ECA RULE ONTOLOGY

Modelling Prescriptive Rules as Descriptive Ontology

Vandana Kabilan

Department of Computer and Systems Sciences, Royal Institute of Technology and Stockholm University

FORUM 100, SE- 16440, Kista, Sweden

Pernilla Svan

FOI, Swedish Defence Research Agency, Division of Systems Technology, Department of Systems Modelling

SE- 16490, Stockholm, Sweden

Keywords:

Ontology.

Abstract:

Ontology is said to be descriptive in contrast to the prescriptive nature of typical behaviour, process or rules.

However, rules and behaviour models are in themselves a domain of re usable shared knowledge. Whether

in the domain of enterprise systems or in military modelling and simulations, we ﬁnd instances of such ’pre-

scriptive’ body of knowledge. While capturing such ’rules’ as programmatic axioms using rule languages

may be viable, it does not help us is establishing the reusable domain knowledge. In this paper, we combine

software engineering technique based on the Event Condition Action logic to propose the ECA Rule Ontology

as a mechanism for capturing such prescriptive rules as ontological conceptualisations. We exemplify our

approach by applying it to the domain of military operations modelling and simulations.

1 INTRODUCTION

Ontology is a conceptualisation of domain knowl-

edge. The fundamental objective behind the design

of ontology is to conceptualize the domain of inter-

est. The other main objectives for the use for ontol-

ogy in information systems as proposed by (Noy and

McGuiness, 2001) are:

• To make explicit implicit domain knowledge

• To promote easy understanding between human

users, thereafter between humans and machines,

followed by machine-to-machine understand abil-

ity and ultimately machine-to-human understand

ability.

• To support reusability of conceptualised knowl-

edge.

To achieve the above,we see at least two main hurdles

as:

• We need guidelines,methods, tools to capture and

represent domain knowledge consistently

• This knowledge capture and representation is to

be carried out by non-ontology experts. That is

the naive domain expert should be able to perform

this domain knowledge analysis and representa-

tion.

The above mentioned issues are key to the success

of all semantic web based applications. In this pa-

per, we focus on these aspects. Our case study do-

main is that of military modelling and simulation, as

shall be discussed in detail later. Thereafter, we fo-

cus speciﬁcally on modelling and represnting a body

of presciptive, behaviour or rules in this domain, like

Rules of Engagement and Standard Operating Proce-

dures. Similar domain knowledge of rules in the en-

terprise system domain could be business rules and

policies. Hence, our research is not restrictive in its

domain of application. However, for this paper, we

ellucidate and discuss it only in the context of mil-

itary modelling and simulation. In this paper, we

combine software engineering technique based on the

Event Condition Action logic to propose the ECA

Rule Ontology as a mechanism for capturing such

prescriptive rules as ontological conceptualisations.

The ECA Rule ontology has a two fold application;

ﬁrst, we use it as a conceptual schema for us to cap-

ture and model individual facts of the domain, i.e, it

acts as our knowledge repository. Secondly, it acts as

a lexicon and a guide for the nave information sys-

tems user, who is not necessarily an ontology expert.

These information system users are our targeted audi-

ence, who will execute the task of conceptualising the

408

Kabilan V. and Svan P. (2007).

ECA RULE ONTOLOGY - Modelling Prescriptive Rules as Descriptive Ontology.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Web Interfaces and Applications, pages 408-415

DOI: 10.5220/0001277904080415

 SciTePress

domain knowledge from natural language to knowl-

edge representation. We use conceptual models as

the ﬁrst visualisation of our knowledge representation

followed by an instantiation in Web Ontology Lan-

guage. We exemplify our approach by applying it to

the domain of military operations modelling and sim-

ulations. We validate both our approach and the pro-

posed ontology through a user feedback study of non-

ontology experts, but military domain experts. The

rest of this paper is structured as follows: in section

2 we give a short background description of the mili-

tary simulation and modelling domain. Therafter, we

introduce our proposed ECA Rule Ontology. In sec-

tion 4, we give short illustrations from our case study

followed by our evaluation study in section 5. Finally

we conclude and summarise our work in section 6.

2 BACKGROUND

In this section we brieﬂy review the theoretical back-

ground for our research as well as summarize the mil-

itary modelling and simulation domain.

