AN ONTOLOGY CHANGE MANAGEMENT SYSTEM

An Experiment on a Health Care Case Study

Soumaya Slimani, Karim Baïna, Salah Baïna

ENSIAS, Mohammed V-Souissi University, Rabat, Morocco

Martin Henkel, Erik Perjons

Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

Keywords: Ontology Evolution, Semantic Service, Multi-agent System, Eye Specialist Ontology, Primary Health Care

Ontology.

Abstract: Numerous ontologies have been developed for life science domains. These ontologies are continuously

changing. Thus, it is becoming profitable to study and to manage these ontologies change in order to keep

all dependent ontologies and their related mappings consistent. The aim of this paper is to propose an agent

based approach enabling not only ontology and ontology mapping evolution analysis but also to manage

their changes. An experiment in health care illustrates the benefits of our approach. We apply our algorithm,

and implementation prototype p

OEManager to eye specialist ontology (ESO) and primary health care

ontology (PCO), and particularly, we use our ontology agent model, and prototype to manage some

significant changes in the ESO ontology.

1 INTRODUCTION

Ontologies become increasingly important in life

sciences. In electronic health care, the greater

problem is the heterogeneity of information systems.

Semantic interoperability of these heterogeneous

systems can be achieved through an agreement

between the underlying ontologies (e.g. RDF, OWL,

etc.). In the context of web services, several

standards were developed to describe web services

semantics (e.g. WSDL-S, WSMO, OWL-S, etc.).

Due to the rapid development of life science

research, ontologies evolve continuously, i.e., they

are frequently changing to incorporate new domain

knowledge into them. However, these changes, may

impact the correctness of future communication

using these ontologies because other services are not

aware of these changes. Hence, ontology mappings

(which allow services to interpret correctly

(translate) exchanged data) should be corrected.

Research in ontology evolution and change

management deal particularly with the versioning

and evolution of the same ontology, and do not

process the evolution of different ontologies,

interrelated by mappings and describing different

services. In this paper we propose an agent-based

algorithm and prototype managing the ontologies

evolution life cycle, as well as the evolution of

ontology-related mappings. Also, in a

comprehensive evaluation, we apply our algorithm

to eye specialist ontology (ESO) and primary health

care ontology (PCO), and, particularly, we use our

ontology agent model and p2OEManager prototype

to manage some significant changes in the ESO

ontology.

2 P

OEMANAGER DESIGN

AND IMPLEMENTATION

OVERVIEW

Fig. 1. shows the general architecture of

p2OEManager (which stands for peer to peer

Ontology Event Manager) upon 3 main components:

Ontology Manager, Ontology Mapping Manager,

and Ontology Agent Manager built on an open

Service Layer. Interactions between instances

p2OEManager peers (i.e. ontology change event

449

Slimani S., Baïna K., Baïna S., Henkel M. and Perjons E..

AN ONTOLOGY CHANGE MANAGEMENT SYSTEM - An Experiment on a Health Care Case Study.

DOI: 10.5220/0003105504490452

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2010), pages 449-452

ISBN: 978-989-8425-29-4

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

messages) are handled through an ontology change

communication channel. In fact, p2OEManager is

neutral regarding the service infrastructures.

Business messages between these services are

supported outside the scope of p2OEManager within

service marshalling/unmarshalling and security,

addressing, reliable messaging, routing, and

transport standard protocols.

Figure 1: p2OEManager Architecture.

As shown in Fig.1. , Service layer, which is outside

p2OEManager, represents services that are

described by service ontologies. These service

ontologies are defined by human designers using

ontology editors, and these service ontologies are

then monitored synchronously by p2OEManager.

Service dependencies are represented by service

ontology mappings defined by human designers too

using ontology mapping generators. Services

communicate and collaborate with each other within

service infrastructure based on ontology mappings

evolving in p2OEManager.

Our proposal is based on a combination of

ontologies and agents. We associate an agent to each

ontology. This agent is responsible of change

management and propagation of these changes to

other dependent ontologies. Table 1 summarizes the

four cases and the agent actions for each case.

For more formalisation details of our ontology agent

model, and algorithms implemented within our

OEManager Architecture, please refer to our

paper (Slimani and al., 2010).

Table 1: Dependent agent actions.

Syntactic Search

True False

Semantic

True

-Updates the

mapping

-Updates the

mapping.

-Annotates the

corresponding

object by the added

object.

False

-Reformulates

the change

definition.

-Negotiates the

change

definition.

-Adds the new

object to the

ontology.

-Updates the

related mapping.

