

particularly widespread even less so on Internet. There are several interactive identification systems but they

usually deal with raw knowledge so it appears that “research and development of web-based expert systems

are still in their early stage” (Li et al., 2002). In this paper we present the G.R.E.E.N. (Gymnosperms

Remote Expert Executed over Network) system as an expert system for the identification of Iberian

Gymnosperms which allows on-line uncertainty queries to be made. The system is operative and it can be

consulted in http://drimys.ugr.es/experto/index.html

1 INTRODUCTION

Plant Taxonomy is a complex, meticulous science

that allows taxa to be identified by retrieving

information contained on them in a classification

system. There are various ways which this

identification may be carried out, although the one

most commonly used employs dichotomic keys (a

process which requires knowledge of botanical

terminology and organography). As a result of the

complexity of this process, botany-related activities

are not particularly automated.

A number of interactive identification systems

have been reported in the literature. Taking into

account the data structure chosen to represent the

knowledge we can distinguish basically two kinds of

systems: matrix-based identification systems like

INTKEY (Dallwitz et al., 1993), MEKA (Meacham,

1996) and rule-based expert systems like IKBS

(Grosser et al., 1999) and RIH (Grove et al., 1999).

We can also divide interactive identification systems

in on-line systems like NAVIKEY (Bartley, 1999),

LUCid (CPITT, 1999), POLLYCLAVE (Dickinson,

1999) or INTKEY and non-on-line systems like

MEKA or XID (XID, 1999). Dallwitz have done a

comparison of interactive identification programs

(Dallwitz, 2000) and it seems that INTKEY and

LUCid are the most complete. Some of the

characteristics described in his paper are not

contained in our system (like guidance about the

next character to use, and subsets), nevertheless, we

introduce a very desirable and not too much studied

characteristic in other identification systems: the

management of uncertainty and imprecise

information.

AI offers a productive approach to identification

by managing uncertainty in order to obtain a better

response when user’s observations don’t match

exactly with the set of characters represented in the

system. Successful rule-based expert systems and a

strong mathematical theory have been developed

since the first expert system (DENDRAL in 1965) to

the present time. In spite of this, intelligent

identification systems that deal with uncertainty are

not particularly widespread. In this sense only a few

systems have been related (Atkinson et al., 1987;

Fermanian et al., 1989). Their main disadvantage is

that the botanical expert must provide a probability

distribution. In this paper we propose an alternative

216

B. Bailón A., Gibaja E., Pérez R. and Quesada C. (2004).

G.R.E.E.N. - An Expert System to identify Gymnosperms.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 216-221

DOI: 10.5220/0002642702160221

 SciTePress

to avoid these disadvantages. The alternative is the

use of expert systems whose uncertainty

management technique is the certainty factors

theory.

Within the wealth and variety offered by the plant

kingdom, the subject of scientific disclosure has

been dealt with a specific study of the group of

Gymnospermae (46 autochthonous and cultivated

taxa present in the Iberian Peninsula). This group

was chosen due to the presence in this area of

important forest species which it contains. In

addition, many of these offer resources or are

cultivated as ornamental, which makes their

identification useful for non-botanical expert users.

This has all given rise to G.R.E.E.N.

(Gymnosperms Remote Expert Executed Over

Networks), an on-line decision aid system that

applies AI techniques of machine learning and

uncertainty management to the field of Botany.

2 SYSTEM STRUCTURE

The system structure is derived from the way in

which botanical experts work. Particularly,

dichotomic keys of the type IF-THEN are used for

the recognition of plant species. That is to say, that

each key leads to either another key or a plant

species. When a botanist wants to identify a

particular species, it is possible to distinguish:

• A source of knowledge comprising all the

available information on each plant species in

the form of dichotomic keys.

• A process of the use of this knowledge. Keys

are searched until a particular species is

identified.

This description coincides with that of a

knowledge-based system and more specifically with

that of a rule-based expert system with: a

knowledge base which stores knowledge about the

domain of the problem in the form of rules and an

inference engine which extracts information from

the knowledge base. In addition to these two

modules, the system has:

• An uncertainty-processing module fitting the

nature and subjectivity of the observer.

• A justifying module which explains the results

achieved by the system in a language close to

the natural language.

• A multimedia database to reference known

species. This module provides images and data

about species, ecology and distribution.

• A glossary of scientific terms to make the

system more accessible to users who are not

botanical experts.

• A server which will deal with user (client)

requests and sends back the results by Internet.

