EXPLOITATION OF ONTOLOGY-BASED RECOMMENDATION

SYSTEM WITH MULTI-AGENT SIMULATIONS

Martina Husáková

and Pavel Čech

Faculty of Informatics and Management, University of Hradec Králové, Hradec Králové, Czech Republic

Faculty of Military Health Sciences, University of Defense, Brno, Czech Republic

Keywords: Ontology, OWL, UML, SWRL, Multi-agent simulation.

Abstract: The paper investigates the usage of ontology engineering-based techniques for recommendation system

development. The potential of ontology containing SWRL rules is studied with relation to the generation of

scenarios (recommendations). These scenarios are the basis for the creation of multi-agent models and

simulations. The multi-agent systems offer dynamic view on implications of recommendations that are

suggested by the ontology-based recommendation system.

1 INTRODUCTION

The nature of response operation during the

biochemical incidents asks for a clear set of actions

that follow the generic goal of minimizing the

casualties and loss of protected assets. There are

strategic documents covering the response operation

in general but the specificity of agents, uniqueness,

and relative rareness of such incidents requires

response operation to be more tailored in detail

depending on the type of agent and its

characteristics. However, most of the knowledge

that needs to be processed is in the form of

declarative assertions and atoms of actions such as

simple rules resident in heads of experts. The aim of

the paper is to design automated support for decision

making during biochemical incidents. The

comprehensive framework for decision support

during emergency situation caused by biochemical

agent is proposed. This framework should cover the

whole decision making process and enable for

elicitation, translation, integration of knowledge and

imitation the decision making processes of human

expert. Traditionally, the expert system with

knowledge base and inference engine would be

deployed. However, we suggest to use ontology for

knowledge representation about biochemical

incidents and extend the expert system with

simulation engine to provide better justification for

recommendation. Thus, the recommended set of

actions will be assessed against the assumed impact

modelled in the simulation. The focus is on

technological aspects so that particular solution can

be designed and tested.

The structure of the paper consists of two parts.

First, the conceptual level of the framework is

outlined. Second, the technological and

implementation aspects of the solution are being

designed and discussed. The solutions will include

the integration of recommendation with simulation

for better justification of the recommended actions.

2 SOLUTION

The conceptual level is used to define basic

requirements and goals of the system. Also the

system architecture is being delineated and the

decomposition of the system into particular

subsystems together with interfaces is being

designed. The technological level deals with

possible technological platform. The particular

methods and techniques are determined and aligned

along the goals given in conceptual level. The

implementation level focuses on realizing the model

in a particular environment using particular

specification or language.

The conceptual level has already been described

in (Čech, 2011). The system was decomposed into

three subsystems:

 subsystem modelling the incident,

 subsystem for scenario generation,

433

Husáková M. and

Cech P..

EXPLOITATION OF ONTOLOGY-BASED RECOMMENDATION SYSTEM WITH MULTI-AGENT SIMULATIONS.

DOI: 10.5220/0003659404330436

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 433-436

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

 subsystem supporting the selection of action.

Subsystem modelling the incident is used for

capturing the domain knowledge and current data

about the incident. The input into this subsystem

consists of the domain knowledge that is

predominantly in the heads of expert on a particular

area being medicine, epidemiology, biology,

chemistry etc. There are also information in the form

of documents, books and similar resources that

contain already formalized knowledge, see “Fig.

1a”. The output of the subsystem that is at the same

time input into the subsystem for scenario generation

are the characteristics of agent together with relevant

characteristics of the environment that are important

relative to the agent.

Subsystem for scenario generation is based on an

inference engine and corresponding causality models

in form of rules, see “Fig. 1b”. Declarative domain

knowledge about the agent and current data about

the incident will be processed against the rules and

the recommended set of action or scenario of

response operations will be generated. It is necessary

to receive the feedback from the expert(s) for

ensuring the correctness of SWRL rules

representation, see “Fig. 1c”.

Figure 1: Ontology-based framework.

Subsystem supporting the selection of actions is

based on a simulation engine. The simulation takes

the characteristics of the agent and environment as

parameters and examines the impact of a particular

set of actions. The particular set of actions represents

alternatives that are to be considered. The resulting

estimations of casualties and impact on the protected

assets form the output of this subsystem, see “Fig.

1d”. Outputs of multi-agent simulations have to be

compared with the outputs presented the reality.

Expert is able to give us the information if the multi-

agent system behaves correctly or not, see “Fig. 1e”.