3 EVALUATION SCENARIO:

THE S:TERIKS HEALTH CARE

In order to illustrate the approach presented in this

paper a health-care case from the REMS project.

The main objective of the REMS project was to

develop a set of e-services that could be used to

create, manage and transfer health care referrals

between S:t Erik’s eye hospital specialist clinic and

primary care units. Having a set of e-services

available from different health care providers would

enable healthcare systems to be interconnected in

order to share information. To achieve this

integration, it is crucial for that the services share the

same set of concepts. Fig. 2 shows the value model

(Henkel and al., 2007) which depicts the main flow

of resources between the patient, the primary health

care and the eye specialist clinic. There are

important aspects that need to be considered when

designing e-services for the information exchange.

In the case of Swedish health services these should

follow standards on the international and national

levels. However, even given these standards it is still

plenty of room for interpretation of the concepts.

Furthermore, there is also a need to specialize the

concepts/models in order to cover specific details of

the Eye health care. Thus it is likely that two

organizations that follow the standardized

concepts/models will end up with two different

models that need to be kept synchronized if they are

to be able to exchange information.

Primary health care

unit

Patient

Eye specialist clinic

Initial opinion /

diagnosis

Ref erral to specialist

Patient fee

Information on

symtoms

Ref erral answer with information on

symptoms and diagnoses

Information on specialist

competencies

Performed

eye-treatment

Diagnosis

Information on ongoing

treatment

Referral,

describing the

health problems

Patient fee

Information on

symtoms

Figure 2: Actors and resource exchanges in the S:tEriks

health care case.

For this example, we focus on the interconnection of

the systems at the primary care units (PCU) and the

systems at the eye specialist clinics (ESC). Fig. 3

shows the basic ontology that is used on the PCU

systems. Fig. 4 shows the ontology that is used at the

ESC. These ontologies will be the basis for the

services that need to interconnect when exchanging

referral information. We have developed the

KEOD 2010 - International Conference on Knowledge Engineering and Ontology Development

450

Primary health care ontology (PCO) and the Eye

specialist ontology (ESO), using Protégé 3.4

(http://protege.stanford.edu).

Figure 3: Primary health care ontology (PCO).

Figure 4: Eye specialist ontology (ESO).

Common to both ontologies are the concepts of

PATIENT, ROLE, HEALTH PROBLEMS,

SYMPTOMS and REFERAL. We have generated a

mapping using Prompt which generate an OWL file

for the mapping

(http://protege.stanford.edu/plugins/prompt/prompt.h

tml). For the sake of describing our approach we

provide in the next section four examples of changes

4 ALGORITHM APPLICATION:

PROCESS OF CHANGES

As input of the process of using the Ontology Agent

Model, we have the Primary health care ontology

(PCO), the Eye specialist ontology (ESO), and the

mapping between PCO and ESO (MEP). The

process of using the Ontology Agent Model can be

presented in 3 steps: (1) We have paramerezed the

Ontology Agent Model by integreting PCO, ESO,

and MEP URIs. We have obtained two Ontology

Agent: one for the Primary health care ontology

(PCO) and another for the Eye specialist ontology

(ESO), (2) run the PCO agent and the ESO agent,

(3) Process changes.

The application of the algorithm in this scenario

begins on the Initiator side (ESO agent). First,

changes are listed as follow:

changeSet = <

C1=<ADD :Class :EYE_REFERAL:

subclassOf REFERAL>,

C2=<ADD:Class:HEALTH_CARE_ACTIVITY:sub

classOf ACT>

C3=<ADD:Class:PARTICIPATION:subclassOf

Thing>

C4=<ADD:Class:ORGANIZATIONAL_UNIT

:subclassOf Thing>

C5=<ADD:ObjectProperty:ORG_CG:<Domain:

ORANIZATIONAL_UNIT, Rang:CARE_GIVERS>>

Then changes are classified according to their

relationship with the mapping. All changes are sent

to PCO agent which process for each change the

correspondents actions as follow:

For C1. The syntactic and semantic search return

false. So, PCO update the mapping by adding the

following correspondences: mapped (

ESO. C

, PCO.C

)

For C2. The syntactic search return false, but

semantic search will return true, because

HEALTH_CARE_ACTIVITY can be mapped to the

existing concept of HEALTH_CARE_EVENTS in

the PCO. Thus the PCO agent (1) Annotate the

concept HEALTH_CARE_EVENTS concept by

adding Rdfs:seeAlso HEALTH_CARE_ACTIVITY

annotation, (2) Update mapping by adding the

following correspondence :

mapped(

HEALTH_CARE_EVENTS,HEALTH_CARE_ACTI

VITY

)

For C3. The syntactic and the semantic search

return true. So, the PCO agent updates the mapping

by adding the following correspondence:

mapped(

ESO.PARTICIPATION, PCO.PARTICIPATION)

For C4 and C5. The concept

ORGANIZATIONAL_UNIT is added with an

association to CARE_GIVERS in the ESO.