We can see the system’s architecture in Fig. 1.

Figure 1: System’s architecture

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217

3 KNOWLEDGE ACQUISITION AND

ELICITATION

The information available on the problem domain is

dispersed, incomplete, imprecise and unstructured.

Particularly, this complexity is manifested in:

• Character dependence. The presence of some

character may depend on the presence/absence

of other ones.

• Differentiating attributes. These attributes

difference only one taxon so when they are

present we can directly do an identification

while this information is not relevant to identify

other taxa.

• Continuous and uncertain values.

In order to be able to represent the knowledge in

an appropriate way, a process of knowledge

acquisition and elicitation was needed. The

acquisition and elicitation process began with

different dichotomous keys whose information was

gathered and summarized, thereby producing a list

of diagnostic characters (descriptors or attributes)

and values at family, genus, species and subspecies

level.

This hierarchical organization of the information

offers the advantage of multilevel answers.

Generally, only a small amount of information

(which is also what is observed more easily) is

needed in order to reach an objective in the higher

levels of the hierarchy. Obviously, the more

information we have, the more we will know. Due to

the inherent complexity of taxonomical information

all information was subsequently compared several

times by observing nature and consulting herbalist

documents and experts.

The most important taxonomical characters in

Gymnosperms were divided into different groups:

general aspect of the taxon, characteristics of the

leaf, of the branches, of the shoots, monoecious or

dioecious, characteristics of the fructification (cone

and "berry" cone), of the seeds, and ecology of the

taxon. With these characters and with additional

expert information, decision tables (Durkin, 1994)

were compiled, which gathered the identifying

diagnostic characters for each taxon (see Table 1).

4 TREATMENT OF

UNCERTAINTY

Information about the domain is based on what

normally happens, but every rule has its exceptions.

“We must take into account diversity and

incompleteness, and exception is the only valid rule”

(Grosser et al., 1999).

Table 1: A fragment of the decision table for Iberian

Gymnosperms Families

Family

General

Appear

ance

Resinif

erous

Leaf

Characters

(.....)

Ephedraceae Shurb No Scale-like (.....)

Cupressaceae

Tree

and

Shurb

Yes

Acicular

and Scale-

(.....)

Taxaceae

Tree

and

Shurb

No (.....)

Pinaceae Tree Yes Acicular (.....)

Araucariaceae Tree Yes (.....)

Taxodiaceae Tree Yes Acicular (.....)

Cephalotaxaceae

Tree No (.....)

Cycadaceae Palm No (.....)

Ginkgoaceae Tree No Fan-Shaped

(.....)

As it is usual for some data not to be known with

absolute certainty errors of measurement may be

committed. Given this large amount of sources of

uncertainty, the system incorporates a module to

deal with uncertainty.

Uncertainty is modelled using certainty factors

(Shortlife, 1975) since it is a simple computational

model which allows experts to estimate confidence

in each hypothesis and in the conclusion, facilitating

the expression of subjective certainty estimations.

This model also enables knowledge to be

represented easily in the form of rules and has

successfully been used in many other systems. So on

one hand the user can tells the system how sure is he

about his own observations and on the other hand

the system is able to give a response with a certainty

degree associated based in the certainty of rules and

user’s data.

Other advantage of the use of certainty factors is

that they can be automatically estimated when we

obtain the rule base from tables so the expert doesn’t

have to give the system any probability distribution.

5 OBTAINING THE KNOWLEDGE

BASE

A set of rules with a certainty factor associated (the

knowledge base) was obtained automatically from

the decision tables. For this, a modification of the

ID3 algorithm proposed by Quinlan (Ignizio, 1991)

was used in order to obtain more than one rule per

objective. Rules of minimum length (entropy

determines the minimum set of diagnostic characters

in order to recognize a taxa) were created which

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excluded irrelevant knowledge, since irrelevant

descriptors were not taken into account. We

obtained keys which were different from the

standard ones. Particularly whose content is more

complete than that of the dichotomic keys, since the

knowledge base contains all the consistent rules

which may be obtained according to the selected

descriptors in order to determine the objectives.

The rules provide a structuring of the knowledge

which the user can understand and which is similar

to the dichotomic keys used by expert botanists.

When the system presents its conclusions in the

form of rules, the user understands the reasoning

followed by the system perfectly and becomes

familiar with the reasoning process followed by the

human experts who have contributed their

knowledge to the system (learning).

Additional advantages of computerized systems

are discussed in (Dallwitz, 2000).