2.1 Related Research

we need to capture and analyze the knowledge and

thereafter represent it in a cohesive, yet easy to un-

derstand knowledge representation. However, most

of the domain knowledge is usually available as nat-

ural language descriptions. The goals of natural lan-

guage understanding and semantic conceptualizations

of domain knowledge representations, as investigated

by (Sukkarieh, 2001) , are similar to those of ontology

design goals and objectives in information systems as

mentioned above. Yet, there exists a gap in design

methodology how this extraction of explicit and im-

plicit knowledge from natural language can be repre-

sented and modeled as conceptual models. Concep-

tual models as Knowledge Representation models are

generally graphical and easy to understand. So for

this research, we restrict ourselves to the context of

natural language (English language) interpretation to

knowledge representation as conceptual models. The

transformability of UML conceptual models into for-

mal Frame-based knowledge representation language

like OWL (2004)(OWL, 2004) has been proposed

by several researchers like (Baclawski et al., 2001),

(Kabilan and Johannesson, 2004) and more recently

the OMG recommendation for Ontology Deﬁnition

Metamodel (2005)(ODM, ), which proposes map-

ping rules for UML to OWL and vice versa. Thus,

the reasoning and support features of formal knowl-

edge representations can be attained. This brings us

back to the issue of modelling knowledge represen-

tations from the highly expressive natural language.

Ontology is said to be descriptive in contrast to the

prescriptive nature of typical behaviour, process or

rules. However, rules and behaviour models are in

themselves a domain of re usable shared knowledge.

Whether in the domain of enterprise systems or in

military modelling and simulations, we ﬁnd instances

of such ’prescriptive’ body of knowledge. While cap-

turing such ’rules’ as programmatic axioms using rule

languages may be viable, it does not help us in estab-

lishing the reusable domain knowledge. We deﬁne

based on deﬁnitions by (Gruber, 1993) and (Uschold

and Gruninger, 1996): ”An ontology is an explicit

formal conceptualization of a shared under-standing

of the domain of interest including the vocabulary of

terms, semantics as well as their pragmatics.” Hence,

our approach is to capture not only the domain ’static’

descriptive concepts but also the prescriptive details.

We do not discuss our approach for conceptualisation

of the military concepts in this paper, the interested

reader is referred to (Kabilan, 2006) and (Mojtahed

et al., 2005) .

2.2 Military Modelling and Simulation

Background

Simulation is a technique where computers are used

to imitate (simulate) real world processes. Because

of the complexity of the real world we need a limited

model of the speciﬁc system to be able to study it and

understand better how it works. A simulation can also

be described as an experiment with a model to follow

and understand the behaviour and causal relationship

in the model over time. Some examples of application

areas where simulations is a useful tool are design

and analyse of manufacturing systems, analysing ﬁ-

nancial and economic systems, reengineering of busi-

ness processes and a numerous of areas in the military

domain (Law and Kelton, 2000). The use of Mod-

eling and Simulation within the military domain has

increased since it reduces cost and increase the efﬁ-

ciency for implementing military operations. More

speciﬁcally it is used to speed up and increase the

quality of analysis and studies. Using Modelling and

Simulation for training and education increases the ef-

ﬁciency and lower the costs substantially since simu-

lators can be used. Simulators are also substantially

for supporting Command and Control (C2) and plan-

ning functions (Holm, 2006) .

ECA RULE ONTOLOGY - Modelling Prescriptive Rules as Descriptive Ontology

409

2.3 Case Study: Rule of Engagement

Military activities are regulated by different rules and

guiding procedures. One of those are Rules of En-

gagement (ROE) which are directives designated to

regulate situations and limitations for when force may

be used. ROE reﬂect political and diplomatic con-

straints and should be set at high general level (Krigs-

man and Svensson, 1999). A ROE deﬁnes the con-

straints for a certain military activity, what is forbid-

den and what is allowed for the speciﬁc activity dur-

ing certain circumstances. Examples of such activ-

ities are use of force, detention of civilians, use of

land mines, use of warning shots, and prevention of

crimes.

The aim of using ROE is threefold. The political

aim is to guarantee that military operations are im-

plemented to support the policy of the political man-

agement. The military aim is to guide commanders

to solve conﬂicts. Finally the legal aim of ROE is

to guarantee that military operations are implemented

within the limits of national, international and human

rights.