ORGANIZATIONAL_UNIT matches syntactically

to ORGANIZATIONAL_UNIT in the PCO, but this

match is semantically incorrect since only

PUBLIC_O can have ORGANIZATIONAL_UNIT

in the PCO. So, the PCO agent calculates adefinition

for ORGANIZATIONAL_UNIT as follow:

<<Class:PCO. ORGANIZATIONAL_UNIT:

subclass of Thing>

<ObjectProperty: PCO.OU_HAS :

Domain: PCO.RGANIZATIONAL_UNIT,

Rang :PCO.PUBLIC_O >>

AN ONTOLOGY CHANGE MANAGEMENT SYSTEM - An Experiment on a Health Care Case Study

451

PCO Agent sends this definition to ESO agent,

which calculates a new definition:

<<Class:PCO.ORGANIZATIONAL_UNIT:

subclass of Thing>

<ObjectProperty:PCO.OU_HAS:

Domain: PCO.RGANIZATIONAL_UNIT,

Rang:PCO.PUBLIC_O>

<Class:ESO.ORGANIZATIONAL_UNIT:

subclass of Thing>

<ObjectProperty: ESO.OU_HAS :

Domain:ESO.ORGANIZATIONAL_UNIT,

Rang:ESO.CARE_GIVERS>>

The following correspondence introduces a conflict

in the definition of CARE_GIVERS:

mapped (ESO.CARE_GIVERS, PCO.PUBLIC_O)

Indeed, as we have already in the mapping that

ESO.PUBLIC_O corresponds to PCO.PUBLIC_O

and ESO.PUBLIC_O is a subclass of

CARE_GIVERS, this can be a source of errors. So,

the agent sends an alert message to the user. User

can modify the mapping and the ontology or validate

any of the definitions contained in the negotiation

exchange. In this example, our algorithm allows

services in the Eye Specialist Clinic and in the

Primary Care Provider to be aware of evolution in

other services. Agents take the necessary decisions

to maintain a reliable exchange of data between

these entities.

5 RELATED WORKS

The ontology evolution and change management has

been addressed by many researches. (Klein and al.,

2001). (Klein, 2004), investigated the versioning of

ontologies, (Plessers and al., 2007) define Change

Definition Language (CDL), (Djedidi and al., 2009)

a patterns-based ontology evolution approach ,

(Zablith, 2009) a Framework for ontology evolution

and (Hartung and al., 2008). But we propose an

evolution model for multi-ontology system when

ontologies are different and not only for instance.

When ontologies are considered as an ontology

instances, the source ontology has the semantics of

the dependent ontologies (because they are instances

of the source ontology). So, ontology evolution will

require applying the same changes on the dependent

ontologies. Note also that in the case of different

ontologies, mapping between ontologies must be

managed in parallel.

6 CONCLUSIONS AND FUTURE

WORK

Interconnecting services is a complex task. A part of

the complexity comes from the need to have a

common, shared view of the information. The

approach proposed in this paper is suitable for

ontology evolution management in distributed and

heterogeneous environments. The aim is to provide a

flexible way to partially automate the process of

ontology evolution management. The approach

consists of software agents that represent each of the

services involved, and their respective ontologies

and related mappings. To illustrate our approach

we applied it to a health care case. The case study

highlighted some of the main benefits of the

approach. The approach could be extended and

improved in several ways. First, there is a need to

further analyze the implications that changes have to

the logic of the running software services.

Furthermore, there is a need to extend the ontology

agent model to include different types of changes in

our algorithms (removal, etc...).

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Slimani,S.,Baïna,S.,Baïna,K., “Agent-based Architecture

for Service Ontology evolution Management”,

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Henkel, M., Johannesson, P., Perjons, E., Zdravkovi, J.,

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IEEE International Conference on e-Business

Engineering (ICEBE'07), Hong Kong, China, October

24-26.

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Semantic Web. In: Proc. Int. Semantic Web Working

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Plessers, P., Troyer, O., and Casteleyn, S., 2007.

Understanding ontology evolution : A change

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49.

Djedidi, R., Aufaure, MA., 2010. ONTO-EVOAL an

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