6 CONSISTENCY REINFORCER

During the development of the knowledge base,

inconsistencies may arise mainly due to errors

during the knowledge acquisition and elicitation

stage. The system is capable of accommodating

uncertainty which is why inconsistencies about the

certainty of results cause an additional impact.

Consequently, this makes it necessary for the

system to incorporate a consistency reinforcer

which systematically analyses each of the rules in

the knowledge base in order to guarantee its

consistency and completeness.

Checking for consistency includes detecting

redundant rules, conflicting rules, subsumed rules,

rules with unnecessary conditions and circular rules

while checking for completeness means checking

for missing rules, unreferenced attribute values,

illegal attribute and decision values, unreachable

conditions and unreachable goals. The algorithm

designed is based in (Grzymala-Busse, 1991) and it

is shown in Fig. 2.

Figure 2: Algorithm to check the consistency

7 SYSTEM REASONING

The inference engine provides the control

mechanism and knowledge inference (the process

used in order to derive new information from known

information). It combines the input facts with the

knowledge gathered in the knowledge base thereby

responding to user queries. In order to design the

inference engine, Ignizio's BASELINE with forward

chaining and a modification to deal with certainty

G.R.E.E.N. - AN EXPERT SYSTEM TO IDENTIFY GYMNOSPERMS

219

factors has been taken as a model (Ignizio, 1991).

The inference engine incorporated into the system is

quite a different module from the knowledge base.

This differentiation is important since:

• The system designers can capture and organize

the knowledge common to the problem domain

independently of its implementation.

• It enables the content of knowledge base to be

changed without the need to change the control

system

• A single inference engine may be used to solve

different problems.

8 OTHER CHARACTERISTICS

The system may be easily adapted in order to

classify species other than Gymnosperms.

It is also easy to use. The specimen descriptors

are grouped into general categories (general

appearance, leaf, branch, cone, etc.) with familiar to

all users names (see Fig. 3.a). Within each category,

users select the descriptor they know and enter a

value for the degree of belief.

The system provides two methods for entering

the query: basic (the user has a set of options, so that

the use of certainty factors is clearer) and advanced

(the user manually enters the certainty value of the

observation).

After entering the data, the inference process is

executed and the system presents the user with a set

of results (ordered according to how well they fit the

query) and an outline of the reasoning followed in

order to reach these conclusions (see Fig. 3.c).

If the user wishes, it is possible to increase the

information about the specimen by accessing the

multimedia database.

As the system has been specifically designed to

work on Internet, the entire on-line transfer of

information has been minimized so as not to

overload the server and in order to obtain a

satisfactory system response time for the user. For

example, suppose a user has done an observation

where “leaf characters” is “for sure scale-like” (see

Fig. 3.b) and “for sure is resiniferous”, with only this

information the system concludes that the item

observed was a Cupressaceae whose CF value is 1.

Figure 3: a) The user interface for introduction of data. b) A view of the character “characteristics of the leaf”. c) The user

interface for identification results

If the user introduces “fruit consistence” is

“fleshy” the system reaches the conclusion “genus is

Juniperus”. By adding “number of seed of the berry

cone is two to four” and “type of shrub” is “probably

postrated” the system concludes “Juniperus sabina”

with CF equal to 0.7 (see Fig. 3.c). The system also

could have reached the same objective with a

different input. For example, it could conclude the

item was a Cupressaceae with “fruit consistence

fleshy”, “seed with fleshy aril=no”, “leaf

persistence=persistent”, “brownish leaf=no”, “sexual

character=dioecius” and “numerous seeds = no”.

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10 CONCLUSIONS

By way of conclusion and to sum up:

• In this paper, an expert system is presented

which will offer the user a new interactive

species identification method whose main

contribution is the use of intelligent techniques

to deal with uncertainty

• It solves problems in which incomplete data is

handled. This is an important feature, since in

taxonomical classification processes

information and observations are not complete.

• IA and Internet technology offer new

advantages to the popularisation of Botany.

• The user can easily learn the features to observe

through interaction with the query interface. It

also is able to spread expert knowledge by

justifying the solution, so that the user can learn

the reasoning followed by the system.

• The model to represent the knowledge is also

useful in order to produce automatic keys or

computer-generated keys.

• The system is a practical operative tool which

may be used on-line and which will enable

different taxa comprising the Iberian

Gymnosperm flora to be recognized.

• The model can be extended to other branches of

Biology as well; it is a question of generating a

suitable knowledge base and a user interface,

while the inference system does not change.

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