The ROEs used in this case study has been taken

from a ﬁctitious scenario used for experiments and ex-

ercise in the Swedish Defence Forces. The scenario

contains a multinational operation with the end state

goal to implement a signed Peace Agreement in an

area with many years of ethnical and religious con-

ﬂicts. A collection of ROEs taken from this scenario

have been used for our analysis and case study.

The ROE was used as input information to

the phase Knowledge Representation in the

DCMF(Defence Conceptual Modelling Frame-

work) process and analysed with the method Five

Ws(Carey et al., 2001). In previous work (Mojtahed

et al., 2005) we used the Five Ws to analyse a

sequential scenario giving the result that it is feasible

but much of the context was lost. This time we were

interested in analysing rules with Five Ws but also

to deﬁne more speciﬁcally how to keep the context

after analysis. In the following section, we brieﬂy

summarise the Five Ws method.

2.3.1 5Ws Knowledge Analysis Method

The Five Ws is a concept used in journalism for struc-

turing a story of something. The principle is that

a complete report must answer the ﬁve interrogative

words: Who, What, When, Why and Where. Some-

times an H is added representing How. All these ques-

tions should be answered with necessary facts in order

to get a complete report and the full story of some-

thing that has happened. This structure is common in

news style and news reports but has also been used

for police investigations. The same Five Ws approach

has been introduced and used for military operations

analysis as seen in works of. ((Turnitsa et al., 2004).)

and ((Carey et al., 2001))

The Five Ws has also been used in the miliatary

domain for structuring information (Turnitsa et al.,

2004) One example is the Battle Management Lan-

guage (BML) effort aiming towards development of

an unambigues language to be used by Command and

Control (C2) forces providing the fundamentals for a

common operational picture. BML consists of a num-

ber of components where one of them is a Data In-

formation Exchange Model (JC3IEDM)(JC3IEDM,

2006) . In the JC3IEDM a set of tables has been

identiﬁed to contain the structure of Five Ws where

it functions as a format for capturing the required in-

formation for an operation order, report or an request.

3 INTRODUCING THE ECA

RULEONTOLOGY

The use of ontologies as knowledge repositories for

facilitating semantic web applications, is widely ac-

cepted. As with any other application, the need for

supporting constraints, policies, rules is self evident.

Event-Condition-Action rules are an intuitive choice

for the same as has been proposed by Papamarkos et

al (2003) (Papamarkos et al., 2003). Other alterna-

tives like the proposed Semantic Web Rule Language

(SWRL) were also considered, but in view of our sec-

ond objective, that is to provide an easy to use guide-

line for the nave user, we chose to adopt the simple

ECA rule approach. An ECA rule has a general syn-

tax of

On EVENT if CONDITION then DO Actions.

We build our ECA Rule ontology on the same prin-

ciple as seen in ﬁgure 1. The EVENT concept speci-

ﬁes the occurrence of any triggers, situations, orders ,

or incidents. The CONDITION concept speciﬁes the

other requirements like state of affairs, current situa-

tion contexts, preconditions, post conditions of other

actions and so forth. The idea is that on the ocuurence

of an EVENT, a pre determined CONDITION needs

to be evaluated. And if the condition is evaluated to be

true then the prescribed ACTION is allowed. Action

in the ECA rule ontology indicates not only physical

activities, but could also be used for delegation of au-

thority, rights etc.

The above information is sufﬁcient, from a data

level software engineering perspective. However,

from the domain knowledge perspective we need to

capture further implicit knowledge like what caused

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410

Figure 1: ECA Rule Ontology Basic- Concepts.

the event? Who was responsible for the event? Who

is affected by the event? What are the consequences?

And so on. This is necessary, as these form the

context for the decision , whether the described rule

is to be executed or not. Therefore, we now ex-

tend the concepts of EVENT with relationships to the

Five Ws analysis approach: who, what, why, where

, when . In the military domain, an event is usu-

ally an unplanned activity, and is perpetrated by en-

emy forces. Similarly, we extend the ACTION con-

cept. For the CONDITION concept, we include as-

pects of preconditions, post conditions, dependencies

etc. The Rule of Engagement is a prescription of

expected and allowed military behaviour in the case

of described events happening. To further aid for

Military domain expert users in their analysis work,

we mapped the ECA Rule Ontology to a military

operations ontology, Defence Conceptual Modelling

Framework Ontology (Kabilan, 2006). This domain

ontology is based on the Joint Command Control and

Communication Information Exchange Data Model

(JC3IEDM)(JC3IEDM, 2006) speciﬁcation from the

Multilateral Interoperability Programme (MIP). For

example ﬁgure 2 shows the mapping of ’who’ concept

to DCMFO (Defence Conceptual Modelling Frame-

work Ontology) concepts. In our proof of concept

demonstration, this is provided as a lexical help for

the user making the analysis and subsequent knowl-

edge representation.