2.1 Ontology and Rules

The principles of ontological modelling are applied

in formalizing the captured knowledge. According to

Gruber (1993), ontology is regarded as “an explicit

specification of a conceptualization”. This definition

was slightly extended (Borst, 1997). Ontology is

“formal specification of shared conceptualization”.

Ontology should be formalized for machine-

processing and reflect consensus between people for

knowledge sharing.

There are several widely used languages for

ontology modelling. The OWL ontology, based on

the interviews with experts and relevant documents,

has been built during the course of the project. The

open-source Java-based environment Protégé 4.1.0.

has been used for the ontology modelling. Logic of

the ontology was verified with the aid of the open-

source Java-based Pellet reasoner in ver. 2.0.0.

Important classes with explanation of their meaning

are mentioned in “Tab. 1”.

In the course of the framework development also

the alternative languages were considered.

Especially, the attention was focused to UML

(Unified Modelling Language). UML is not

primarily oriented to modelling ontologies though;

there might be sound reasons to use UML also for

describing concepts and relations in a knowledge

base. UML is a de facto standard modelling

language in software industry. UML serves as

modelling tool for development of computer

information systems. The UML is designed to be

general enough so that it can serve also other

purposes than it was originally intended to. UML

offers specification together with graphical notation

for describing models.

Both OWL and UML are based on common

principles coming from object oriented thinking and

modelling. However, OWL conforms to the

principles of declarative programming paradigm, the

UML follows the imperative programming

paradigm. The declarative formalism is closer to the

nature of knowledge that was to be captured. The

imperative formalism was closer to what should be

recommended since the result of the

recommendation should be an imperative set of

actions. The major difference with respect to the

goal is that OWL enables to use existing inference

engines. In case of UML the inference engine has to

be implemented separately.

Currently, ontologies have been widely used in

semantic web applications in order to recommend

web pages or multimedia resources (Middleton,

2006); (Lops, 2011).

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

434

Table 1: Important classes of the ontology.

Classes Meaning

Agent This class represents a biochemical agent.

The agent is characterized by its

occurrence, reservoir, infectivity,

transmission, fatality, symptoms,

incubation period and prevention. The

particular agents are modelled as

individuals with specific values of given

characteristics represented as well as

individuals. The following individuals are

represented: Anthrax, Brucellosis,

Cholera, Glanders, Melioidosis, Bubonic

plague and Tularemia.

Environment This class describes the important

characteristics of the scene of the incident.

The environment is characterized by wind

speed, direction, temperature, humidity,

animal occurrence, density of population

in the area, and also number of infected

persons, number of infected animals,

number of dead persons, number of dead

animals, time from first symptom

observed, occurrence in public transport,

etc.

Response

Operation

This class describes particular response

operation mainly with its impact to

protected assets. The following individuals

are represented: Vaccination, Water

reservoir decontamination, Area

quarantine Animal kill off, Water supply,

Food supply, Insect repellent supply,

Protective mask supply, Army power

utilization, Soil reservoir decontamination,

Human quarantine, Animal quarantine,

Vaccine buying, Laboratory analysis of

sample, Air decontamination

Recommendation This class is going to be associated with

individuals of Response operation class.

The individuals associated will be inferred

based on the domain knowledge in form of

the rules.

Incident This class is used to associate Agent,

Environment and Recommendation class.

Individual belonging to this class would

represent a current incident and would be

associated with individuals of Agent class

that caused the incident, individual

describing the current environment setting

and would be linked to particular response

operations inferred as a recommendation

to tackle the incident.

Protected Asset This class represents the protected assets

that are threatened during the incident by

the agent. The protected assets are also

impact by recommended response

operations. Currently, there are three types

of subclasses and that is the tangible

property, intangible property or financial

assets of humans. Particular protected

assets will be represented as individuals

belonging to one of these subclasses.

The ontology describes user preferences and

particular items of interest and based on the

principles of content or collaborative filtering the

similarity was computed. In such cases the ontology

based inferences can be utilized since the description

of an item or user preferences can be enriched by

implicit classification based on the defined

properties and relations. However, there are some

limitations of ontology based reasoning. First, it

regards only classes and thus is not able to handle

individual. Modelling particular instances of certain

events and elements, however, better reflect the

reality. Second, it is not able to reason based on

expressed causality that is an evident part of

knowledge need during response operations. That is

why it was necessary to employ add into the

ontology another level of expressivity using rules.

OWL comes with extension including Horn-like

rules that is called SWRL (Semantic Web Rule

Language). There are six core classes in the paper,

see “Tab. 1”.