4 CASE STUDY RESULTS

As mentioned earlier,the case study was based on a

ﬁctional document for military exercises and a short

excerpt from that document is given below:

ROE 1: The use of minimum force

to prevent any attempts by persons to

prevent the BFOR from dischargingits

duties is permitted.

ROE 2: The use of minimum force to

defend friendly forces and persons with

designated special status against hostile

action is permitted.

ROE 5a:The use of minimum force to

prevent the taking possession of or

destruction of property with designated

special status is permitted.

ROE 5b:Individual service personnel are

to be informed when they are protecting

specific property on this basis.

An analysis of the same through only the Five

Ws gave result like that shown in Figure 3. The se-

lected ROEs were analysed with Five Ws in order to

study feasibility of analysing rules with this method

and how to keep the context after analysis. The result

showed that it is feasible to analyse ROE with Five

Ws but it is not really satisfying since there is a loss

in information and context which affects the reusabil-

ity. If too much context is lost during the process it is

difﬁcult to reuse the analysed information. One way

of dealing with this is to ﬁll in the gaps with infor-

mation and have a Subject Matter Expert verify the

extended information.

Five Ws is useful on a conceptual level to get a

ECA RULE ONTOLOGY - Modelling Prescriptive Rules as Descriptive Ontology

411

Figure 2: ECA mapped to Domain Ontology for Lexical help.

Figure 3: Sample Rule analysed with only 5Ws.

quick categorisation and understanding of an event.

Using the Five Ws as a support for categorising an

event is feasible since it can contain the most impor-

tant aspects of that event. The Five Ws is good start

though but need to be complemented with rules.

Now,we apply the proposed ECA rule ontology

along with its mapppings to the 5Ws and military

concepts (DCMFO). The same rules were analysed

as follows(ﬁgure 4): The ECArule ontology as well

as the case study was implemented using Protege On-

tology editor, a screenshot of which may be seen in

ﬁgure 5. The targeted users can either create new

knowledge instances of rules in the Protege ontology

editor itself, as seen in ﬁgure 5 or can use our proof

of concept demonstration application. The DCMF

tool(ﬁgure 6) is wizard-like in its approach and aids

the user to analyse and capture the knowledge in a

stepwise process. The tool displays the military do-

main ontology which has been mapped to the ECA

, and the user makes use of this information, while

analysing the text at hand.

5 EVALUATING THE ECA RULE

ONTOLOGY

To assess the ECA Rule Ontology, ﬁve persons were

asked to compare the ECA Rule Ontology and Five

Ws for analysing information. The test group had

knowledge of the military domain and experience of

categorising information. They were asked to cate-

gorise a couple ROE using the simple Five Ws ontol-

ogy. After that they categorised the same ROE using

the ECA rule ontology. Before beginning, they were

introduced to the basic concepts of Five Ws and ECA

rule ontology.

After accomplished the analysis and categorisa-

tion the test persons were asked the following ques-

tions:

1. Ease of analysing the ROE using Five Ws (on a

scale 1-5)

2. Ease of analysing the ROE using ECA rule ontol-

ogy (on a scale 1-5)

3. What information do you think can be analysed

with Five Ws?

4. What information do you think could be analysed

with ECA Rule Ontology?

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412

Figure 4: ECA Analysis for Rule of Engagement.

Figure 5: ECA Analysis for Rule of Engagement.

5. Have you used Protege before? If yes, how

would you rate that the ECA Rule Ontology, for

analysing ROE, has improved (on a scale 1-5) the

following aspects:

• usability

• ease

• utility

For the ﬁrst question, ease of analysing with Five

Ws, there was a mean answer of 3 (max= 4 and

min=2). Analysing with ECA rule ontology was con-

sidered to be easier with an average of 3.6 (max= 5

and min = 2). The domain experts felt that analysing

with only Five Ws was not satisfactory since contex-

tual and implicit information was lost. Speciﬁcally

in the case of Rules of Engagement, the rules do not

explicitly predeﬁne a ’when’ or ’where’ the rule may

be applied. It depends on the situation and context.

The assessment group answered that they would use

the Five Ws to analyse information which described

”sequences of events”, ”scenarios already occurred in

a certain order and a certain place”. On the question-

when to use the ECA Rule Ontology- the test group

had the view that the ECA rule ontology could be used

in cases like description of rules which are not bound

to time or location, instructions and general directives

and so on. Hence, our basic intention to capture and

model prescriptive behavior is fulﬁlled. The entire

test group had seen or used the protege ontology ed-

itor prior to our interview.They rated an average of

4 on the questions regarding the actual ECA Rule

Ontology implementation and interface built in pro-

tege.( on the account of usability (mean=3.8, max=5,

min=3), utility (mean=4.0, max= 5, min=4) and ease

ECA RULE ONTOLOGY - Modelling Prescriptive Rules as Descriptive Ontology

413

Figure 6: Screenshot of our Proof of concept DCMF Analysis tool.

(mean= 4.2, max= 5, min=3) )

The Users felt that the main advantages with the

ECA Rule Ontology are:-

• Their analysis from natural language to knowl-

edge representation became easier, since they had

a help from the ontology and previous ’instances’

to look up.

• They could do the analysis from natural language

to ontology consistently. That is , even though all

the users in our target group were non-ontology

experts, and they all independently carried out the

exercises, they all came up with similar results.

(the ECA analysis result)

• Since most of them were familiar with Five Ws

analysis, the ECA which used 5Ws as well, was

easy to comprehend and adopt.

However, the users also felt that the ECARule On-

tology was not covering all the semantics of CONDI-

TIONS and all its possible relationships to EVENT

and ACTION. We consider this to be an useful input

and hope to improve our model in the next iteration.

6 CONCLUSION

In this paper we focused on the issues of:

• Making implicit knowledge explicit. In every

domain, the semantics reach far beyond the mean-

ing of the natural language used. Contextual

meaning, domain knowledge, purpose are some

examples of implicit knowledge that needs to be

made explicit. The purpose for this to have a com-

mon shared easy to understand model, that can

be used for consensus, sharing knowledge among

other uses.

• Providing guidanace for knowledge analysis to

knowledge representation for naive users (do-

main experts) Most of the current state of the art

ontology design methodologies and guidlines fo-

cus on building ontologies from scratch. While

some like METHONTOLOGY(Fernandez et al.,

1997) focus also on maintenance and evolution

of ontology.The assumption is that these activities

are carried out by the ’ontology designer’ or ex-

perts in the art of conceptualisation. In reality, it is

often so, that once a system is designed and setup

in operation, it needs to be populated with infor-

mation (instances) and maintained. It is expected

that the targeted user groups(domain experts and

not ontology experts) should be able to carry out

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414

this ontology instantiation process by themselves.

This needs that the designed ontology model is

comprehensible for the naive user and is intuitive

to use.

We have proposed ECA Rule Ontology for cap-

turing and modelling prescriptive rules and behav-

iours while addressing the above mentioned key is-

sues. Our proposed work has been validated on two

fronts. First, we have tested the feasibility of captur-

ing and analysing a large number of Rules from the

case scenario, some standard operating procedures as

well. Secondly, more importantly, our proposed ECA

Rule Ontology has been assessed by the targeted users

from the military domain. Our next aim is to apply the

proposed ontology to other domains.

While we have illustrated the utility of the pro-

posed ECA Rule Ontology for the military modelling

and simulations domain, we contend that the same

may be applied in different domains as well. For ex-

ample, if we were to analyse a typical business case

scenario from enterprise systems, like that of a Pizza

fast food restaurent, that guarentees to deliver piz-

zas to its customers within 15 minutes or return the

money. Such prescriptive behaviour may be mod-

elled using ECA as the customer compliant being the

’EVENT’ , the ’delivery of pizza after 15 minutes’ be-

ing the ’CONDITION’ , and the proposed ’ACTION’

being ’return money to customer’. Thus our future

work is focused on application of our proposed ontol-

ogy in other domains. We belive that our research is

aimed at resolving some of the practical issues which

need to be addressed for the success of semantic web

technology based applications.

ACKNOWLEDGEMENTS

The authors acknowledge the collaborated effort, sup-

port and feedback provided by the DCMF Project

team.

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