These classes with their individuals are basis for

the implementation of SWRL-based rules. These

rules represent knowledge of experts that were

elicited during interviews. SWRL editor of Protégé

4.1.0 tool was used. SWRL-based rules are the

inputs for the inference engine. We use the open-

Pellet reasoner in ver. 2.0.0. It is able to infer new

relations between classes and individuals or between

individuals only (Sirin, 2007).

2.2 Simulation

The subsystem for modelling the incident is linked

to the subsystem responsible for simulation. The

main goal of the simulation is to estimate the impact

of these actions to people and protected assets as the

time develops. The recommended set of actions

together with the description of the environment

represents the input into the simulation subsystem.

The simulation model is based on the domain

knowledge gained from experts and other resources

such as papers, reports, etc. In particular, data from

Committee on Toxicology (1997) and U. S.

Department of the Army (1990) were used for the

compiling of the document with chemical agent

characteristics (NBC, 2011). This document was

used in our ontology development.

Simulation is based on multi-agent technology.

Multi-agent simulation appropriately reflects the

emergency situation during biochemical incident in

which there are many heterogeneous elements

characterized by given properties and with its own

behaviour. Agents represent infected persons

(individuals), dangerous object (virus, bacteria, etc.)

as well as protected assets. Currently the model

simulating the spread of Anthrax in an environment

EXPLOITATION OF ONTOLOGY-BASED RECOMMENDATION SYSTEM WITH MULTI-AGENT SIMULATIONS

435

is prepared in order to test the technology and the

possibility of integration with the subsystem for

modelling the incident. The model reflects the

characteristics of the Anthrax and simulates the

spread in an environment based on the wind speed

and wind direction and the corresponding effect of

the contaminated cloud on population.

The subsystem for simulation is implemented in

the open source environment called NetLogo.

NetLogo enables to study systems that change in

time. The connection to subsystem modelling the

incident is realized using the NetLogo API

(Application Programming Interface).

Using the simulation the person responsible for

commanding the response operations can better

predict the outcomes of performed activities. The

simulations are useful also in situations in which

alternative set of actions is being recommended.

3 CONCLUSIONS

The response operation during emergency situation

caused by biochemical agent requires to process

huge amount of domain knowledge from various

areas. Ontology represents a possibility how to

capture domain knowledge and enable

recommendation inferred based on the rules

imitating expert decision making. Presented

ontology together with rule based reasoning and

simulation is a part of a proposed decision support

framework. The paper points to technical and

implementation aspects of the framework

development.

ACKNOWLEDGEMENTS

This paper was created with the support of the

research proposal Information support of crises

management in health care No. MO0FVZ0000604,

and the GAČR project SMEW, project num.

403/10/1310.

REFERENCES

Borst, W. N., 1997. Construction of Engineering

Ontologies for Knowledge Sharing and Reuse: Ph.D.

Dissertation, University of Twente.

Committee on Toxicology, National Research Council.

1997 Review of Acute Human-Toxicity Estimates for

Selected Chemical Warfare Agents. Washington,

D.C.: Nationa Academi Press.

Čech, P. et al., 2011. Ontological models and expert

systems in decision support of emergency situations in

Military Medical. Science. Letters, vol. 80, 2011, p.

21-27.

Gruber, T., 1993. A translation approach to portable

ontologies. Knowledge Acquisition, vol. 5, issue 2, pp.

199-220, 1993.

Lops, P., M. de Gemmis and G. Semeraro, 2011. Content-

based Recommender Systems: State of the Art and

Trends in Recommender Systems Handbook, Part 1,

73-105, 2011.

Middleton, S. E., D. De Roure and N. R. Shadbolt, 2009.

Ontology-Based Recommender Systems in Handbook

on Ontologies International Handbooks on

Information Systems, 2009, Part 6, 779-796.

NBC Biological and Chemical Agent Characteristics,

NBC Product and Service Handbook [Internet]

http://www.approvedgasmasks.com/BioChemAgentC

haracteristics.pdf [accessed 22.1.2011].

Sirin, E, 2007. Pellet: A Practical OWL-DL Reasoner.

Web Semantics: Science, Services and Agents on the

World Wide Web Vol. 5, Issue 2, June 2007, pp. 51-

53.

U.S., Department of the Army, Potential Military

Chemical/Biological Agnes and Compounds, U.S.

Army Field Manual 3-9, (NAVFAC P-467, AFR 355-

'7), 12 December 1990. Washington, D.C. Government

Printing Office.

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